This shows an example of integrated workflow between xgxr nlmixr and ggPmx

library(nlmixr)
library(xgxr)
library(readr)
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggPMX)

Load the data

pkpd_data <- case1_pkpd %>%
  arrange(DOSE) %>%
  select(-IPRED) %>%
  mutate(TRTACT_low2high = factor(TRTACT, levels = unique(TRTACT)),
         TRTACT_high2low = factor(TRTACT, levels = rev(unique(TRTACT))),
         DAY_label = paste("Day", PROFDAY),
         DAY_label = ifelse(DAY_label == "Day 0","Baseline",DAY_label))
 
pk_data <- pkpd_data %>%
  filter(CMT == 2)

pk_data_cycle1 <- pk_data %>%
    filter(CYCLE == 1)

Exploratory analysis using ggplot and xgx helper functions

Use xgxr for simplified concentration over time, colored by Dose, mean +/- 95% CI

Often in exploring data it is worthwhile to plot by dose by each nominal time and add the 95% confidence interval. This typical plot can be cumbersome and lack some nice features that xgxr can help with. Note the following helper functions:

  • xgx_theme_set() this sets the theme to black and white color theme and other best pratices in xgxr.

  • xgx_geom_ci() which creates the Confidence Interval and mean plots in a simple interface.

  • xgx_scale_y_log10() which creates a log-scale that includes the minor grids that immediately show the viewer that the plot is a semi-log plot without carefully examining the y axis.

  • xgx_scale_x_time_units() which creates an appropriate scale based on your times observed and the units you use. It also allows you to convert units easily for the right display.

  • xgx_annote_status() which adds a DRAFT annotation which is often considered best practice when the data or plots are draft.

xgx_theme_set() # This uses black and white theme based on xgxr best
                # pratices

# flag for labeling figures as draft
status <- "DRAFT"

time_units_dataset <- "hours"
time_units_plot    <- "days"
trtact_label       <- "Dose"
dose_label         <- "Dose (mg)"
conc_label         <- "Concentration (ng/ml)" 
auc_label          <- "AUCtau (h.(ng/ml))"
concnorm_label     <- "Normalized Concentration (ng/ml)/mg"
sex_label          <- "Sex"
w100_label         <- "WEIGHTB>100"
pd_label           <- "FEV1 (mL)"
cens_label         <- "Censored"


ggplot(data = pk_data_cycle1, aes(x     = NOMTIME,
                                  y     = LIDV,
                                  group = DOSE,
                                  color = TRTACT_high2low)) +
    xgx_geom_ci(conf_level = 0.95) + # Easy CI with xgxr
    xgx_scale_y_log10() + # semi-log plots with semi-log grid minor lines
    xgx_scale_x_time_units(units_dataset = time_units_dataset,
                           units_plot = time_units_plot) +
    # The last line creates an appropriate x scale based on time-units
    # and time unit scale
    labs(y = conc_label, color = trtact_label) +
    xgx_annotate_status(status) #  Adds draft status to plot

With this plot you see the mean concentrations confidence intervals stratified by dose

Concentration over time, faceted by Dose, mean +/- 95% CI, overlaid on gray spaghetti plots

Not only is it useful to look at the mean concentrations, it is often useful to look at the mean concentrations and their relationship between actual individual profiles. Using ggplot coupled with the xgxr helper functions used above, we can easily create these plots as well:

ggplot(data = pk_data_cycle1, aes(x = TIME, y = LIDV)) +
  geom_line(aes(group = ID), color = "grey50", size = 1, alpha = 0.3) +
  geom_point(aes(color = factor(CENS), shape = factor(CENS))) + 
  scale_shape_manual(values = c(1, 8)) +
  scale_color_manual(values = c("grey50", "red")) +
  xgx_geom_ci(aes(x = NOMTIME, color = NULL, group = NULL, shape = NULL), conf_level = 0.95) +
  xgx_scale_y_log10() +
  xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +
  labs(y = conc_label, color = trtact_label) +
  theme(legend.position = "none") +
  facet_grid(.~TRTACT_low2high) +
  xgx_annotate_status(status)

To me it appears the variability seems to be higher with higher doses and higher with later times.

Exploring the dose linearity

A common way to explore the dose linearity is to normalize by the dose. If the confidence intervals overlap, often this is a dose linear example.

ggplot(data = pk_data_cycle1,
       aes(x = NOMTIME,
           y = LIDV / as.numeric(as.character(DOSE)),
           group = DOSE,
           color = TRTACT_high2low)) +
  xgx_geom_ci(conf_level = 0.95, alpha = 0.5, position = position_dodge(1)) +
  xgx_scale_y_log10() +
  xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +
  labs(y = concnorm_label, color = trtact_label) +
    xgx_annotate_status(status)

This example seems to be dose-linear, with the exception of the censored data. This can be made even more clear by removing the censored data for this plot:

ggplot(data = pk_data_cycle1 %>% filter(CENS == 0),
       aes(x = NOMTIME,
           y = LIDV / as.numeric(as.character(DOSE)),
           group = DOSE,
           color = TRTACT_high2low)) +
  xgx_geom_ci(conf_level = 0.95, alpha = 0.5, position = position_dodge(1)) +
  xgx_scale_y_log10() +
  xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +
  labs(y = concnorm_label, color = trtact_label) +
    xgx_annotate_status(status)

The lowest dose, with the most censoring, is the one that seems to be the outlier. That is likely an artifact of censoring.

Other ways to explore the data include by looking at normalized Cmax and AUC values (which we will skip in this vignette).

Exploring Covariates in the dataset

Using the xgx helper functions to ggplot you can explore the effect of high baseline weight. This particular plot is shown below:

ggplot(data = pk_data_cycle1, aes(x = NOMTIME,
                                  y = LIDV,
                                  group = WEIGHTB > 100,
                                  color = WEIGHTB > 100)) + 
    xgx_geom_ci(conf_level = 0.95) +
    xgx_scale_y_log10() +
    xgx_scale_x_time_units(units_dataset = time_units_dataset, units_plot = time_units_plot) +
    facet_grid(.~DOSE) +
    labs(y = conc_label, color = w100_label) +
    xgx_annotate_status(status)

It seems that the weight effect is not extreme for either dose group

Summary of exploratory analysis findings

From the exploratory analysis we see: - The doses seem proportional - The PK seems to have a 2-compartment model - Censoring has a large effect on the PK data.

Fitting the data with nlmixr

First we need to subset to the PK only data and rename LIDV to DV

dat <- case1_pkpd %>%
    rename(DV=LIDV) %>%
    filter(CMT %in% 1:2) %>%
    # Filter (for now) since CENS supprot in nlmixr is in development
    filter(CENS == 0) %>%
    filter(TRTACT != "Placebo")

Next create a 2 compartment model:


## Use 2 compartment model
cmt2 <- function(){
    ini({
        lka <- log(0.1) # log Ka
        lv <- log(10) # Log Vc
        lcl <- log(4) # Log Cl
        lq <- log(10) # log Q
        lvp <- log(20) # Log Vp

        eta.ka ~ 0.01
        eta.v ~ 0.1
        eta.cl ~ 0.1
        logn.sd = 10
    })
    model({
        ka <- exp(lka + eta.ka)
        cl <- exp(lcl + eta.cl)
        v <- exp(lv + eta.v)
        q <- exp(lq)
        vp <- exp(lvp)
        linCmt() ~ lnorm(logn.sd)
    })
}

## Check parsing
cmt2m <- nlmixr(cmt2)
print(cmt2m)
#> ▂▂ RxODE-based 1-compartment model with first-order absorption ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 
#> ── Initialization: ───────────────────────────────────────────────────────────── 
#> Fixed Effects ($theta): 
#>       lka        lv       lcl        lq       lvp 
#> -2.302585  2.302585  1.386294  2.302585  2.995732 
#> 
#> Omega ($omega): 
#>        eta.ka eta.v eta.cl
#> eta.ka   0.01   0.0    0.0
#> eta.v    0.00   0.1    0.0
#> eta.cl   0.00   0.0    0.1
#> ── μ-referencing ($muRefTable): ──────────────────────────────────────────────── 
#> ┌─────────┬─────────┐
#> │ theta   │ eta     │
#> ├─────────┼─────────┤
#> │ lka     │ eta.ka  │
#> ├─────────┼─────────┤
#> │ lcl     │ eta.cl  │
#> ├─────────┼─────────┤
#> │ lv      │ eta.v   │
#> └─────────┴─────────┘
#> ── Model: ────────────────────────────────────────────────────────────────────── 
#>         ka <- exp(lka + eta.ka)
#>         cl <- exp(lcl + eta.cl)
#>         v <- exp(lv + eta.v)
#>         q <- exp(lq)
#>         vp <- exp(lvp)
#>         linCmt() ~ lnorm(logn.sd) 
#> ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
## First try log-normal (since the variabilitiy seemed proportional to concentration)
cmt2fit.logn <- nlmixr(cmt2m, dat, "saem",
                       control=list(print=0), 
                       table=tableControl(npde=TRUE,cwres=TRUE))
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:20:25


## Now try proportional
cmt2fit.prop <- cmt2fit.logn %>%
    update(linCmt() ~ prop(prop.sd)) %>%
    nlmixr(est="saem", control=list(control=0),
           table=tableControl(npde=TRUE, cwres=TRUE))
#> 1:   -1.1874   2.7659   2.4635   2.3812   5.2937   0.2470   0.2520   0.0443   0.6514
#> 2:   -1.1250   2.6752   2.4474   2.3340   5.3406   0.2355   0.2394   0.0428   0.4766
#> 3:   -1.0575   2.6114   2.4358   2.3005   5.2976   0.2237   0.2274   0.0440   0.3873
#> 4:   -1.0251   2.5456   2.4150   2.3164   5.3001   0.2206   0.2171   0.0424   0.3372
#> 5:   -1.0131   2.5231   2.4137   2.3211   5.2530   0.2096   0.2162   0.0472   0.3121
#> 6:   -0.9910   2.5260   2.4086   2.3245   5.2601   0.2058   0.2054   0.0480   0.3026
#> 7:   -0.9873   2.5135   2.4149   2.3257   5.2056   0.2033   0.2082   0.0506   0.2920
#> 8:   -0.9692   2.5324   2.4162   2.3031   5.1897   0.1936   0.2175   0.0491   0.2883
#> 9:   -0.9823   2.5093   2.4199   2.3073   5.1847   0.1852   0.2104   0.0497   0.2853
#> 10:   -0.9885   2.5056   2.4193   2.3011   5.1805   0.1874   0.2087   0.0476   0.2821
#> 11:   -0.9898   2.5034   2.4281   2.3085   5.1688   0.1786   0.2189   0.0453   0.2831
#> 12:   -0.9979   2.4964   2.4153   2.3065   5.1551   0.1734   0.2313   0.0433   0.2798
#> 13:   -1.0039   2.4974   2.4064   2.2959   5.1737   0.1749   0.2265   0.0413   0.2797
#> 14:   -0.9935   2.5135   2.4031   2.2874   5.1642   0.1662   0.2238   0.0393   0.2784
#> 15:   -0.9812   2.5183   2.4116   2.3052   5.1634   0.1792   0.2233   0.0373   0.2793
#> 16:   -0.9821   2.5139   2.4143   2.3227   5.1453   0.1741   0.2260   0.0368   0.2792
#> 17:   -0.9865   2.5245   2.4095   2.3108   5.1420   0.1785   0.2201   0.0356   0.2808
#> 18:   -0.9826   2.5193   2.4174   2.3026   5.1547   0.1863   0.2193   0.0355   0.2809
#> 19:   -0.9847   2.5122   2.4261   2.3152   5.1523   0.1942   0.2230   0.0346   0.2793
#> 20:   -0.9801   2.5058   2.4371   2.3047   5.1412   0.1931   0.2354   0.0346   0.2785
#> 21:   -0.9853   2.5007   2.4365   2.3059   5.1544   0.1942   0.2336   0.0328   0.2799
#> 22:   -0.9801   2.4809   2.4268   2.3051   5.1431   0.1845   0.2413   0.0333   0.2783
#> 23:   -0.9852   2.5017   2.4291   2.3062   5.1264   0.1834   0.2457   0.0317   0.2784
#> 24:   -0.9977   2.4997   2.4421   2.2966   5.1333   0.1742   0.2334   0.0320   0.2806
#> 25:   -0.9973   2.5115   2.4547   2.3134   5.1352   0.1770   0.2388   0.0349   0.2810
#> 26:   -0.9907   2.5087   2.4507   2.3085   5.1340   0.1721   0.2395   0.0331   0.2792
#> 27:   -0.9949   2.5093   2.4394   2.3004   5.1363   0.1786   0.2415   0.0322   0.2773
#> 28:   -0.9774   2.5054   2.4489   2.3018   5.1257   0.1799   0.2508   0.0324   0.2788
#> 29:   -0.9693   2.5137   2.4455   2.3159   5.1167   0.1747   0.2475   0.0330   0.2790
#> 30:   -0.9813   2.5169   2.4379   2.3180   5.1093   0.1822   0.2413   0.0347   0.2789
#> 31:   -0.9903   2.5004   2.4373   2.3360   5.1017   0.1863   0.2434   0.0348   0.2796
#> 32:   -0.9776   2.5092   2.4285   2.3355   5.1149   0.1861   0.2428   0.0346   0.2791
#> 33:   -0.9756   2.5004   2.4309   2.3231   5.1058   0.1827   0.2307   0.0371   0.2790
#> 34:   -0.9890   2.5005   2.4324   2.3115   5.1048   0.1897   0.2325   0.0353   0.2789
#> 35:   -0.9829   2.4988   2.4357   2.3120   5.1099   0.1980   0.2366   0.0342   0.2801
#> 36:   -0.9934   2.5029   2.4353   2.3231   5.1041   0.1907   0.2361   0.0325   0.2796
#> 37:   -0.9761   2.5163   2.4254   2.3052   5.1017   0.1873   0.2363   0.0329   0.2786
#> 38:   -0.9801   2.5064   2.4248   2.3218   5.0984   0.1861   0.2482   0.0317   0.2795
#> 39:   -0.9887   2.5181   2.4253   2.3212   5.1010   0.1882   0.2358   0.0320   0.2830
#> 40:   -0.9878   2.5308   2.4357   2.3025   5.1019   0.1864   0.2315   0.0331   0.2828
#> 41:   -0.9852   2.5172   2.4382   2.3173   5.0975   0.1837   0.2479   0.0377   0.2808
#> 42:   -0.9864   2.5190   2.4333   2.3191   5.0994   0.1880   0.2441   0.0387   0.2799
#> 43:   -0.9764   2.5178   2.4341   2.3141   5.1023   0.1941   0.2501   0.0398   0.2799
#> 44:   -0.9727   2.5057   2.4473   2.3247   5.0984   0.1878   0.2522   0.0378   0.2782
#> 45:   -0.9673   2.5188   2.4608   2.3310   5.0917   0.1931   0.2396   0.0360   0.2811
#> 46:   -0.9575   2.5158   2.4633   2.3287   5.0973   0.2044   0.2456   0.0345   0.2792
#> 47:   -0.9610   2.5086   2.4648   2.3105   5.0935   0.1941   0.2542   0.0368   0.2788
#> 48:   -0.9765   2.5136   2.4589   2.3143   5.0899   0.1906   0.2415   0.0350   0.2786
#> 49:   -0.9742   2.5132   2.4487   2.3143   5.0963   0.1813   0.2365   0.0332   0.2813
#> 50:   -0.9755   2.5109   2.4512   2.3178   5.0981   0.1911   0.2492   0.0316   0.2810
#> 51:   -0.9774   2.5102   2.4465   2.3079   5.0934   0.1930   0.2519   0.0310   0.2791
#> 52:   -0.9774   2.5049   2.4423   2.3167   5.0956   0.1959   0.2473   0.0294   0.2789
#> 53:   -0.9766   2.5046   2.4507   2.3202   5.0907   0.1960   0.2349   0.0290   0.2794
#> 54:   -0.9906   2.5040   2.4484   2.3128   5.0877   0.1913   0.2247   0.0307   0.2795
#> 55:   -0.9960   2.5040   2.4454   2.3232   5.0870   0.1860   0.2274   0.0308   0.2789
#> 56:   -0.9909   2.5038   2.4528   2.3286   5.0860   0.1878   0.2321   0.0321   0.2828
#> 57:   -0.9927   2.5065   2.4557   2.3118   5.0918   0.1958   0.2247   0.0305   0.2816
#> 58:   -0.9874   2.5038   2.4615   2.3096   5.0910   0.1908   0.2437   0.0357   0.2818
#> 59:   -0.9909   2.5033   2.4703   2.3083   5.0923   0.1903   0.2471   0.0369   0.2827
#> 60:   -0.9787   2.5185   2.4680   2.3124   5.0869   0.1891   0.2431   0.0390   0.2823
#> 61:   -0.9747   2.5067   2.4728   2.2989   5.0856   0.1879   0.2491   0.0370   0.2809
#> 62:   -0.9777   2.5060   2.4643   2.3170   5.0824   0.2009   0.2448   0.0381   0.2821
#> 63:   -0.9753   2.5134   2.4584   2.3173   5.0827   0.1909   0.2385   0.0365   0.2821
#> 64:   -0.9622   2.5218   2.4559   2.3143   5.0888   0.1940   0.2392   0.0357   0.2797
#> 65:   -0.9637   2.5117   2.4624   2.3109   5.0828   0.2002   0.2347   0.0339   0.2794
#> 66:   -0.9800   2.5114   2.4611   2.3137   5.0812   0.1908   0.2377   0.0326   0.2800
#> 67:   -0.9682   2.5157   2.4681   2.3176   5.0871   0.1980   0.2372   0.0333   0.2790
#> 68:   -0.9779   2.5071   2.4645   2.3120   5.0871   0.2007   0.2312   0.0327   0.2805
#> 69:   -0.9669   2.5019   2.4582   2.3287   5.0885   0.1971   0.2392   0.0311   0.2797
#> 70:   -0.9763   2.5132   2.4541   2.3228   5.0971   0.1913   0.2298   0.0299   0.2809
#> 71:   -0.9823   2.5146   2.4449   2.3187   5.0926   0.1911   0.2280   0.0292   0.2807
#> 72:   -0.9852   2.5145   2.4402   2.3097   5.0938   0.1886   0.2286   0.0297   0.2804
#> 73:   -0.9961   2.5078   2.4282   2.2999   5.1001   0.1902   0.2232   0.0282   0.2811
#> 74:   -1.0034   2.4953   2.4354   2.3021   5.1058   0.1900   0.2251   0.0268   0.2817
#> 75:   -0.9961   2.5066   2.4443   2.2865   5.1128   0.1844   0.2271   0.0263   0.2800
#> 76:   -0.9929   2.5144   2.4408   2.3036   5.1160   0.2037   0.2253   0.0285   0.2804
#> 77:   -1.0007   2.5107   2.4357   2.2986   5.1173   0.1984   0.2338   0.0271   0.2806
#> 78:   -0.9885   2.5096   2.4320   2.3124   5.1233   0.2066   0.2441   0.0257   0.2838
#> 79:   -0.9948   2.4884   2.4388   2.3073   5.1316   0.1963   0.2574   0.0264   0.2821
#> 80:   -0.9947   2.4994   2.4357   2.2789   5.1275   0.1931   0.2445   0.0278   0.2803
#> 81:   -0.9855   2.5165   2.4429   2.2832   5.1237   0.2050   0.2471   0.0269   0.2812
#> 82:   -0.9819   2.5215   2.4540   2.2782   5.1205   0.2077   0.2348   0.0257   0.2822
#> 83:   -0.9708   2.5148   2.4498   2.2838   5.1158   0.2022   0.2439   0.0261   0.2780
#> 84:   -0.9671   2.5107   2.4585   2.2956   5.1183   0.1926   0.2519   0.0248   0.2784
#> 85:   -0.9789   2.5113   2.4614   2.2840   5.1193   0.1916   0.2502   0.0246   0.2813
#> 86:   -0.9921   2.5039   2.4586   2.2907   5.1196   0.1995   0.2377   0.0234   0.2816
#> 87:   -0.9923   2.4941   2.4550   2.2916   5.1179   0.2068   0.2378   0.0234   0.2804
#> 88:   -0.9886   2.5187   2.4515   2.2882   5.1180   0.2087   0.2421   0.0252   0.2805
#> 89:   -0.9780   2.5246   2.4413   2.2978   5.1193   0.2049   0.2356   0.0274   0.2806
#> 90:   -0.9837   2.5233   2.4308   2.2906   5.1183   0.2039   0.2438   0.0272   0.2821
#> 91:   -0.9929   2.5156   2.4311   2.2841   5.1206   0.2211   0.2323   0.0292   0.2799
#> 92:   -0.9923   2.5110   2.4280   2.2923   5.1218   0.2111   0.2240   0.0315   0.2798
#> 93:   -0.9944   2.5175   2.4246   2.2874   5.1201   0.2065   0.2242   0.0316   0.2812
#> 94:   -0.9969   2.5120   2.4301   2.2887   5.1161   0.2084   0.2303   0.0317   0.2796
#> 95:   -1.0024   2.5186   2.4353   2.2714   5.1198   0.2058   0.2321   0.0316   0.2812
#> 96:   -1.0015   2.5176   2.4277   2.2812   5.1250   0.1981   0.2240   0.0313   0.2810
#> 97:   -0.9972   2.5083   2.4284   2.2832   5.1231   0.1904   0.2481   0.0342   0.2788
#> 98:   -0.9930   2.5153   2.4265   2.2849   5.1233   0.1929   0.2448   0.0342   0.2810
#> 99:   -1.0076   2.5142   2.4279   2.2965   5.1247   0.1908   0.2404   0.0325   0.2827
#> 100:   -1.0106   2.5149   2.4281   2.2928   5.1208   0.1927   0.2340   0.0318   0.2827
#> 101:   -1.0017   2.5187   2.4204   2.3058   5.1218   0.1949   0.2360   0.0327   0.2814
#> 102:   -1.0090   2.5063   2.4252   2.3009   5.1167   0.1896   0.2503   0.0333   0.2815
#> 103:   -1.0124   2.5122   2.4191   2.3067   5.1183   0.1973   0.2552   0.0318   0.2815
#> 104:   -1.0002   2.5102   2.4214   2.3179   5.1128   0.1974   0.2428   0.0314   0.2799
#> 105:   -1.0183   2.4916   2.4137   2.3122   5.1151   0.2115   0.2358   0.0354   0.2790
#> 106:   -1.0053   2.5136   2.4128   2.3085   5.1181   0.2076   0.2340   0.0362   0.2804
#> 107:   -1.0055   2.4876   2.4240   2.3059   5.1148   0.1972   0.2306   0.0352   0.2800
#> 108:   -1.0189   2.4904   2.4176   2.3054   5.1149   0.1882   0.2318   0.0356   0.2799
#> 109:   -1.0178   2.4911   2.4191   2.3087   5.1151   0.1893   0.2414   0.0355   0.2825
#> 110:   -0.9987   2.5025   2.4210   2.3267   5.1165   0.1803   0.2597   0.0353   0.2824
#> 111:   -0.9779   2.5338   2.4135   2.3247   5.1158   0.1846   0.2467   0.0372   0.2802
#> 112:   -0.9806   2.5252   2.4226   2.3146   5.1178   0.1850   0.2344   0.0375   0.2799
#> 113:   -0.9922   2.5021   2.4378   2.3089   5.1198   0.2012   0.2418   0.0356   0.2795
#> 114:   -1.0032   2.5103   2.4233   2.2992   5.1216   0.2009   0.2297   0.0342   0.2802
#> 115:   -1.0140   2.5048   2.4284   2.3086   5.1245   0.1969   0.2351   0.0389   0.2826
#> 116:   -0.9958   2.5182   2.4246   2.3099   5.1249   0.2063   0.2389   0.0396   0.2834
#> 117:   -1.0053   2.5069   2.4325   2.3103   5.1200   0.1960   0.2408   0.0399   0.2810
#> 118:   -0.9795   2.5107   2.4422   2.3175   5.1191   0.2008   0.2535   0.0391   0.2808
#> 119:   -0.9863   2.4976   2.4418   2.3171   5.1194   0.1948   0.2817   0.0376   0.2811
#> 120:   -0.9758   2.5110   2.4442   2.3224   5.1236   0.2057   0.2676   0.0357   0.2798
#> 121:   -0.9971   2.4941   2.4345   2.3120   5.1163   0.1987   0.2709   0.0339   0.2799
#> 122:   -0.9865   2.4992   2.4407   2.3064   5.1161   0.1971   0.2664   0.0327   0.2782
#> 123:   -0.9927   2.4872   2.4500   2.3147   5.1122   0.2023   0.2674   0.0342   0.2797
#> 124:   -0.9806   2.5159   2.4543   2.3166   5.1102   0.1957   0.2540   0.0325   0.2802
#> 125:   -0.9688   2.5339   2.4630   2.3160   5.1123   0.2037   0.2428   0.0309   0.2832
#> 126:   -0.9712   2.5315   2.4691   2.3152   5.1119   0.1952   0.2459   0.0306   0.2832
#> 127:   -0.9749   2.5191   2.4700   2.3088   5.1127   0.2030   0.2444   0.0300   0.2830
#> 128:   -0.9765   2.5145   2.4664   2.3072   5.1124   0.2041   0.2503   0.0301   0.2817
#> 129:   -0.9803   2.5082   2.4757   2.3057   5.1101   0.1938   0.2498   0.0294   0.2825
#> 130:   -0.9849   2.4978   2.4646   2.3111   5.1076   0.2001   0.2505   0.0297   0.2823
#> 131:   -0.9885   2.4940   2.4622   2.3152   5.1156   0.2089   0.2554   0.0292   0.2829
#> 132:   -0.9761   2.5087   2.4688   2.3044   5.1160   0.2077   0.2571   0.0283   0.2820
#> 133:   -0.9781   2.5176   2.4763   2.3136   5.1139   0.1982   0.2443   0.0274   0.2838
#> 134:   -0.9680   2.5289   2.4717   2.3061   5.1105   0.1960   0.2427   0.0287   0.2838
#> 135:   -0.9811   2.5183   2.4635   2.3107   5.1115   0.2040   0.2323   0.0290   0.2829
#> 136:   -0.9671   2.5120   2.4548   2.3172   5.1110   0.1938   0.2382   0.0292   0.2823
#> 137:   -0.9716   2.5096   2.4596   2.3236   5.1105   0.1995   0.2372   0.0283   0.2842
#> 138:   -0.9948   2.4921   2.4611   2.3214   5.1129   0.2035   0.2438   0.0269   0.2843
#> 139:   -0.9795   2.4999   2.4511   2.3310   5.1120   0.2066   0.2402   0.0306   0.2818
#> 140:   -0.9622   2.5055   2.4494   2.3298   5.1149   0.2137   0.2476   0.0311   0.2808
#> 141:   -0.9811   2.4985   2.4548   2.3322   5.1159   0.2143   0.2352   0.0318   0.2819
#> 142:   -0.9861   2.5171   2.4494   2.3237   5.1174   0.2150   0.2347   0.0306   0.2856
#> 143:   -0.9818   2.5031   2.4482   2.3232   5.1186   0.2048   0.2392   0.0291   0.2836
#> 144:   -1.0055   2.4963   2.4424   2.3297   5.1186   0.1968   0.2386   0.0276   0.2822
#> 145:   -0.9968   2.5051   2.4323   2.3258   5.1213   0.1916   0.2356   0.0262   0.2799
#> 146:   -0.9917   2.5177   2.4362   2.3319   5.1235   0.1934   0.2347   0.0263   0.2818
#> 147:   -0.9956   2.4952   2.4354   2.3197   5.1216   0.1887   0.2449   0.0289   0.2808
#> 148:   -0.9851   2.5072   2.4315   2.3136   5.1226   0.1895   0.2328   0.0318   0.2826
#> 149:   -0.9857   2.5029   2.4375   2.3259   5.1203   0.1911   0.2493   0.0308   0.2839
#> 150:   -0.9848   2.5091   2.4442   2.3044   5.1166   0.1975   0.2426   0.0314   0.2819
#> 151:   -0.9975   2.5027   2.4449   2.3005   5.1191   0.1952   0.2598   0.0309   0.2815
#> 152:   -1.0004   2.4859   2.4497   2.2933   5.1179   0.1977   0.2583   0.0318   0.2815
#> 153:   -1.0012   2.5028   2.4415   2.2999   5.1161   0.2005   0.2366   0.0328   0.2806
#> 154:   -0.9952   2.5034   2.4452   2.2996   5.1150   0.1985   0.2394   0.0299   0.2838
#> 155:   -1.0004   2.5101   2.4501   2.3047   5.1150   0.1901   0.2380   0.0301   0.2841
#> 156:   -0.9978   2.5272   2.4568   2.3035   5.1205   0.1933   0.2303   0.0291   0.2859
#> 157:   -0.9901   2.5256   2.4595   2.3006   5.1201   0.2078   0.2291   0.0296   0.2836
#> 158:   -0.9686   2.5194   2.4546   2.2974   5.1206   0.2179   0.2346   0.0294   0.2817
#> 159:   -1.0069   2.5051   2.4582   2.2966   5.1199   0.2003   0.2450   0.0275   0.2803
#> 160:   -0.9797   2.5085   2.4625   2.2960   5.1231   0.2111   0.2413   0.0273   0.2810
#> 161:   -0.9908   2.5140   2.4616   2.2847   5.1217   0.1919   0.2351   0.0266   0.2813
#> 162:   -1.0010   2.5151   2.4563   2.2776   5.1216   0.1968   0.2411   0.0288   0.2818
#> 163:   -1.0021   2.4975   2.4672   2.2756   5.1238   0.1941   0.2390   0.0288   0.2801
#> 164:   -0.9917   2.4889   2.4737   2.2815   5.1186   0.2017   0.2239   0.0280   0.2816
#> 165:   -0.9951   2.5000   2.4782   2.2729   5.1186   0.1998   0.2343   0.0273   0.2811
#> 166:   -1.0032   2.4889   2.4699   2.2784   5.1178   0.2090   0.2375   0.0277   0.2803
#> 167:   -0.9812   2.5113   2.4707   2.2928   5.1134   0.2058   0.2329   0.0299   0.2799
#> 168:   -0.9883   2.5032   2.4653   2.2936   5.1125   0.1905   0.2396   0.0297   0.2782
#> 169:   -0.9917   2.5155   2.4562   2.2940   5.1120   0.2011   0.2455   0.0273   0.2810
#> 170:   -0.9964   2.5134   2.4518   2.2932   5.1131   0.2046   0.2534   0.0262   0.2829
#> 171:   -0.9988   2.5008   2.4567   2.2962   5.1082   0.2051   0.2521   0.0253   0.2826
#> 172:   -0.9951   2.5051   2.4517   2.2920   5.1019   0.2152   0.2470   0.0249   0.2827
#> 173:   -0.9854   2.5109   2.4464   2.3036   5.1059   0.2159   0.2544   0.0251   0.2831
#> 174:   -0.9873   2.5134   2.4479   2.3036   5.1031   0.2235   0.2605   0.0252   0.2838
#> 175:   -0.9911   2.5160   2.4475   2.3019   5.1004   0.1986   0.2669   0.0258   0.2823
#> 176:   -0.9996   2.5100   2.4447   2.3012   5.1013   0.2084   0.2482   0.0257   0.2825
#> 177:   -0.9642   2.5192   2.4490   2.3049   5.0992   0.2023   0.2416   0.0248   0.2790
#> 178:   -0.9765   2.5158   2.4431   2.3056   5.1007   0.2071   0.2400   0.0229   0.2795
#> 179:   -0.9861   2.4879   2.4530   2.2967   5.0983   0.2042   0.2508   0.0255   0.2793
#> 180:   -1.0005   2.5005   2.4548   2.3030   5.0959   0.2100   0.2431   0.0252   0.2830
#> 181:   -0.9810   2.5073   2.4568   2.2979   5.0978   0.2064   0.2388   0.0252   0.2817
#> 182:   -0.9835   2.5272   2.4536   2.2976   5.0925   0.2119   0.2362   0.0278   0.2836
#> 183:   -0.9938   2.4994   2.4588   2.2935   5.0927   0.2067   0.2559   0.0250   0.2824
#> 184:   -0.9850   2.5077   2.4537   2.3008   5.0945   0.1971   0.2433   0.0267   0.2823
#> 185:   -0.9918   2.5169   2.4492   2.2981   5.0955   0.2032   0.2432   0.0265   0.2817
#> 186:   -0.9851   2.5080   2.4499   2.2964   5.0956   0.2108   0.2392   0.0263   0.2826
#> 187:   -0.9931   2.5192   2.4561   2.2880   5.0977   0.2118   0.2410   0.0245   0.2819
#> 188:   -0.9892   2.5031   2.4626   2.2954   5.1005   0.1999   0.2422   0.0242   0.2812
#> 189:   -0.9875   2.5114   2.4591   2.3030   5.1018   0.2149   0.2346   0.0232   0.2808
#> 190:   -0.9834   2.5012   2.4618   2.2976   5.1041   0.2028   0.2525   0.0221   0.2797
#> 191:   -0.9772   2.5096   2.4692   2.2960   5.1053   0.2151   0.2459   0.0234   0.2816
#> 192:   -0.9725   2.5215   2.4622   2.2955   5.1076   0.2098   0.2498   0.0234   0.2806
#> 193:   -0.9748   2.5293   2.4555   2.2901   5.1075   0.2019   0.2286   0.0234   0.2810
#> 194:   -0.9750   2.5396   2.4488   2.2958   5.1103   0.2060   0.2301   0.0245   0.2817
#> 195:   -0.9771   2.5222   2.4575   2.2999   5.1100   0.2067   0.2405   0.0239   0.2815
#> 196:   -0.9829   2.5131   2.4546   2.3037   5.1073   0.2018   0.2462   0.0257   0.2814
#> 197:   -0.9817   2.5180   2.4582   2.3034   5.1126   0.2064   0.2482   0.0267   0.2795
#> 198:   -0.9884   2.5008   2.4573   2.2960   5.1127   0.2152   0.2566   0.0266   0.2809
#> 199:   -0.9726   2.5089   2.4554   2.3055   5.1088   0.2048   0.2423   0.0255   0.2797
#> 200:   -0.9834   2.5186   2.4575   2.2867   5.1132   0.2142   0.2265   0.0269   0.2817
#> 201:   -0.9787   2.5191   2.4561   2.2882   5.1140   0.2145   0.2289   0.0274   0.2801
#> 202:   -0.9766   2.5170   2.4541   2.2956   5.1115   0.2100   0.2312   0.0277   0.2793
#> 203:   -0.9756   2.5179   2.4537   2.2961   5.1110   0.2093   0.2318   0.0280   0.2796
#> 204:   -0.9749   2.5163   2.4569   2.2948   5.1111   0.2081   0.2343   0.0279   0.2799
#> 205:   -0.9759   2.5141   2.4574   2.2944   5.1106   0.2036   0.2370   0.0278   0.2800
#> 206:   -0.9765   2.5134   2.4600   2.2956   5.1101   0.2017   0.2362   0.0278   0.2801
#> 207:   -0.9773   2.5115   2.4601   2.2949   5.1097   0.2013   0.2364   0.0279   0.2801
#> 208:   -0.9774   2.5116   2.4591   2.2957   5.1100   0.2019   0.2365   0.0286   0.2802
#> 209:   -0.9772   2.5113   2.4593   2.2962   5.1099   0.2018   0.2366   0.0290   0.2803
#> 210:   -0.9785   2.5111   2.4589   2.2953   5.1101   0.2024   0.2370   0.0291   0.2803
#> 211:   -0.9787   2.5110   2.4586   2.2959   5.1103   0.2021   0.2378   0.0290   0.2803
#> 212:   -0.9781   2.5109   2.4583   2.2953   5.1102   0.2017   0.2380   0.0289   0.2801
#> 213:   -0.9791   2.5108   2.4575   2.2950   5.1099   0.2009   0.2392   0.0288   0.2800
#> 214:   -0.9798   2.5108   2.4569   2.2952   5.1099   0.2003   0.2396   0.0288   0.2800
#> 215:   -0.9803   2.5102   2.4565   2.2958   5.1095   0.2002   0.2403   0.0288   0.2799
#> 216:   -0.9806   2.5096   2.4564   2.2955   5.1095   0.2006   0.2406   0.0288   0.2800
#> 217:   -0.9811   2.5078   2.4561   2.2950   5.1095   0.2007   0.2415   0.0289   0.2799
#> 218:   -0.9817   2.5067   2.4558   2.2944   5.1097   0.2007   0.2420   0.0289   0.2799
#> 219:   -0.9818   2.5056   2.4558   2.2951   5.1096   0.2000   0.2430   0.0289   0.2799
#> 220:   -0.9812   2.5062   2.4555   2.2955   5.1098   0.1997   0.2437   0.0289   0.2800
#> 221:   -0.9815   2.5063   2.4549   2.2950   5.1098   0.1999   0.2444   0.0288   0.2800
#> 222:   -0.9816   2.5059   2.4549   2.2952   5.1100   0.2004   0.2451   0.0288   0.2799
#> 223:   -0.9825   2.5055   2.4545   2.2948   5.1099   0.2005   0.2454   0.0288   0.2799
#> 224:   -0.9835   2.5057   2.4540   2.2950   5.1099   0.2005   0.2455   0.0287   0.2800
#> 225:   -0.9839   2.5058   2.4536   2.2948   5.1099   0.2005   0.2458   0.0288   0.2801
#> 226:   -0.9843   2.5059   2.4531   2.2944   5.1097   0.2003   0.2461   0.0288   0.2801
#> 227:   -0.9845   2.5067   2.4530   2.2941   5.1097   0.2006   0.2457   0.0287   0.2802
#> 228:   -0.9847   2.5071   2.4527   2.2936   5.1096   0.2010   0.2452   0.0288   0.2802
#> 229:   -0.9845   2.5075   2.4528   2.2936   5.1095   0.2014   0.2454   0.0288   0.2803
#> 230:   -0.9842   2.5082   2.4529   2.2936   5.1094   0.2014   0.2452   0.0288   0.2804
#> 231:   -0.9837   2.5085   2.4532   2.2937   5.1094   0.2020   0.2450   0.0289   0.2804
#> 232:   -0.9836   2.5085   2.4535   2.2937   5.1096   0.2021   0.2449   0.0290   0.2804
#> 233:   -0.9836   2.5085   2.4538   2.2938   5.1095   0.2019   0.2454   0.0291   0.2805
#> 234:   -0.9839   2.5083   2.4538   2.2938   5.1095   0.2020   0.2459   0.0291   0.2805
#> 235:   -0.9847   2.5082   2.4538   2.2937   5.1095   0.2017   0.2460   0.0292   0.2805
#> 236:   -0.9846   2.5081   2.4535   2.2937   5.1095   0.2016   0.2463   0.0292   0.2805
#> 237:   -0.9851   2.5083   2.4532   2.2933   5.1096   0.2017   0.2463   0.0292   0.2805
#> 238:   -0.9852   2.5084   2.4531   2.2933   5.1097   0.2016   0.2461   0.0291   0.2806
#> 239:   -0.9856   2.5085   2.4530   2.2932   5.1096   0.2014   0.2461   0.0291   0.2806
#> 240:   -0.9861   2.5085   2.4530   2.2930   5.1096   0.2015   0.2464   0.0291   0.2806
#> 241:   -0.9864   2.5086   2.4531   2.2928   5.1095   0.2013   0.2465   0.0291   0.2807
#> 242:   -0.9867   2.5085   2.4530   2.2926   5.1095   0.2011   0.2464   0.0292   0.2807
#> 243:   -0.9870   2.5083   2.4528   2.2926   5.1095   0.2010   0.2465   0.0293   0.2806
#> 244:   -0.9872   2.5082   2.4527   2.2927   5.1094   0.2007   0.2463   0.0293   0.2806
#> 245:   -0.9874   2.5078   2.4527   2.2926   5.1095   0.2006   0.2463   0.0294   0.2807
#> 246:   -0.9877   2.5080   2.4528   2.2927   5.1095   0.2005   0.2464   0.0294   0.2807
#> 247:   -0.9878   2.5079   2.4530   2.2927   5.1096   0.2005   0.2465   0.0295   0.2807
#> 248:   -0.9877   2.5081   2.4529   2.2930   5.1097   0.2008   0.2464   0.0296   0.2808
#> 249:   -0.9874   2.5081   2.4529   2.2932   5.1098   0.2009   0.2463   0.0296   0.2808
#> 250:   -0.9874   2.5081   2.4528   2.2934   5.1098   0.2008   0.2462   0.0296   0.2808
#> 251:   -0.9876   2.5083   2.4527   2.2935   5.1098   0.2005   0.2463   0.0296   0.2808
#> 252:   -0.9875   2.5084   2.4526   2.2935   5.1098   0.2006   0.2463   0.0296   0.2808
#> 253:   -0.9873   2.5086   2.4524   2.2935   5.1098   0.2007   0.2463   0.0297   0.2808
#> 254:   -0.9872   2.5088   2.4522   2.2938   5.1099   0.2010   0.2464   0.0297   0.2808
#> 255:   -0.9871   2.5093   2.4519   2.2937   5.1099   0.2009   0.2465   0.0296   0.2808
#> 256:   -0.9873   2.5094   2.4518   2.2938   5.1099   0.2012   0.2465   0.0296   0.2809
#> 257:   -0.9872   2.5096   2.4518   2.2939   5.1099   0.2012   0.2464   0.0296   0.2809
#> 258:   -0.9873   2.5096   2.4518   2.2938   5.1099   0.2012   0.2464   0.0295   0.2810
#> 259:   -0.9871   2.5094   2.4518   2.2939   5.1099   0.2009   0.2462   0.0295   0.2809
#> 260:   -0.9870   2.5094   2.4518   2.2939   5.1099   0.2009   0.2461   0.0295   0.2809
#> 261:   -0.9869   2.5095   2.4519   2.2939   5.1098   0.2009   0.2460   0.0295   0.2809
#> 262:   -0.9869   2.5096   2.4520   2.2939   5.1097   0.2010   0.2460   0.0295   0.2810
#> 263:   -0.9868   2.5098   2.4520   2.2936   5.1097   0.2011   0.2460   0.0295   0.2810
#> 264:   -0.9868   2.5100   2.4521   2.2936   5.1096   0.2011   0.2461   0.0295   0.2810
#> 265:   -0.9866   2.5100   2.4521   2.2936   5.1096   0.2012   0.2460   0.0295   0.2810
#> 266:   -0.9863   2.5102   2.4518   2.2936   5.1096   0.2014   0.2458   0.0295   0.2810
#> 267:   -0.9862   2.5101   2.4518   2.2935   5.1096   0.2015   0.2459   0.0295   0.2810
#> 268:   -0.9862   2.5103   2.4516   2.2936   5.1096   0.2014   0.2461   0.0295   0.2810
#> 269:   -0.9862   2.5106   2.4515   2.2936   5.1096   0.2015   0.2462   0.0295   0.2810
#> 270:   -0.9862   2.5106   2.4514   2.2935   5.1096   0.2017   0.2460   0.0295   0.2810
#> 271:   -0.9861   2.5107   2.4515   2.2934   5.1096   0.2018   0.2462   0.0295   0.2811
#> 272:   -0.9861   2.5108   2.4516   2.2936   5.1096   0.2017   0.2461   0.0295   0.2811
#> 273:   -0.9860   2.5108   2.4516   2.2936   5.1095   0.2015   0.2462   0.0295   0.2811
#> 274:   -0.9860   2.5108   2.4516   2.2936   5.1096   0.2013   0.2464   0.0295   0.2811
#> 275:   -0.9860   2.5110   2.4516   2.2936   5.1096   0.2012   0.2464   0.0295   0.2812
#> 276:   -0.9861   2.5110   2.4517   2.2937   5.1096   0.2012   0.2465   0.0295   0.2812
#> 277:   -0.9860   2.5109   2.4518   2.2936   5.1096   0.2013   0.2465   0.0295   0.2812
#> 278:   -0.9860   2.5110   2.4519   2.2936   5.1096   0.2013   0.2467   0.0295   0.2812
#> 279:   -0.9859   2.5111   2.4520   2.2937   5.1096   0.2014   0.2467   0.0295   0.2811
#> 280:   -0.9860   2.5108   2.4522   2.2936   5.1096   0.2014   0.2468   0.0295   0.2812
#> 281:   -0.9860   2.5106   2.4523   2.2936   5.1096   0.2014   0.2469   0.0295   0.2812
#> 282:   -0.9861   2.5106   2.4522   2.2937   5.1096   0.2013   0.2469   0.0295   0.2811
#> 283:   -0.9862   2.5105   2.4521   2.2937   5.1096   0.2012   0.2470   0.0294   0.2812
#> 284:   -0.9865   2.5105   2.4520   2.2936   5.1096   0.2011   0.2471   0.0294   0.2812
#> 285:   -0.9865   2.5106   2.4519   2.2937   5.1096   0.2011   0.2472   0.0294   0.2812
#> 286:   -0.9866   2.5105   2.4518   2.2936   5.1097   0.2011   0.2472   0.0294   0.2812
#> 287:   -0.9867   2.5104   2.4517   2.2936   5.1097   0.2010   0.2471   0.0294   0.2812
#> 288:   -0.9868   2.5104   2.4517   2.2936   5.1096   0.2011   0.2470   0.0294   0.2812
#> 289:   -0.9869   2.5103   2.4517   2.2935   5.1096   0.2010   0.2470   0.0294   0.2812
#> 290:   -0.9869   2.5104   2.4516   2.2935   5.1096   0.2010   0.2470   0.0294   0.2812
#> 291:   -0.9871   2.5104   2.4515   2.2934   5.1096   0.2011   0.2469   0.0295   0.2812
#> 292:   -0.9872   2.5103   2.4515   2.2934   5.1096   0.2011   0.2471   0.0295   0.2812
#> 293:   -0.9872   2.5104   2.4515   2.2935   5.1096   0.2011   0.2470   0.0295   0.2813
#> 294:   -0.9874   2.5102   2.4515   2.2935   5.1096   0.2010   0.2470   0.0295   0.2813
#> 295:   -0.9875   2.5100   2.4515   2.2935   5.1096   0.2009   0.2470   0.0295   0.2813
#> 296:   -0.9874   2.5100   2.4515   2.2934   5.1096   0.2009   0.2470   0.0295   0.2813
#> 297:   -0.9875   2.5100   2.4515   2.2934   5.1096   0.2009   0.2472   0.0295   0.2813
#> 298:   -0.9875   2.5099   2.4515   2.2933   5.1096   0.2008   0.2472   0.0295   0.2813
#> 299:   -0.9875   2.5099   2.4514   2.2934   5.1096   0.2008   0.2473   0.0295   0.2813
#> 300:   -0.9876   2.5098   2.4513   2.2934   5.1096   0.2009   0.2474   0.0295   0.2813
#> 301:   -0.9876   2.5099   2.4512   2.2934   5.1096   0.2010   0.2474   0.0295   0.2814
#> 302:   -0.9877   2.5099   2.4511   2.2934   5.1096   0.2010   0.2474   0.0295   0.2814
#> 303:   -0.9878   2.5099   2.4510   2.2934   5.1096   0.2010   0.2475   0.0295   0.2814
#> 304:   -0.9878   2.5100   2.4510   2.2934   5.1096   0.2011   0.2475   0.0296   0.2814
#> 305:   -0.9878   2.5100   2.4511   2.2935   5.1096   0.2012   0.2476   0.0296   0.2815
#> 306:   -0.9877   2.5101   2.4510   2.2935   5.1096   0.2015   0.2476   0.0296   0.2815
#> 307:   -0.9877   2.5102   2.4510   2.2935   5.1096   0.2016   0.2476   0.0296   0.2815
#> 308:   -0.9878   2.5103   2.4509   2.2934   5.1096   0.2018   0.2476   0.0296   0.2815
#> 309:   -0.9878   2.5104   2.4510   2.2934   5.1096   0.2019   0.2475   0.0296   0.2815
#> 310:   -0.9879   2.5103   2.4511   2.2934   5.1096   0.2019   0.2475   0.0296   0.2815
#> 311:   -0.9879   2.5103   2.4512   2.2934   5.1096   0.2019   0.2476   0.0296   0.2815
#> 312:   -0.9880   2.5104   2.4512   2.2934   5.1096   0.2018   0.2476   0.0296   0.2815
#> 313:   -0.9879   2.5104   2.4511   2.2934   5.1096   0.2018   0.2478   0.0296   0.2815
#> 314:   -0.9880   2.5103   2.4511   2.2934   5.1096   0.2017   0.2478   0.0295   0.2815
#> 315:   -0.9881   2.5102   2.4512   2.2934   5.1096   0.2018   0.2477   0.0295   0.2815
#> 316:   -0.9882   2.5102   2.4512   2.2934   5.1096   0.2018   0.2477   0.0295   0.2815
#> 317:   -0.9882   2.5103   2.4511   2.2934   5.1096   0.2019   0.2476   0.0295   0.2816
#> 318:   -0.9883   2.5103   2.4511   2.2934   5.1096   0.2019   0.2476   0.0294   0.2816
#> 319:   -0.9884   2.5104   2.4511   2.2934   5.1096   0.2019   0.2476   0.0294   0.2816
#> 320:   -0.9886   2.5103   2.4510   2.2934   5.1096   0.2019   0.2478   0.0294   0.2816
#> 321:   -0.9886   2.5103   2.4510   2.2934   5.1096   0.2019   0.2479   0.0294   0.2816
#> 322:   -0.9885   2.5103   2.4510   2.2934   5.1096   0.2019   0.2480   0.0294   0.2816
#> 323:   -0.9886   2.5105   2.4509   2.2934   5.1096   0.2019   0.2480   0.0294   0.2816
#> 324:   -0.9887   2.5105   2.4508   2.2934   5.1096   0.2019   0.2481   0.0294   0.2816
#> 325:   -0.9887   2.5105   2.4507   2.2934   5.1096   0.2019   0.2482   0.0294   0.2816
#> 326:   -0.9888   2.5104   2.4506   2.2934   5.1096   0.2020   0.2482   0.0293   0.2817
#> 327:   -0.9890   2.5103   2.4506   2.2934   5.1096   0.2020   0.2483   0.0293   0.2817
#> 328:   -0.9890   2.5103   2.4506   2.2934   5.1096   0.2020   0.2484   0.0293   0.2817
#> 329:   -0.9890   2.5103   2.4506   2.2934   5.1096   0.2020   0.2485   0.0293   0.2817
#> 330:   -0.9891   2.5102   2.4505   2.2934   5.1096   0.2020   0.2486   0.0292   0.2817
#> 331:   -0.9892   2.5102   2.4505   2.2934   5.1096   0.2020   0.2487   0.0292   0.2818
#> 332:   -0.9893   2.5102   2.4506   2.2934   5.1096   0.2020   0.2487   0.0292   0.2818
#> 333:   -0.9894   2.5103   2.4505   2.2934   5.1096   0.2019   0.2487   0.0292   0.2818
#> 334:   -0.9893   2.5102   2.4505   2.2934   5.1096   0.2018   0.2486   0.0292   0.2818
#> 335:   -0.9894   2.5102   2.4504   2.2935   5.1096   0.2018   0.2485   0.0292   0.2818
#> 336:   -0.9895   2.5102   2.4503   2.2935   5.1096   0.2018   0.2485   0.0291   0.2818
#> 337:   -0.9895   2.5101   2.4503   2.2934   5.1096   0.2018   0.2485   0.0291   0.2819
#> 338:   -0.9896   2.5101   2.4502   2.2935   5.1096   0.2018   0.2484   0.0291   0.2819
#> 339:   -0.9897   2.5101   2.4502   2.2935   5.1096   0.2018   0.2484   0.0291   0.2819
#> 340:   -0.9898   2.5100   2.4502   2.2934   5.1096   0.2017   0.2484   0.0291   0.2819
#> 341:   -0.9898   2.5099   2.4501   2.2934   5.1096   0.2018   0.2483   0.0290   0.2819
#> 342:   -0.9898   2.5100   2.4501   2.2934   5.1096   0.2019   0.2482   0.0290   0.2819
#> 343:   -0.9898   2.5100   2.4500   2.2934   5.1096   0.2019   0.2483   0.0290   0.2819
#> 344:   -0.9899   2.5100   2.4500   2.2934   5.1096   0.2019   0.2483   0.0290   0.2819
#> 345:   -0.9899   2.5099   2.4500   2.2934   5.1096   0.2020   0.2484   0.0289   0.2819
#> 346:   -0.9899   2.5099   2.4500   2.2934   5.1096   0.2021   0.2485   0.0289   0.2819
#> 347:   -0.9900   2.5099   2.4500   2.2934   5.1096   0.2020   0.2484   0.0289   0.2820
#> 348:   -0.9900   2.5099   2.4500   2.2934   5.1096   0.2021   0.2484   0.0289   0.2820
#> 349:   -0.9901   2.5099   2.4500   2.2934   5.1096   0.2021   0.2484   0.0289   0.2820
#> 350:   -0.9901   2.5099   2.4499   2.2934   5.1096   0.2021   0.2485   0.0289   0.2820
#> 351:   -0.9901   2.5098   2.4498   2.2934   5.1096   0.2022   0.2485   0.0289   0.2820
#> 352:   -0.9903   2.5098   2.4497   2.2934   5.1096   0.2022   0.2485   0.0289   0.2820
#> 353:   -0.9902   2.5098   2.4497   2.2934   5.1096   0.2022   0.2486   0.0289   0.2820
#> 354:   -0.9903   2.5097   2.4498   2.2934   5.1096   0.2022   0.2486   0.0289   0.2820
#> 355:   -0.9903   2.5097   2.4497   2.2934   5.1096   0.2022   0.2486   0.0289   0.2820
#> 356:   -0.9903   2.5095   2.4497   2.2934   5.1096   0.2022   0.2486   0.0289   0.2820
#> 357:   -0.9903   2.5095   2.4497   2.2934   5.1096   0.2023   0.2486   0.0289   0.2820
#> 358:   -0.9903   2.5095   2.4497   2.2934   5.1096   0.2024   0.2486   0.0289   0.2821
#> 359:   -0.9902   2.5095   2.4497   2.2934   5.1096   0.2024   0.2487   0.0288   0.2821
#> 360:   -0.9902   2.5095   2.4497   2.2934   5.1096   0.2024   0.2488   0.0289   0.2821
#> 361:   -0.9902   2.5095   2.4497   2.2934   5.1096   0.2024   0.2489   0.0288   0.2821
#> 362:   -0.9902   2.5095   2.4496   2.2934   5.1096   0.2024   0.2489   0.0288   0.2821
#> 363:   -0.9902   2.5095   2.4496   2.2934   5.1096   0.2024   0.2490   0.0288   0.2821
#> 364:   -0.9903   2.5094   2.4496   2.2934   5.1096   0.2024   0.2489   0.0288   0.2821
#> 365:   -0.9904   2.5094   2.4495   2.2934   5.1096   0.2024   0.2488   0.0288   0.2821
#> 366:   -0.9905   2.5093   2.4495   2.2934   5.1096   0.2024   0.2488   0.0288   0.2821
#> 367:   -0.9906   2.5093   2.4494   2.2934   5.1096   0.2025   0.2488   0.0288   0.2821
#> 368:   -0.9905   2.5094   2.4493   2.2934   5.1096   0.2026   0.2488   0.0289   0.2821
#> 369:   -0.9905   2.5095   2.4493   2.2934   5.1096   0.2028   0.2487   0.0289   0.2821
#> 370:   -0.9904   2.5096   2.4493   2.2934   5.1096   0.2028   0.2487   0.0289   0.2821
#> 371:   -0.9904   2.5097   2.4493   2.2934   5.1096   0.2028   0.2487   0.0289   0.2821
#> 372:   -0.9904   2.5097   2.4493   2.2934   5.1096   0.2028   0.2487   0.0289   0.2821
#> 373:   -0.9905   2.5097   2.4492   2.2934   5.1096   0.2028   0.2487   0.0289   0.2821
#> 374:   -0.9905   2.5097   2.4492   2.2934   5.1096   0.2029   0.2486   0.0289   0.2821
#> 375:   -0.9905   2.5097   2.4492   2.2934   5.1096   0.2028   0.2486   0.0288   0.2822
#> 376:   -0.9906   2.5098   2.4492   2.2934   5.1096   0.2028   0.2486   0.0288   0.2822
#> 377:   -0.9906   2.5098   2.4491   2.2934   5.1096   0.2028   0.2486   0.0288   0.2822
#> 378:   -0.9907   2.5098   2.4491   2.2934   5.1096   0.2027   0.2485   0.0288   0.2822
#> 379:   -0.9907   2.5098   2.4491   2.2934   5.1096   0.2028   0.2486   0.0288   0.2822
#> 380:   -0.9908   2.5098   2.4490   2.2934   5.1096   0.2030   0.2485   0.0288   0.2822
#> 381:   -0.9909   2.5098   2.4489   2.2934   5.1096   0.2031   0.2485   0.0288   0.2822
#> 382:   -0.9909   2.5098   2.4489   2.2934   5.1096   0.2031   0.2486   0.0287   0.2823
#> 383:   -0.9909   2.5098   2.4490   2.2934   5.1096   0.2031   0.2486   0.0287   0.2823
#> 384:   -0.9909   2.5098   2.4489   2.2934   5.1096   0.2030   0.2486   0.0287   0.2823
#> 385:   -0.9910   2.5097   2.4489   2.2934   5.1096   0.2030   0.2486   0.0287   0.2823
#> 386:   -0.9910   2.5097   2.4489   2.2934   5.1096   0.2030   0.2486   0.0287   0.2823
#> 387:   -0.9910   2.5096   2.4489   2.2934   5.1096   0.2030   0.2486   0.0287   0.2823
#> 388:   -0.9911   2.5096   2.4489   2.2934   5.1096   0.2030   0.2486   0.0287   0.2823
#> 389:   -0.9911   2.5095   2.4488   2.2934   5.1096   0.2029   0.2486   0.0287   0.2823
#> 390:   -0.9912   2.5094   2.4488   2.2934   5.1096   0.2029   0.2487   0.0287   0.2823
#> 391:   -0.9912   2.5095   2.4487   2.2934   5.1096   0.2029   0.2486   0.0287   0.2823
#> 392:   -0.9912   2.5096   2.4487   2.2934   5.1096   0.2029   0.2486   0.0287   0.2823
#> 393:   -0.9912   2.5096   2.4487   2.2934   5.1096   0.2029   0.2487   0.0287   0.2823
#> 394:   -0.9912   2.5096   2.4488   2.2934   5.1096   0.2029   0.2487   0.0287   0.2823
#> 395:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2029   0.2487   0.0287   0.2823
#> 396:   -0.9912   2.5098   2.4488   2.2934   5.1096   0.2029   0.2488   0.0287   0.2823
#> 397:   -0.9911   2.5098   2.4488   2.2934   5.1096   0.2030   0.2488   0.0287   0.2823
#> 398:   -0.9911   2.5098   2.4488   2.2934   5.1096   0.2031   0.2489   0.0287   0.2823
#> 399:   -0.9911   2.5098   2.4488   2.2934   5.1096   0.2031   0.2489   0.0287   0.2823
#> 400:   -0.9910   2.5098   2.4488   2.2934   5.1096   0.2031   0.2489   0.0287   0.2823
#> 401:   -0.9910   2.5098   2.4488   2.2934   5.1096   0.2032   0.2489   0.0287   0.2823
#> 402:   -0.9911   2.5097   2.4488   2.2934   5.1096   0.2032   0.2489   0.0287   0.2823
#> 403:   -0.9911   2.5098   2.4488   2.2934   5.1096   0.2033   0.2489   0.0287   0.2823
#> 404:   -0.9911   2.5098   2.4488   2.2934   5.1096   0.2033   0.2490   0.0287   0.2823
#> 405:   -0.9911   2.5098   2.4487   2.2934   5.1096   0.2032   0.2490   0.0287   0.2823
#> 406:   -0.9911   2.5098   2.4487   2.2934   5.1096   0.2033   0.2489   0.0287   0.2823
#> 407:   -0.9911   2.5098   2.4487   2.2934   5.1096   0.2033   0.2490   0.0287   0.2823
#> 408:   -0.9912   2.5097   2.4487   2.2934   5.1096   0.2033   0.2490   0.0287   0.2824
#> 409:   -0.9912   2.5097   2.4487   2.2934   5.1096   0.2033   0.2490   0.0287   0.2824
#> 410:   -0.9912   2.5096   2.4487   2.2934   5.1096   0.2033   0.2491   0.0287   0.2824
#> 411:   -0.9913   2.5096   2.4486   2.2934   5.1096   0.2034   0.2491   0.0287   0.2824
#> 412:   -0.9913   2.5096   2.4487   2.2934   5.1096   0.2034   0.2492   0.0287   0.2824
#> 413:   -0.9912   2.5097   2.4487   2.2934   5.1096   0.2033   0.2491   0.0287   0.2824
#> 414:   -0.9912   2.5097   2.4487   2.2934   5.1096   0.2033   0.2491   0.0287   0.2824
#> 415:   -0.9913   2.5097   2.4488   2.2934   5.1096   0.2034   0.2492   0.0286   0.2824
#> 416:   -0.9913   2.5097   2.4488   2.2934   5.1096   0.2034   0.2492   0.0286   0.2824
#> 417:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2034   0.2492   0.0286   0.2824
#> 418:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2034   0.2492   0.0286   0.2824
#> 419:   -0.9911   2.5098   2.4488   2.2934   5.1096   0.2035   0.2492   0.0286   0.2824
#> 420:   -0.9911   2.5097   2.4488   2.2934   5.1096   0.2035   0.2492   0.0286   0.2824
#> 421:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2036   0.2493   0.0286   0.2824
#> 422:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2036   0.2493   0.0286   0.2824
#> 423:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2036   0.2493   0.0286   0.2824
#> 424:   -0.9912   2.5097   2.4488   2.2934   5.1096   0.2036   0.2493   0.0286   0.2824
#> 425:   -0.9913   2.5096   2.4488   2.2934   5.1096   0.2036   0.2492   0.0286   0.2824
#> 426:   -0.9913   2.5096   2.4487   2.2934   5.1096   0.2036   0.2493   0.0286   0.2824
#> 427:   -0.9913   2.5096   2.4487   2.2934   5.1096   0.2036   0.2493   0.0286   0.2824
#> 428:   -0.9913   2.5096   2.4486   2.2934   5.1096   0.2036   0.2494   0.0286   0.2824
#> 429:   -0.9913   2.5096   2.4485   2.2934   5.1096   0.2036   0.2494   0.0286   0.2824
#> 430:   -0.9914   2.5096   2.4485   2.2934   5.1096   0.2036   0.2494   0.0286   0.2825
#> 431:   -0.9915   2.5096   2.4484   2.2934   5.1096   0.2036   0.2494   0.0286   0.2825
#> 432:   -0.9915   2.5095   2.4484   2.2934   5.1096   0.2036   0.2494   0.0286   0.2825
#> 433:   -0.9917   2.5095   2.4484   2.2934   5.1096   0.2036   0.2494   0.0286   0.2825
#> 434:   -0.9917   2.5094   2.4483   2.2934   5.1096   0.2036   0.2494   0.0286   0.2825
#> 435:   -0.9917   2.5094   2.4483   2.2934   5.1096   0.2036   0.2494   0.0285   0.2824
#> 436:   -0.9917   2.5094   2.4483   2.2934   5.1096   0.2036   0.2494   0.0285   0.2825
#> 437:   -0.9917   2.5094   2.4483   2.2934   5.1096   0.2036   0.2495   0.0285   0.2825
#> 438:   -0.9918   2.5094   2.4484   2.2934   5.1096   0.2036   0.2496   0.0285   0.2825
#> 439:   -0.9918   2.5093   2.4484   2.2934   5.1096   0.2037   0.2496   0.0285   0.2825
#> 440:   -0.9918   2.5093   2.4484   2.2934   5.1096   0.2037   0.2496   0.0285   0.2825
#> 441:   -0.9919   2.5092   2.4484   2.2934   5.1096   0.2037   0.2496   0.0285   0.2825
#> 442:   -0.9919   2.5092   2.4484   2.2934   5.1096   0.2038   0.2496   0.0285   0.2825
#> 443:   -0.9919   2.5091   2.4484   2.2934   5.1096   0.2038   0.2496   0.0285   0.2825
#> 444:   -0.9919   2.5091   2.4484   2.2934   5.1096   0.2038   0.2495   0.0285   0.2825
#> 445:   -0.9919   2.5092   2.4484   2.2934   5.1096   0.2039   0.2495   0.0285   0.2825
#> 446:   -0.9919   2.5092   2.4484   2.2934   5.1096   0.2039   0.2495   0.0285   0.2825
#> 447:   -0.9919   2.5092   2.4484   2.2934   5.1096   0.2040   0.2495   0.0285   0.2825
#> 448:   -0.9920   2.5092   2.4483   2.2934   5.1096   0.2040   0.2495   0.0285   0.2825
#> 449:   -0.9920   2.5092   2.4483   2.2934   5.1096   0.2039   0.2495   0.0285   0.2825
#> 450:   -0.9921   2.5093   2.4483   2.2934   5.1096   0.2039   0.2495   0.0285   0.2825
#> 451:   -0.9921   2.5093   2.4483   2.2934   5.1096   0.2038   0.2496   0.0285   0.2825
#> 452:   -0.9921   2.5092   2.4483   2.2934   5.1096   0.2038   0.2495   0.0285   0.2825
#> 453:   -0.9921   2.5092   2.4483   2.2934   5.1096   0.2038   0.2496   0.0284   0.2825
#> 454:   -0.9920   2.5092   2.4483   2.2934   5.1096   0.2037   0.2495   0.0284   0.2825
#> 455:   -0.9921   2.5092   2.4483   2.2934   5.1096   0.2037   0.2495   0.0284   0.2825
#> 456:   -0.9921   2.5092   2.4483   2.2934   5.1096   0.2037   0.2495   0.0285   0.2826
#> 457:   -0.9922   2.5091   2.4483   2.2934   5.1096   0.2037   0.2496   0.0285   0.2826
#> 458:   -0.9922   2.5091   2.4483   2.2934   5.1096   0.2037   0.2496   0.0285   0.2826
#> 459:   -0.9921   2.5091   2.4483   2.2934   5.1096   0.2037   0.2496   0.0285   0.2826
#> 460:   -0.9921   2.5091   2.4483   2.2934   5.1096   0.2037   0.2495   0.0285   0.2826
#> 461:   -0.9921   2.5092   2.4483   2.2934   5.1096   0.2037   0.2494   0.0285   0.2826
#> 462:   -0.9920   2.5092   2.4483   2.2934   5.1096   0.2038   0.2494   0.0285   0.2826
#> 463:   -0.9920   2.5092   2.4483   2.2934   5.1096   0.2038   0.2495   0.0284   0.2826
#> 464:   -0.9920   2.5092   2.4483   2.2934   5.1096   0.2038   0.2494   0.0284   0.2826
#> 465:   -0.9920   2.5093   2.4483   2.2934   5.1096   0.2038   0.2494   0.0284   0.2826
#> 466:   -0.9919   2.5094   2.4483   2.2934   5.1096   0.2038   0.2494   0.0285   0.2826
#> 467:   -0.9919   2.5094   2.4483   2.2934   5.1096   0.2038   0.2494   0.0284   0.2826
#> 468:   -0.9919   2.5094   2.4484   2.2934   5.1096   0.2038   0.2494   0.0284   0.2826
#> 469:   -0.9918   2.5094   2.4484   2.2934   5.1096   0.2038   0.2494   0.0284   0.2826
#> 470:   -0.9918   2.5094   2.4484   2.2934   5.1096   0.2038   0.2494   0.0284   0.2826
#> 471:   -0.9917   2.5094   2.4484   2.2934   5.1096   0.2038   0.2495   0.0284   0.2826
#> 472:   -0.9918   2.5093   2.4484   2.2934   5.1096   0.2038   0.2496   0.0284   0.2826
#> 473:   -0.9918   2.5093   2.4484   2.2934   5.1096   0.2038   0.2496   0.0284   0.2826
#> 474:   -0.9918   2.5093   2.4484   2.2934   5.1096   0.2038   0.2496   0.0284   0.2826
#> 475:   -0.9917   2.5093   2.4484   2.2934   5.1096   0.2038   0.2495   0.0284   0.2826
#> 476:   -0.9917   2.5093   2.4484   2.2934   5.1096   0.2038   0.2496   0.0284   0.2826
#> 477:   -0.9918   2.5093   2.4483   2.2934   5.1096   0.2038   0.2496   0.0285   0.2826
#> 478:   -0.9919   2.5092   2.4483   2.2934   5.1096   0.2038   0.2496   0.0285   0.2826
#> 479:   -0.9919   2.5092   2.4482   2.2934   5.1096   0.2039   0.2496   0.0285   0.2826
#> 480:   -0.9920   2.5092   2.4482   2.2934   5.1096   0.2039   0.2496   0.0285   0.2826
#> 481:   -0.9920   2.5092   2.4482   2.2934   5.1096   0.2039   0.2496   0.0285   0.2826
#> 482:   -0.9920   2.5092   2.4481   2.2934   5.1096   0.2039   0.2496   0.0285   0.2826
#> 483:   -0.9921   2.5092   2.4481   2.2934   5.1096   0.2039   0.2496   0.0285   0.2826
#> 484:   -0.9921   2.5092   2.4481   2.2934   5.1096   0.2040   0.2496   0.0285   0.2826
#> 485:   -0.9921   2.5092   2.4481   2.2934   5.1096   0.2040   0.2495   0.0285   0.2826
#> 486:   -0.9920   2.5092   2.4481   2.2934   5.1096   0.2040   0.2496   0.0285   0.2826
#> 487:   -0.9920   2.5092   2.4482   2.2934   5.1096   0.2041   0.2495   0.0285   0.2827
#> 488:   -0.9921   2.5092   2.4482   2.2934   5.1096   0.2040   0.2496   0.0285   0.2827
#> 489:   -0.9921   2.5091   2.4481   2.2934   5.1096   0.2040   0.2496   0.0285   0.2826
#> 490:   -0.9920   2.5092   2.4481   2.2934   5.1096   0.2040   0.2496   0.0285   0.2826
#> 491:   -0.9920   2.5092   2.4481   2.2934   5.1096   0.2040   0.2496   0.0285   0.2826
#> 492:   -0.9920   2.5092   2.4481   2.2934   5.1096   0.2040   0.2497   0.0285   0.2826
#> 493:   -0.9920   2.5092   2.4481   2.2934   5.1096   0.2040   0.2497   0.0285   0.2826
#> 494:   -0.9920   2.5091   2.4481   2.2934   5.1096   0.2041   0.2498   0.0285   0.2826
#> 495:   -0.9920   2.5092   2.4480   2.2934   5.1096   0.2041   0.2498   0.0285   0.2827
#> 496:   -0.9921   2.5092   2.4480   2.2934   5.1096   0.2041   0.2498   0.0285   0.2827
#> 497:   -0.9921   2.5092   2.4480   2.2934   5.1096   0.2041   0.2497   0.0285   0.2827
#> 498:   -0.9921   2.5093   2.4479   2.2934   5.1096   0.2041   0.2498   0.0285   0.2827
#> 499:   -0.9921   2.5093   2.4479   2.2934   5.1096   0.2041   0.2498   0.0285   0.2827
#> 500:   -0.9922   2.5093   2.4479   2.2934   5.1096   0.2041   0.2497   0.0285   0.2827
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:20:15

## now try add+prop
cmt2fit.add.prop <- cmt2fit.prop %>%
    update(linCmt() ~ prop(prop.sd) + add(add.sd)) %>%
    nlmixr(est="saem", control=list(control=0), 
           table=tableControl(npde=TRUE, cwres=TRUE))
#> 1:   -1.0177   2.6365   2.4465   2.4003   5.1262   0.1944   0.2434   0.0287   0.1955   0.6178
#> 2:   -1.0950   2.8301   2.4429   2.4712   5.1404   0.1847   0.2313   0.0291   0.0662   0.4902
#> 3:   -1.0745   2.8187   2.4578   2.4647   5.1423   0.1754   0.2284   0.0282   0.0341   0.3848
#> 4:   -1.0417   2.7080   2.4406   2.3662   5.2104   0.1667   0.2170   0.0289   0.0217   0.3209
#> 5:   -1.0092   2.6474   2.4477   2.3582   5.2017   0.1583   0.2061   0.0331   0.0139   0.2922
#> 6:   -0.9825   2.5795   2.4467   2.3628   5.2070   0.1504   0.1967   0.0354   0.0079   0.2874
#> 7:   -0.9704   2.5607   2.4462   2.3543   5.1841   0.1429   0.2037   0.0355   0.0045   0.2844
#> 8:   -0.9539   2.5616   2.4436   2.3234   5.1600   0.1418   0.2132   0.0348   0.0011   0.2944
#> 9:   -9.5503e-01   2.5239e+00   2.4465e+00   2.3452e+00   5.1428e+00   1.3544e-01   2.1297e-01   3.4922e-02   2.7359e-05   2.8917e-01
#> 10:   -9.6154e-01   2.5101e+00   2.4447e+00   2.3521e+00   5.1417e+00   1.3542e-01   2.1279e-01   3.4712e-02   9.2295e-05   2.8340e-01
#> 11:   -9.5907e-01   2.5023e+00   2.4466e+00   2.3519e+00   5.1283e+00   1.3596e-01   2.1716e-01   3.4517e-02   7.7891e-05   2.7541e-01
#> 12:   -9.6657e-01   2.4963e+00   2.4358e+00   2.3463e+00   5.1245e+00   1.3141e-01   2.3303e-01   3.2791e-02   2.9096e-05   2.7578e-01
#> 13:   -9.7922e-01   2.4999e+00   2.4195e+00   2.3268e+00   5.1441e+00   1.3836e-01   2.4222e-01   3.2502e-02   2.7330e-06   2.7882e-01
#> 14:   -9.6916e-01   2.5178e+00   2.4154e+00   2.3237e+00   5.1292e+00   1.3692e-01   2.3605e-01   3.1483e-02   8.9255e-06   2.7835e-01
#> 15:   -9.6284e-01   2.5191e+00   2.4246e+00   2.3358e+00   5.1335e+00   1.4753e-01   2.2696e-01   3.1255e-02   7.6693e-07   2.8254e-01
#> 16:   -9.5888e-01   2.5140e+00   2.4299e+00   2.3486e+00   5.1119e+00   1.5500e-01   2.2625e-01   3.2865e-02   9.7912e-06   2.7899e-01
#> 17:   -9.5816e-01   2.5312e+00   2.4289e+00   2.3374e+00   5.1119e+00   1.6300e-01   2.2286e-01   3.1957e-02   1.0348e-06   2.8254e-01
#> 18:   -9.5062e-01   2.5209e+00   2.4324e+00   2.3372e+00   5.1294e+00   1.7585e-01   2.2251e-01   3.1792e-02   3.0989e-06   2.8348e-01
#> 19:   -9.5251e-01   2.5163e+00   2.4397e+00   2.3496e+00   5.1257e+00   1.7843e-01   2.2460e-01   3.0542e-02   6.2674e-06   2.8348e-01
#> 20:   -9.5364e-01   2.5032e+00   2.4541e+00   2.3479e+00   5.1150e+00   1.8021e-01   2.3530e-01   2.9015e-02   1.0540e-05   2.8348e-01
#> 21:   -9.5359e-01   2.5003e+00   2.4552e+00   2.3497e+00   5.1319e+00   1.8306e-01   2.3107e-01   2.7854e-02   1.2877e-06   2.8254e-01
#> 22:   -9.4878e-01   2.4886e+00   2.4510e+00   2.3472e+00   5.1173e+00   1.7391e-01   2.4330e-01   2.9911e-02   6.0967e-06   2.7835e-01
#> 23:   -9.5369e-01   2.5096e+00   2.4534e+00   2.3427e+00   5.1017e+00   1.6682e-01   2.4347e-01   2.8416e-02   1.2565e-05   2.7998e-01
#> 24:   -9.6534e-01   2.5059e+00   2.4640e+00   2.3323e+00   5.1116e+00   1.6560e-01   2.3216e-01   2.9304e-02   2.0532e-06   2.8254e-01
#> 25:   -9.5958e-01   2.5084e+00   2.4783e+00   2.3550e+00   5.1184e+00   1.6499e-01   2.3364e-01   3.0317e-02   3.1478e-05   2.8416e-01
#> 26:   -9.5577e-01   2.5130e+00   2.4730e+00   2.3456e+00   5.1117e+00   1.6608e-01   2.4293e-01   2.9235e-02   1.2241e-05   2.8254e-01
#> 27:   -9.6301e-01   2.5041e+00   2.4693e+00   2.3497e+00   5.1149e+00   1.7536e-01   2.3839e-01   2.9245e-02   2.9506e-08   2.7540e-01
#> 28:   -9.5029e-01   2.5016e+00   2.4798e+00   2.3600e+00   5.1055e+00   1.7543e-01   2.5388e-01   2.8147e-02   4.5252e-05   2.7439e-01
#> 29:   -9.4803e-01   2.5004e+00   2.4772e+00   2.3729e+00   5.1014e+00   1.6666e-01   2.4979e-01   2.8638e-02   1.8533e-05   2.7601e-01
#> 30:   -9.5362e-01   2.5046e+00   2.4736e+00   2.3798e+00   5.0968e+00   1.7608e-01   2.4041e-01   3.0008e-02   7.5201e-06   2.7882e-01
#> 31:   -9.5938e-01   2.4961e+00   2.4673e+00   2.3791e+00   5.0908e+00   1.7867e-01   2.4567e-01   2.8893e-02   1.6619e-05   2.7835e-01
#> 32:   -9.4362e-01   2.5076e+00   2.4633e+00   2.3791e+00   5.1030e+00   1.7372e-01   2.3901e-01   2.9646e-02   8.8246e-06   2.7882e-01
#> 33:   -9.4002e-01   2.5037e+00   2.4667e+00   2.3617e+00   5.1006e+00   1.7786e-01   2.2806e-01   3.1795e-02   3.0762e-06   2.7824e-01
#> 34:   -9.5669e-01   2.5045e+00   2.4660e+00   2.3439e+00   5.1010e+00   1.9060e-01   2.2271e-01   3.0205e-02   1.8147e-05   2.7998e-01
#> 35:   -9.5415e-01   2.4994e+00   2.4701e+00   2.3419e+00   5.1040e+00   1.9108e-01   2.2731e-01   3.0294e-02   7.7691e-06   2.7882e-01
#> 36:   -9.6887e-01   2.5012e+00   2.4682e+00   2.3475e+00   5.0962e+00   1.8153e-01   2.3457e-01   3.0579e-02   1.6857e-05   2.7843e-01
#> 37:   -9.5124e-01   2.5131e+00   2.4609e+00   2.3253e+00   5.0958e+00   1.8062e-01   2.3247e-01   3.2804e-02   1.5101e-05   2.7955e-01
#> 38:   -9.5702e-01   2.5041e+00   2.4576e+00   2.3375e+00   5.0944e+00   1.8022e-01   2.4296e-01   3.1164e-02   6.4240e-05   2.7915e-01
#> 39:   -9.6491e-01   2.5186e+00   2.4568e+00   2.3302e+00   5.0980e+00   1.8438e-01   2.3081e-01   3.1940e-02   4.2358e-06   2.7779e-01
#> 40:   -9.6546e-01   2.5311e+00   2.4680e+00   2.3103e+00   5.0972e+00   1.8217e-01   2.3479e-01   3.2029e-02   2.8845e-09   2.8254e-01
#> 41:   -9.5879e-01   2.5221e+00   2.4726e+00   2.3352e+00   5.0950e+00   1.7974e-01   2.4979e-01   3.5433e-02   6.3491e-07   2.8348e-01
#> 42:   -9.5740e-01   2.5273e+00   2.4678e+00   2.3333e+00   5.0974e+00   1.8199e-01   2.5011e-01   3.6911e-02   2.1972e-06   2.7901e-01
#> 43:   -9.5194e-01   2.5238e+00   2.4696e+00   2.3256e+00   5.1035e+00   1.9027e-01   2.4936e-01   3.8768e-02   3.6204e-08   2.7781e-01
#> 44:   -9.5160e-01   2.5054e+00   2.4838e+00   2.3400e+00   5.0976e+00   1.9204e-01   2.4491e-01   3.6830e-02   4.3632e-06   2.7901e-01
#> 45:   -9.4440e-01   2.5166e+00   2.4937e+00   2.3521e+00   5.0868e+00   1.9095e-01   2.3390e-01   3.5167e-02   5.2895e-10   2.8254e-01
#> 46:   -9.3426e-01   2.5180e+00   2.4971e+00   2.3492e+00   5.0891e+00   2.0192e-01   2.4808e-01   3.5436e-02   4.3632e-06   2.8254e-01
#> 47:   -9.3798e-01   2.5074e+00   2.4991e+00   2.3365e+00   5.0833e+00   1.9182e-01   2.4568e-01   3.5115e-02   3.0306e-05   2.7884e-01
#> 48:   -9.4402e-01   2.5165e+00   2.4930e+00   2.3317e+00   5.0810e+00   1.9061e-01   2.4602e-01   3.3360e-02   1.6821e-05   2.8168e-01
#> 49:   -9.4545e-01   2.5186e+00   2.4857e+00   2.3441e+00   5.0857e+00   1.8438e-01   2.4046e-01   3.3277e-02   1.9553e-05   2.8362e-01
#> 50:   -9.4369e-01   2.5145e+00   2.4913e+00   2.3561e+00   5.0891e+00   1.8635e-01   2.5513e-01   3.1926e-02   1.4782e-05   2.8442e-01
#> 51:   -9.4531e-01   2.5145e+00   2.4882e+00   2.3366e+00   5.0860e+00   1.8951e-01   2.5422e-01   3.3760e-02   2.1048e-05   2.8348e-01
#> 52:   -9.4611e-01   2.5084e+00   2.4884e+00   2.3418e+00   5.0884e+00   1.9487e-01   2.4842e-01   3.2398e-02   6.0489e-06   2.7882e-01
#> 53:   -9.4228e-01   2.5096e+00   2.4949e+00   2.3431e+00   5.0825e+00   1.9639e-01   2.3600e-01   3.2174e-02   1.5444e-06   2.7824e-01
#> 54:   -9.5901e-01   2.5026e+00   2.4879e+00   2.3327e+00   5.0808e+00   1.8795e-01   2.2861e-01   3.4894e-02   6.6416e-06   2.7835e-01
#> 55:   -9.6669e-01   2.5015e+00   2.4823e+00   2.3386e+00   5.0821e+00   1.8289e-01   2.3312e-01   3.3802e-02   1.1811e-05   2.7998e-01
#> 56:   -9.6057e-01   2.5027e+00   2.4885e+00   2.3420e+00   5.0789e+00   1.8728e-01   2.3949e-01   3.6522e-02   1.7471e-05   2.8348e-01
#> 57:   -9.5906e-01   2.5033e+00   2.4892e+00   2.3332e+00   5.0811e+00   1.9160e-01   2.3427e-01   3.4695e-02   4.2767e-06   2.8254e-01
#> 58:   -9.5758e-01   2.5049e+00   2.4984e+00   2.3314e+00   5.0803e+00   1.8202e-01   2.4742e-01   4.0282e-02   1.9175e-09   2.8254e-01
#> 59:   -9.5879e-01   2.4992e+00   2.5084e+00   2.3343e+00   5.0840e+00   1.8644e-01   2.4852e-01   4.3005e-02   6.1920e-07   2.8348e-01
#> 60:   -9.5291e-01   2.5132e+00   2.4982e+00   2.3365e+00   5.0764e+00   1.9444e-01   2.3665e-01   4.1497e-02   2.3409e-06   2.8348e-01
#> 61:   -9.5579e-01   2.5064e+00   2.4959e+00   2.3239e+00   5.0782e+00   1.8938e-01   2.4549e-01   3.9422e-02   5.1670e-06   2.8348e-01
#> 62:   -9.5591e-01   2.5078e+00   2.4876e+00   2.3418e+00   5.0776e+00   2.0031e-01   2.3721e-01   3.8612e-02   2.8762e-06   2.8442e-01
#> 63:   -9.4455e-01   2.5196e+00   2.4821e+00   2.3455e+00   5.0773e+00   1.9030e-01   2.2716e-01   3.7404e-02   1.2516e-06   2.8442e-01
#> 64:   -9.3304e-01   2.5245e+00   2.4864e+00   2.3441e+00   5.0802e+00   1.8787e-01   2.3848e-01   3.6767e-02   9.8614e-07   2.8254e-01
#> 65:   -9.4244e-01   2.5123e+00   2.4950e+00   2.3411e+00   5.0728e+00   1.9725e-01   2.3651e-01   3.4928e-02   1.2516e-06   2.8254e-01
#> 66:   -9.5349e-01   2.5164e+00   2.4880e+00   2.3467e+00   5.0722e+00   1.9248e-01   2.3882e-01   3.3779e-02   6.0180e-06   2.7835e-01
#> 67:   -9.4900e-01   2.5191e+00   2.4949e+00   2.3441e+00   5.0780e+00   1.9965e-01   2.3928e-01   3.3033e-02   4.3325e-05   2.7915e-01
#> 68:   -9.5623e-01   2.5117e+00   2.4888e+00   2.3402e+00   5.0773e+00   1.9406e-01   2.2899e-01   3.2667e-02   2.2070e-05   2.8169e-01
#> 69:   -9.4447e-01   2.5051e+00   2.4791e+00   2.3460e+00   5.0784e+00   1.9430e-01   2.3785e-01   3.1609e-02   9.4096e-06   2.7768e-01
#> 70:   -9.6575e-01   2.5044e+00   2.4722e+00   2.3391e+00   5.0857e+00   1.8518e-01   2.3170e-01   3.1167e-02   3.4255e-06   2.7824e-01
#> 71:   -9.6771e-01   2.5095e+00   2.4661e+00   2.3363e+00   5.0823e+00   1.8424e-01   2.3010e-01   3.0399e-02   1.0145e-05   2.7835e-01
#> 72:   -9.7265e-01   2.5093e+00   2.4639e+00   2.3263e+00   5.0839e+00   1.8136e-01   2.2713e-01   3.2166e-02   1.1521e-06   2.8254e-01
#> 73:   -9.7943e-01   2.5037e+00   2.4565e+00   2.3056e+00   5.0898e+00   1.8677e-01   2.2277e-01   3.0558e-02   2.0541e-08   2.7824e-01
#> 74:   -9.8796e-01   2.4943e+00   2.4633e+00   2.3086e+00   5.0937e+00   1.9210e-01   2.2589e-01   2.9030e-02   3.8750e-06   2.8254e-01
#> 75:   -9.7857e-01   2.5076e+00   2.4695e+00   2.2978e+00   5.0979e+00   1.8753e-01   2.2413e-01   2.7578e-02   5.6520e-07   2.7824e-01
#> 76:   -9.7370e-01   2.5104e+00   2.4641e+00   2.3152e+00   5.1014e+00   2.0451e-01   2.2322e-01   2.9854e-02   2.3822e-05   2.7915e-01
#> 77:   -9.8281e-01   2.5102e+00   2.4588e+00   2.3184e+00   5.1030e+00   1.9978e-01   2.3674e-01   2.8493e-02   3.1474e-05   2.8159e-01
#> 78:   -9.7312e-01   2.5134e+00   2.4564e+00   2.3267e+00   5.1086e+00   2.0945e-01   2.4662e-01   2.7473e-02   1.2239e-05   2.8254e-01
#> 79:   -9.8009e-01   2.4877e+00   2.4634e+00   2.3141e+00   5.1148e+00   1.9898e-01   2.5817e-01   2.7782e-02   1.7990e-05   2.8348e-01
#> 80:   -9.8005e-01   2.4961e+00   2.4611e+00   2.2923e+00   5.1116e+00   1.9030e-01   2.4527e-01   2.9435e-02   7.8725e-06   2.7882e-01
#> 81:   -9.7006e-01   2.5121e+00   2.4713e+00   2.2971e+00   5.1076e+00   2.0334e-01   2.4646e-01   2.8522e-02   4.8160e-07   2.8254e-01
#> 82:   -9.6323e-01   2.5243e+00   2.4838e+00   2.2949e+00   5.1041e+00   2.0951e-01   2.3414e-01   2.7096e-02   2.0652e-06   2.8348e-01
#> 83:   -9.5266e-01   2.5190e+00   2.4818e+00   2.3002e+00   5.1021e+00   1.9913e-01   2.4470e-01   2.7203e-02   4.5527e-07   2.8254e-01
#> 84:   -9.4837e-01   2.5134e+00   2.4902e+00   2.3064e+00   5.1048e+00   1.9138e-01   2.4863e-01   2.5843e-02   2.3076e-05   2.7915e-01
#> 85:   -9.5896e-01   2.5099e+00   2.4912e+00   2.3012e+00   5.1048e+00   1.9135e-01   2.5087e-01   2.6378e-02   7.2466e-06   2.8254e-01
#> 86:   -9.6613e-01   2.5093e+00   2.4907e+00   2.3028e+00   5.1051e+00   1.9693e-01   2.4462e-01   2.6010e-02   3.3656e-07   2.8254e-01
#> 87:   -9.6991e-01   2.4955e+00   2.4871e+00   2.3091e+00   5.1060e+00   2.0761e-01   2.3866e-01   2.5765e-02   8.0286e-06   2.7899e-01
#> 88:   -9.6264e-01   2.5246e+00   2.4860e+00   2.3093e+00   5.1085e+00   2.0865e-01   2.4291e-01   2.7480e-02   1.2792e-05   2.8348e-01
#> 89:   -9.5299e-01   2.5310e+00   2.4775e+00   2.3201e+00   5.1114e+00   2.0512e-01   2.3789e-01   3.0020e-02   1.8660e-05   2.8348e-01
#> 90:   -9.5705e-01   2.5290e+00   2.4691e+00   2.3126e+00   5.1109e+00   2.0599e-01   2.4543e-01   2.9319e-02   1.7669e-05   2.8362e-01
#> 91:   -9.5956e-01   2.5221e+00   2.4724e+00   2.3004e+00   5.1127e+00   2.2355e-01   2.3316e-01   3.0438e-02   4.3751e-06   2.8254e-01
#> 92:   -9.6734e-01   2.5146e+00   2.4669e+00   2.3064e+00   5.1153e+00   2.1922e-01   2.2313e-01   3.2096e-02   1.1738e-05   2.7835e-01
#> 93:   -9.6385e-01   2.5175e+00   2.4679e+00   2.3046e+00   5.1152e+00   2.0826e-01   2.2732e-01   3.0945e-02   1.7272e-06   2.8254e-01
#> 94:   -9.6451e-01   2.5151e+00   2.4717e+00   2.3037e+00   5.1111e+00   2.0130e-01   2.3722e-01   3.0264e-02   9.2607e-05   2.7481e-01
#> 95:   -9.7504e-01   2.5142e+00   2.4785e+00   2.2874e+00   5.1148e+00   1.9756e-01   2.3747e-01   3.1093e-02   2.0658e-06   2.7907e-01
#> 96:   -9.7778e-01   2.5221e+00   2.4688e+00   2.3001e+00   5.1176e+00   1.9142e-01   2.2560e-01   2.9590e-02   7.6822e-06   2.7835e-01
#> 97:   -9.6552e-01   2.5164e+00   2.4746e+00   2.3025e+00   5.1175e+00   1.8185e-01   2.4977e-01   3.0706e-02   1.4574e-05   2.7640e-01
#> 98:   -9.6079e-01   2.5124e+00   2.4705e+00   2.3129e+00   5.1169e+00   1.8137e-01   2.4596e-01   3.0899e-02   2.9098e-06   2.8254e-01
#> 99:   -9.7517e-01   2.5204e+00   2.4688e+00   2.3199e+00   5.1166e+00   1.8229e-01   2.4092e-01   2.9354e-02   1.2739e-06   2.8442e-01
#> 100:   -9.7988e-01   2.5156e+00   2.4706e+00   2.3111e+00   5.1131e+00   1.8669e-01   2.3334e-01   2.8833e-02   3.5035e-06   2.8348e-01
#> 101:   -9.7043e-01   2.5216e+00   2.4669e+00   2.3264e+00   5.1131e+00   1.8851e-01   2.3569e-01   2.9169e-02   6.8375e-06   2.8348e-01
#> 102:   -9.7269e-01   2.5061e+00   2.4714e+00   2.3209e+00   5.1081e+00   1.8471e-01   2.4833e-01   3.0472e-02   2.5306e-07   2.8254e-01
#> 103:   -9.7223e-01   2.5133e+00   2.4697e+00   2.3212e+00   5.1103e+00   1.9485e-01   2.5420e-01   2.8948e-02   3.3761e-06   2.7835e-01
#> 104:   -9.6881e-01   2.5104e+00   2.4715e+00   2.3313e+00   5.1046e+00   2.0139e-01   2.4149e-01   2.8451e-02   7.5288e-08   2.8254e-01
#> 105:   -9.8253e-01   2.5018e+00   2.4654e+00   2.3274e+00   5.1069e+00   2.0531e-01   2.3881e-01   3.1401e-02   4.6643e-05   2.7439e-01
#> 106:   -9.6414e-01   2.5228e+00   2.4616e+00   2.3252e+00   5.1096e+00   2.0516e-01   2.4127e-01   3.1869e-02   2.9439e-05   2.8168e-01
#> 107:   -9.7587e-01   2.4982e+00   2.4689e+00   2.3222e+00   5.1069e+00   1.9490e-01   2.3503e-01   3.0627e-02   5.7990e-06   2.7750e-01
#> 108:   -9.9012e-01   2.4960e+00   2.4600e+00   2.3197e+00   5.1088e+00   1.8740e-01   2.3935e-01   3.1135e-02   1.4006e-05   2.7835e-01
#> 109:   -9.8802e-01   2.4956e+00   2.4605e+00   2.3293e+00   5.1093e+00   1.8583e-01   2.4427e-01   2.9639e-02   2.0121e-05   2.8348e-01
#> 110:   -9.7819e-01   2.5015e+00   2.4591e+00   2.3482e+00   5.1108e+00   1.7903e-01   2.5955e-01   2.9641e-02   5.6348e-06   2.8254e-01
#> 111:   -9.6058e-01   2.5347e+00   2.4499e+00   2.3477e+00   5.1088e+00   1.8233e-01   2.4658e-01   3.1169e-02   9.7149e-06   2.8348e-01
#> 112:   -9.6220e-01   2.5289e+00   2.4537e+00   2.3389e+00   5.1106e+00   1.8000e-01   2.3425e-01   3.0134e-02   1.0101e-06   2.8254e-01
#> 113:   -9.6952e-01   2.5029e+00   2.4622e+00   2.3346e+00   5.1125e+00   1.9328e-01   2.3700e-01   2.8628e-02   1.2796e-05   2.7569e-01
#> 114:   -9.8004e-01   2.5105e+00   2.4537e+00   2.3224e+00   5.1131e+00   1.9287e-01   2.2918e-01   2.7883e-02   5.5717e-06   2.7824e-01
#> 115:   -9.8556e-01   2.5089e+00   2.4618e+00   2.3323e+00   5.1157e+00   1.9543e-01   2.3729e-01   3.1366e-02   6.1821e-08   2.8254e-01
#> 116:   -9.7133e-01   2.5166e+00   2.4592e+00   2.3318e+00   5.1175e+00   2.0151e-01   2.3474e-01   3.1208e-02   1.0793e-07   2.8442e-01
#> 117:   -9.8212e-01   2.5049e+00   2.4707e+00   2.3288e+00   5.1144e+00   1.9144e-01   2.3395e-01   3.0545e-02   1.1484e-06   2.8348e-01
#> 118:   -9.6353e-01   2.5106e+00   2.4769e+00   2.3326e+00   5.1124e+00   2.0446e-01   2.4250e-01   2.9409e-02   5.7889e-06   2.7835e-01
#> 119:   -9.6063e-01   2.4996e+00   2.4760e+00   2.3308e+00   5.1112e+00   1.9832e-01   2.7157e-01   2.7938e-02   1.3991e-05   2.7835e-01
#> 120:   -9.5408e-01   2.5104e+00   2.4738e+00   2.3318e+00   5.1133e+00   2.0448e-01   2.5842e-01   2.6541e-02   2.5753e-05   2.7835e-01
#> 121:   -9.7202e-01   2.4908e+00   2.4649e+00   2.3237e+00   5.1039e+00   1.9652e-01   2.6130e-01   2.6122e-02   7.0375e-06   2.7601e-01
#> 122:   -9.6245e-01   2.4981e+00   2.4702e+00   2.3207e+00   5.1028e+00   1.8704e-01   2.6243e-01   2.5917e-02   1.5770e-05   2.7843e-01
#> 123:   -9.6954e-01   2.4817e+00   2.4786e+00   2.3283e+00   5.0990e+00   1.9184e-01   2.6299e-01   2.6967e-02   6.7581e-06   2.7637e-01
#> 124:   -9.5382e-01   2.5111e+00   2.4827e+00   2.3347e+00   5.0981e+00   1.8589e-01   2.5324e-01   2.5619e-02   1.9125e-06   2.7824e-01
#> 125:   -9.4191e-01   2.5278e+00   2.4898e+00   2.3350e+00   5.0994e+00   1.9656e-01   2.4589e-01   2.4540e-02   4.5201e-06   2.8348e-01
#> 126:   -9.5051e-01   2.5248e+00   2.4933e+00   2.3361e+00   5.1009e+00   1.8673e-01   2.5446e-01   2.5021e-02   2.3990e-06   2.8442e-01
#> 127:   -9.5538e-01   2.5245e+00   2.4879e+00   2.3234e+00   5.1021e+00   1.8906e-01   2.4174e-01   2.4489e-02   5.2532e-06   2.8348e-01
#> 128:   -9.5905e-01   2.5146e+00   2.4887e+00   2.3200e+00   5.0996e+00   1.9823e-01   2.4630e-01   2.4348e-02   3.2459e-08   2.8254e-01
#> 129:   -9.6742e-01   2.5074e+00   2.4960e+00   2.3165e+00   5.0987e+00   1.8832e-01   2.4654e-01   2.3475e-02   8.5242e-07   2.8348e-01
#> 130:   -9.6684e-01   2.5048e+00   2.4894e+00   2.3251e+00   5.0962e+00   1.9651e-01   2.5057e-01   2.2870e-02   1.0783e-05   2.8038e-01
#> 131:   -9.7427e-01   2.5005e+00   2.4838e+00   2.3245e+00   5.1034e+00   2.0478e-01   2.6150e-01   2.1727e-02   7.3259e-06   2.8442e-01
#> 132:   -9.6280e-01   2.5109e+00   2.4863e+00   2.3081e+00   5.1033e+00   2.0920e-01   2.5910e-01   2.1044e-02   1.1901e-05   2.8348e-01
#> 133:   -9.6819e-01   2.5167e+00   2.4922e+00   2.3177e+00   5.1034e+00   1.9906e-01   2.4614e-01   2.0042e-02   8.2517e-06   2.8442e-01
#> 134:   -9.5662e-01   2.5304e+00   2.4921e+00   2.3088e+00   5.1008e+00   1.9421e-01   2.4432e-01   2.1111e-02   5.2689e-06   2.8442e-01
#> 135:   -9.6499e-01   2.5236e+00   2.4853e+00   2.3159e+00   5.1004e+00   2.0177e-01   2.3211e-01   2.1244e-02   2.9524e-06   2.8442e-01
#> 136:   -9.5709e-01   2.5121e+00   2.4751e+00   2.3241e+00   5.0987e+00   1.9168e-01   2.3956e-01   2.1482e-02   1.5489e-07   2.8254e-01
#> 137:   -9.5919e-01   2.5048e+00   2.4809e+00   2.3365e+00   5.0990e+00   1.9675e-01   2.3971e-01   2.1956e-02   3.3708e-08   2.8442e-01
#> 138:   -9.7790e-01   2.4922e+00   2.4800e+00   2.3394e+00   5.1001e+00   1.9858e-01   2.4163e-01   2.0858e-02   1.5489e-07   2.8442e-01
#> 139:   -9.5994e-01   2.5042e+00   2.4763e+00   2.3517e+00   5.1008e+00   1.9645e-01   2.3716e-01   2.3912e-02   3.3708e-08   2.8442e-01
#> 140:   -9.4674e-01   2.5098e+00   2.4737e+00   2.3444e+00   5.1029e+00   2.0713e-01   2.4815e-01   2.3418e-02   8.5877e-07   2.8348e-01
#> 141:   -9.5980e-01   2.5032e+00   2.4718e+00   2.3441e+00   5.1039e+00   2.0932e-01   2.3574e-01   2.4523e-02   2.7882e-06   2.8348e-01
#> 142:   -9.6869e-01   2.5174e+00   2.4665e+00   2.3340e+00   5.1049e+00   2.1113e-01   2.2859e-01   2.3640e-02   1.6244e-05   2.8038e-01
#> 143:   -9.6231e-01   2.5057e+00   2.4705e+00   2.3320e+00   5.1064e+00   2.0647e-01   2.3271e-01   2.3103e-02   2.0186e-05   2.8362e-01
#> 144:   -9.7801e-01   2.5052e+00   2.4702e+00   2.3409e+00   5.1091e+00   1.9614e-01   2.3617e-01   2.3410e-02   5.6693e-06   2.8254e-01
#> 145:   -9.7586e-01   2.5113e+00   2.4629e+00   2.3359e+00   5.1128e+00   1.9227e-01   2.3266e-01   2.2239e-02   1.3557e-06   2.7824e-01
#> 146:   -9.7275e-01   2.5193e+00   2.4665e+00   2.3428e+00   5.1155e+00   1.9420e-01   2.3606e-01   2.1661e-02   1.1680e-05   2.7899e-01
#> 147:   -9.7189e-01   2.4946e+00   2.4624e+00   2.3310e+00   5.1136e+00   1.8449e-01   2.4900e-01   2.3105e-02   4.8443e-06   2.7824e-01
#> 148:   -9.6502e-01   2.5076e+00   2.4556e+00   2.3227e+00   5.1144e+00   1.8849e-01   2.3655e-01   2.3595e-02   7.9483e-09   2.8254e-01
#> 149:   -9.6981e-01   2.4978e+00   2.4600e+00   2.3372e+00   5.1124e+00   1.9272e-01   2.5163e-01   2.3701e-02   2.3815e-07   2.8442e-01
#> 150:   -9.6764e-01   2.5079e+00   2.4651e+00   2.3158e+00   5.1113e+00   2.0001e-01   2.4624e-01   2.5363e-02   7.5150e-06   2.7899e-01
#> 151:   -9.8433e-01   2.5017e+00   2.4638e+00   2.3123e+00   5.1114e+00   2.0052e-01   2.6041e-01   2.5146e-02   1.8246e-05   2.7913e-01
#> 152:   -9.8163e-01   2.4908e+00   2.4671e+00   2.3090e+00   5.1116e+00   1.9681e-01   2.5932e-01   2.4889e-02   4.6646e-06   2.8254e-01
#> 153:   -9.8093e-01   2.5098e+00   2.4606e+00   2.3153e+00   5.1110e+00   2.0217e-01   2.3663e-01   2.5909e-02   8.4267e-06   2.8348e-01
#> 154:   -9.7617e-01   2.5067e+00   2.4642e+00   2.3128e+00   5.1103e+00   1.9954e-01   2.3187e-01   2.3705e-02   5.4089e-06   2.8442e-01
#> 155:   -9.8065e-01   2.5096e+00   2.4639e+00   2.3181e+00   5.1126e+00   1.9628e-01   2.3053e-01   2.3456e-02   3.0574e-06   2.8442e-01
#> 156:   -9.7964e-01   2.5271e+00   2.4695e+00   2.3147e+00   5.1165e+00   1.9761e-01   2.2564e-01   2.3612e-02   1.3721e-06   2.8442e-01
#> 157:   -9.6372e-01   2.5292e+00   2.4700e+00   2.3157e+00   5.1147e+00   2.0328e-01   2.3411e-01   2.3319e-02   3.5310e-07   2.8442e-01
#> 158:   -9.4938e-01   2.5174e+00   2.4662e+00   2.3151e+00   5.1136e+00   2.1419e-01   2.3071e-01   2.4284e-02   1.7884e-06   2.8348e-01
#> 159:   -9.8796e-01   2.5071e+00   2.4701e+00   2.3120e+00   5.1126e+00   1.9682e-01   2.4104e-01   2.2045e-02   5.9984e-07   2.8254e-01
#> 160:   -9.5915e-01   2.5146e+00   2.4800e+00   2.3129e+00   5.1158e+00   1.9964e-01   2.4005e-01   2.2293e-02   2.3031e-06   2.8348e-01
#> 161:   -9.6488e-01   2.5237e+00   2.4870e+00   2.2996e+00   5.1136e+00   1.8591e-01   2.3348e-01   2.1837e-02   5.1108e-06   2.8348e-01
#> 162:   -9.7458e-01   2.5160e+00   2.4812e+00   2.2956e+00   5.1136e+00   1.8571e-01   2.4379e-01   2.2799e-02   9.0228e-06   2.8348e-01
#> 163:   -9.7704e-01   2.4970e+00   2.4893e+00   2.2948e+00   5.1168e+00   1.8462e-01   2.3462e-01   2.3311e-02   7.9564e-07   2.8254e-01
#> 164:   -9.7091e-01   2.4902e+00   2.4935e+00   2.3028e+00   5.1116e+00   1.9619e-01   2.2767e-01   2.2909e-02   2.6735e-06   2.8348e-01
#> 165:   -9.7287e-01   2.5011e+00   2.4999e+00   2.2942e+00   5.1128e+00   1.9325e-01   2.3737e-01   2.2619e-02   5.6558e-06   2.8348e-01
#> 166:   -9.8522e-01   2.4914e+00   2.4912e+00   2.2987e+00   5.1129e+00   2.0472e-01   2.4246e-01   2.2763e-02   9.7425e-06   2.8348e-01
#> 167:   -9.6985e-01   2.5153e+00   2.4917e+00   2.3086e+00   5.1100e+00   2.0247e-01   2.4180e-01   2.4819e-02   1.0190e-06   2.8254e-01
#> 168:   -9.7776e-01   2.5011e+00   2.4854e+00   2.3116e+00   5.1102e+00   1.8350e-01   2.4761e-01   2.4611e-02   1.2764e-05   2.7569e-01
#> 169:   -9.7912e-01   2.5096e+00   2.4745e+00   2.3117e+00   5.1095e+00   1.9116e-01   2.5638e-01   2.3317e-02   2.1343e-06   2.8254e-01
#> 170:   -9.8464e-01   2.5158e+00   2.4708e+00   2.3057e+00   5.1110e+00   1.9389e-01   2.5912e-01   2.2329e-02   4.8577e-06   2.8348e-01
#> 171:   -9.8356e-01   2.5006e+00   2.4761e+00   2.3092e+00   5.1069e+00   1.9677e-01   2.5386e-01   2.1445e-02   8.6855e-06   2.8348e-01
#> 172:   -9.8086e-01   2.5051e+00   2.4707e+00   2.3043e+00   5.1023e+00   2.0605e-01   2.4890e-01   2.1056e-02   5.6167e-06   2.8442e-01
#> 173:   -9.7203e-01   2.5122e+00   2.4717e+00   2.3188e+00   5.1042e+00   2.0366e-01   2.5693e-01   2.1167e-02   3.2141e-06   2.8442e-01
#> 174:   -9.6701e-01   2.5152e+00   2.4732e+00   2.3190e+00   5.1014e+00   2.1516e-01   2.6085e-01   2.1176e-02   1.7250e-05   2.8038e-01
#> 175:   -9.6766e-01   2.5174e+00   2.4711e+00   2.3187e+00   5.0978e+00   1.9822e-01   2.6775e-01   2.1190e-02   1.9096e-05   2.8362e-01
#> 176:   -9.8052e-01   2.5110e+00   2.4670e+00   2.3173e+00   5.0980e+00   2.0770e-01   2.4249e-01   2.0757e-02   1.7250e-05   2.8362e-01
#> 177:   -9.5237e-01   2.5185e+00   2.4722e+00   2.3223e+00   5.0943e+00   2.0615e-01   2.3228e-01   2.0482e-02   8.3749e-06   2.7882e-01
#> 178:   -9.6042e-01   2.5141e+00   2.4645e+00   2.3224e+00   5.0939e+00   2.0720e-01   2.3728e-01   1.8517e-02   1.3228e-05   2.8348e-01
#> 179:   -9.7321e-01   2.4901e+00   2.4727e+00   2.3111e+00   5.0926e+00   2.0050e-01   2.4986e-01   2.0008e-02   2.3263e-06   2.8254e-01
#> 180:   -9.8528e-01   2.4986e+00   2.4755e+00   2.3188e+00   5.0903e+00   2.0715e-01   2.4433e-01   1.9733e-02   5.1453e-06   2.8348e-01
#> 181:   -9.6391e-01   2.5084e+00   2.4767e+00   2.3138e+00   5.0916e+00   2.0598e-01   2.3958e-01   1.9846e-02   9.0687e-06   2.8348e-01
#> 182:   -9.6885e-01   2.5240e+00   2.4739e+00   2.3152e+00   5.0865e+00   2.0685e-01   2.3582e-01   2.1611e-02   5.9257e-06   2.8442e-01
#> 183:   -9.7617e-01   2.5000e+00   2.4741e+00   2.3185e+00   5.0854e+00   1.9928e-01   2.5210e-01   2.0002e-02   1.0096e-05   2.8348e-01
#> 184:   -9.7072e-01   2.5074e+00   2.4708e+00   2.3208e+00   5.0875e+00   1.9601e-01   2.3998e-01   2.1151e-02   1.1354e-06   2.8254e-01
#> 185:   -9.7342e-01   2.5178e+00   2.4678e+00   2.3132e+00   5.0878e+00   2.0114e-01   2.4644e-01   2.1207e-02   3.2712e-06   2.8348e-01
#> 186:   -9.6813e-01   2.5037e+00   2.4697e+00   2.3076e+00   5.0870e+00   2.1136e-01   2.4266e-01   2.1537e-02   6.5115e-06   2.8348e-01
#> 187:   -9.7997e-01   2.5157e+00   2.4765e+00   2.3001e+00   5.0879e+00   2.1129e-01   2.4148e-01   1.9870e-02   1.0856e-05   2.8348e-01
#> 188:   -9.7410e-01   2.5054e+00   2.4787e+00   2.3065e+00   5.0906e+00   1.9851e-01   2.5098e-01   1.9012e-02   1.6305e-05   2.8348e-01
#> 189:   -9.7773e-01   2.5099e+00   2.4757e+00   2.3147e+00   5.0923e+00   2.1070e-01   2.4044e-01   1.9091e-02   3.7101e-06   2.8254e-01
#> 190:   -9.7552e-01   2.5049e+00   2.4779e+00   2.3113e+00   5.0956e+00   1.9586e-01   2.5914e-01   1.7626e-02   3.4472e-08   2.8254e-01
#> 191:   -9.6561e-01   2.5024e+00   2.4835e+00   2.3098e+00   5.0963e+00   2.1089e-01   2.4959e-01   1.8945e-02   8.6261e-07   2.8348e-01
#> 192:   -9.6010e-01   2.5180e+00   2.4809e+00   2.3115e+00   5.0997e+00   1.9971e-01   2.4927e-01   1.7939e-02   1.3996e-06   2.8254e-01
#> 193:   -9.6141e-01   2.5277e+00   2.4781e+00   2.3039e+00   5.0992e+00   1.9662e-01   2.2842e-01   1.7991e-02   3.7101e-06   2.8348e-01
#> 194:   -9.5984e-01   2.5370e+00   2.4731e+00   2.3132e+00   5.1005e+00   2.0003e-01   2.2742e-01   1.9091e-02   7.1249e-06   2.8348e-01
#> 195:   -9.6341e-01   2.5178e+00   2.4796e+00   2.3172e+00   5.1022e+00   2.0382e-01   2.3623e-01   1.9122e-02   1.1644e-05   2.8348e-01
#> 196:   -9.6864e-01   2.5073e+00   2.4751e+00   2.3204e+00   5.0984e+00   1.9312e-01   2.3917e-01   2.0426e-02   1.7268e-05   2.8348e-01
#> 197:   -9.7435e-01   2.5081e+00   2.4759e+00   2.3160e+00   5.1032e+00   1.9423e-01   2.4769e-01   2.0846e-02   4.1765e-06   2.8254e-01
#> 198:   -9.7490e-01   2.4928e+00   2.4763e+00   2.3078e+00   5.1040e+00   2.0495e-01   2.5522e-01   2.1113e-02   4.6475e-09   2.8254e-01
#> 199:   -9.6222e-01   2.5089e+00   2.4743e+00   2.3148e+00   5.1010e+00   2.0073e-01   2.4546e-01   1.9337e-02   1.9672e-06   2.7835e-01
#> 200:   -9.6334e-01   2.5205e+00   2.4756e+00   2.2896e+00   5.1036e+00   2.1490e-01   2.3006e-01   2.0596e-02   4.6038e-06   2.8348e-01
#> 201:   -9.5990e-01   2.5225e+00   2.4748e+00   2.2925e+00   5.1043e+00   2.1052e-01   2.2776e-01   2.1024e-02   4.1565e-06   2.8152e-01
#> 202:   -9.5967e-01   2.5208e+00   2.4732e+00   2.2975e+00   5.1017e+00   2.0515e-01   2.2930e-01   2.1130e-02   3.8740e-06   2.8086e-01
#> 203:   -9.5772e-01   2.5234e+00   2.4724e+00   2.2978e+00   5.1013e+00   2.0351e-01   2.3081e-01   2.1364e-02   1.2200e-05   2.8043e-01
#> 204:   -9.5816e-01   2.5229e+00   2.4745e+00   2.2951e+00   5.1013e+00   2.0344e-01   2.3294e-01   2.1485e-02   1.0141e-05   2.8085e-01
#> 205:   -9.5929e-01   2.5215e+00   2.4743e+00   2.2948e+00   5.1010e+00   1.9985e-01   2.3516e-01   2.1725e-02   8.6427e-06   2.8114e-01
#> 206:   -9.6107e-01   2.5216e+00   2.4758e+00   2.2960e+00   5.1007e+00   1.9825e-01   2.3464e-01   2.1973e-02   8.2055e-06   2.8160e-01
#> 207:   -9.6210e-01   2.5200e+00   2.4755e+00   2.2953e+00   5.1001e+00   1.9776e-01   2.3552e-01   2.2154e-02   7.2507e-06   2.8172e-01
#> 208:   -9.6239e-01   2.5195e+00   2.4746e+00   2.2959e+00   5.1004e+00   1.9815e-01   2.3602e-01   2.2689e-02   6.4825e-06   2.8181e-01
#> 209:   -9.6279e-01   2.5184e+00   2.4748e+00   2.2959e+00   5.1003e+00   1.9771e-01   2.3601e-01   2.3050e-02   5.8531e-06   2.8189e-01
#> 210:   -9.6377e-01   2.5177e+00   2.4742e+00   2.2955e+00   5.1004e+00   1.9817e-01   2.3649e-01   2.3154e-02   5.4525e-06   2.8156e-01
#> 211:   -9.6386e-01   2.5176e+00   2.4737e+00   2.2966e+00   5.1007e+00   1.9760e-01   2.3745e-01   2.3056e-02   5.0023e-06   2.8164e-01
#> 212:   -9.6344e-01   2.5174e+00   2.4733e+00   2.2969e+00   5.1007e+00   1.9734e-01   2.3741e-01   2.2957e-02   4.6187e-06   2.8171e-01
#> 213:   -9.6449e-01   2.5173e+00   2.4725e+00   2.2971e+00   5.1006e+00   1.9614e-01   2.3870e-01   2.2891e-02   4.2889e-06   2.8177e-01
#> 214:   -9.6548e-01   2.5168e+00   2.4719e+00   2.2977e+00   5.1005e+00   1.9507e-01   2.3898e-01   2.2884e-02   4.0281e-06   2.8176e-01
#> 215:   -9.6594e-01   2.5160e+00   2.4714e+00   2.2985e+00   5.1001e+00   1.9472e-01   2.3936e-01   2.2893e-02   3.8154e-06   2.8154e-01
#> 216:   -9.6620e-01   2.5154e+00   2.4711e+00   2.2984e+00   5.1001e+00   1.9475e-01   2.3940e-01   2.2937e-02   4.0186e-06   2.8165e-01
#> 217:   -9.6705e-01   2.5136e+00   2.4707e+00   2.2978e+00   5.1000e+00   1.9502e-01   2.4028e-01   2.3008e-02   3.7960e-06   2.8170e-01
#> 218:   -9.6770e-01   2.5127e+00   2.4703e+00   2.2972e+00   5.1002e+00   1.9510e-01   2.4071e-01   2.3029e-02   3.6951e-06   2.8185e-01
#> 219:   -9.6771e-01   2.5114e+00   2.4702e+00   2.2982e+00   5.1000e+00   1.9477e-01   2.4136e-01   2.3055e-02   7.5741e-06   2.8149e-01
#> 220:   -9.6750e-01   2.5114e+00   2.4698e+00   2.2988e+00   5.1002e+00   1.9452e-01   2.4169e-01   2.3037e-02   7.7952e-06   2.8159e-01
#> 221:   -9.6779e-01   2.5114e+00   2.4692e+00   2.2984e+00   5.1002e+00   1.9440e-01   2.4202e-01   2.2963e-02   7.4619e-06   2.8163e-01
#> 222:   -9.6789e-01   2.5110e+00   2.4691e+00   2.2987e+00   5.1005e+00   1.9480e-01   2.4281e-01   2.2908e-02   7.2372e-06   2.8148e-01
#> 223:   -9.6848e-01   2.5108e+00   2.4688e+00   2.2986e+00   5.1004e+00   1.9490e-01   2.4318e-01   2.2852e-02   7.4268e-06   2.8157e-01
#> 224:   -9.6942e-01   2.5107e+00   2.4683e+00   2.2990e+00   5.1004e+00   1.9500e-01   2.4344e-01   2.2858e-02   8.1644e-06   2.8152e-01
#> 225:   -9.6981e-01   2.5107e+00   2.4679e+00   2.2988e+00   5.1005e+00   1.9536e-01   2.4374e-01   2.2893e-02   8.0503e-06   2.8163e-01
#> 226:   -9.7029e-01   2.5109e+00   2.4674e+00   2.2984e+00   5.1003e+00   1.9550e-01   2.4408e-01   2.2859e-02   8.3967e-06   2.8151e-01
#> 227:   -9.7069e-01   2.5116e+00   2.4672e+00   2.2979e+00   5.1003e+00   1.9567e-01   2.4389e-01   2.2821e-02   8.5702e-06   2.8158e-01
#> 228:   -9.7109e-01   2.5120e+00   2.4667e+00   2.2973e+00   5.1003e+00   1.9599e-01   2.4362e-01   2.2862e-02   8.7393e-06   2.8165e-01
#> 229:   -9.7136e-01   2.5124e+00   2.4666e+00   2.2974e+00   5.1002e+00   1.9646e-01   2.4384e-01   2.2895e-02   8.9966e-06   2.8156e-01
#> 230:   -9.7124e-01   2.5132e+00   2.4666e+00   2.2976e+00   5.1002e+00   1.9670e-01   2.4377e-01   2.2947e-02   9.1582e-06   2.8162e-01
#> 231:   -9.7102e-01   2.5135e+00   2.4666e+00   2.2983e+00   5.1002e+00   1.9716e-01   2.4359e-01   2.3024e-02   8.8981e-06   2.8165e-01
#> 232:   -9.7096e-01   2.5132e+00   2.4667e+00   2.2984e+00   5.1003e+00   1.9722e-01   2.4348e-01   2.3070e-02   8.6515e-06   2.8168e-01
#> 233:   -9.7113e-01   2.5131e+00   2.4667e+00   2.2987e+00   5.1003e+00   1.9727e-01   2.4380e-01   2.3123e-02   8.7963e-06   2.8173e-01
#> 234:   -9.7142e-01   2.5129e+00   2.4666e+00   2.2989e+00   5.1003e+00   1.9734e-01   2.4418e-01   2.3148e-02   8.5658e-06   2.8176e-01
#> 235:   -9.7230e-01   2.5128e+00   2.4666e+00   2.2987e+00   5.1003e+00   1.9719e-01   2.4427e-01   2.3161e-02   8.7020e-06   2.8180e-01
#> 236:   -9.7210e-01   2.5128e+00   2.4663e+00   2.2989e+00   5.1003e+00   1.9723e-01   2.4437e-01   2.3170e-02   8.4858e-06   2.8182e-01
#> 237:   -9.7253e-01   2.5129e+00   2.4661e+00   2.2984e+00   5.1003e+00   1.9746e-01   2.4469e-01   2.3133e-02   8.2793e-06   2.8184e-01
#> 238:   -9.7263e-01   2.5128e+00   2.4660e+00   2.2983e+00   5.1004e+00   1.9752e-01   2.4478e-01   2.3104e-02   8.2027e-06   2.8191e-01
#> 239:   -9.7300e-01   2.5127e+00   2.4661e+00   2.2981e+00   5.1003e+00   1.9750e-01   2.4477e-01   2.3070e-02   8.4383e-06   2.8182e-01
#> 240:   -9.7355e-01   2.5126e+00   2.4661e+00   2.2981e+00   5.1003e+00   1.9768e-01   2.4526e-01   2.3082e-02   8.2478e-06   2.8184e-01
#> 241:   -9.7378e-01   2.5125e+00   2.4662e+00   2.2978e+00   5.1002e+00   1.9754e-01   2.4539e-01   2.3075e-02   8.1768e-06   2.8190e-01
#> 242:   -9.7409e-01   2.5123e+00   2.4661e+00   2.2977e+00   5.1002e+00   1.9739e-01   2.4523e-01   2.3092e-02   8.0494e-06   2.8181e-01
#> 243:   -9.7441e-01   2.5121e+00   2.4659e+00   2.2977e+00   5.1002e+00   1.9734e-01   2.4522e-01   2.3087e-02   7.9262e-06   2.8173e-01
#> 244:   -9.7463e-01   2.5119e+00   2.4657e+00   2.2978e+00   5.1001e+00   1.9710e-01   2.4497e-01   2.3120e-02   8.1327e-06   2.8166e-01
#> 245:   -9.7475e-01   2.5116e+00   2.4657e+00   2.2977e+00   5.1002e+00   1.9700e-01   2.4507e-01   2.3177e-02   8.2369e-06   2.8170e-01
#> 246:   -9.7512e-01   2.5116e+00   2.4658e+00   2.2978e+00   5.1002e+00   1.9710e-01   2.4520e-01   2.3198e-02   8.1735e-06   2.8176e-01
#> 247:   -9.7529e-01   2.5114e+00   2.4659e+00   2.2979e+00   5.1003e+00   1.9715e-01   2.4532e-01   2.3215e-02   8.1116e-06   2.8181e-01
#> 248:   -9.7511e-01   2.5116e+00   2.4659e+00   2.2982e+00   5.1004e+00   1.9734e-01   2.4512e-01   2.3251e-02   8.0514e-06   2.8186e-01
#> 249:   -9.7490e-01   2.5115e+00   2.4659e+00   2.2984e+00   5.1005e+00   1.9739e-01   2.4508e-01   2.3270e-02   7.9429e-06   2.8179e-01
#> 250:   -9.7493e-01   2.5116e+00   2.4658e+00   2.2985e+00   5.1005e+00   1.9710e-01   2.4517e-01   2.3284e-02   8.0358e-06   2.8182e-01
#> 251:   -9.7523e-01   2.5116e+00   2.4657e+00   2.2987e+00   5.1005e+00   1.9686e-01   2.4545e-01   2.3276e-02   7.8913e-06   2.8184e-01
#> 252:   -9.7534e-01   2.5114e+00   2.4656e+00   2.2988e+00   5.1005e+00   1.9692e-01   2.4548e-01   2.3302e-02   7.7903e-06   2.8177e-01
#> 253:   -9.7537e-01   2.5115e+00   2.4655e+00   2.2987e+00   5.1005e+00   1.9697e-01   2.4558e-01   2.3343e-02   7.8773e-06   2.8180e-01
#> 254:   -9.7559e-01   2.5117e+00   2.4652e+00   2.2989e+00   5.1006e+00   1.9727e-01   2.4570e-01   2.3378e-02   7.8245e-06   2.8185e-01
#> 255:   -9.7553e-01   2.5120e+00   2.4649e+00   2.2986e+00   5.1006e+00   1.9730e-01   2.4577e-01   2.3339e-02   7.6932e-06   2.8186e-01
#> 256:   -9.7576e-01   2.5120e+00   2.4648e+00   2.2987e+00   5.1006e+00   1.9747e-01   2.4564e-01   2.3300e-02   7.5659e-06   2.8187e-01
#> 257:   -9.7584e-01   2.5121e+00   2.4647e+00   2.2988e+00   5.1006e+00   1.9748e-01   2.4560e-01   2.3277e-02   7.6459e-06   2.8190e-01
#> 258:   -9.7597e-01   2.5122e+00   2.4646e+00   2.2987e+00   5.1006e+00   1.9755e-01   2.4558e-01   2.3278e-02   7.5974e-06   2.8194e-01
#> 259:   -9.7593e-01   2.5120e+00   2.4645e+00   2.2988e+00   5.1006e+00   1.9742e-01   2.4542e-01   2.3246e-02   7.4777e-06   2.8195e-01
#> 260:   -9.7590e-01   2.5120e+00   2.4644e+00   2.2988e+00   5.1006e+00   1.9737e-01   2.4537e-01   2.3237e-02   7.5534e-06   2.8198e-01
#> 261:   -9.7585e-01   2.5122e+00   2.4644e+00   2.2989e+00   5.1005e+00   1.9738e-01   2.4531e-01   2.3272e-02   7.6282e-06   2.8200e-01
#> 262:   -9.7584e-01   2.5123e+00   2.4645e+00   2.2988e+00   5.1005e+00   1.9738e-01   2.4522e-01   2.3270e-02   7.9236e-06   2.8198e-01
#> 263:   -9.7575e-01   2.5124e+00   2.4644e+00   2.2986e+00   5.1004e+00   1.9738e-01   2.4517e-01   2.3266e-02   7.8075e-06   2.8199e-01
#> 264:   -9.7572e-01   2.5124e+00   2.4645e+00   2.2986e+00   5.1004e+00   1.9745e-01   2.4520e-01   2.3266e-02   7.7257e-06   2.8193e-01
#> 265:   -9.7555e-01   2.5124e+00   2.4644e+00   2.2988e+00   5.1004e+00   1.9751e-01   2.4518e-01   2.3265e-02   7.9871e-06   2.8186e-01
#> 266:   -9.7545e-01   2.5124e+00   2.4642e+00   2.2988e+00   5.1004e+00   1.9768e-01   2.4490e-01   2.3284e-02   7.8755e-06   2.8187e-01
#> 267:   -9.7546e-01   2.5123e+00   2.4642e+00   2.2987e+00   5.1004e+00   1.9770e-01   2.4496e-01   2.3312e-02   7.7668e-06   2.8188e-01
#> 268:   -9.7554e-01   2.5124e+00   2.4641e+00   2.2986e+00   5.1004e+00   1.9757e-01   2.4519e-01   2.3309e-02   7.8348e-06   2.8190e-01
#> 269:   -9.7554e-01   2.5127e+00   2.4639e+00   2.2987e+00   5.1004e+00   1.9763e-01   2.4527e-01   2.3320e-02   7.7934e-06   2.8194e-01
#> 270:   -9.7553e-01   2.5127e+00   2.4639e+00   2.2987e+00   5.1004e+00   1.9785e-01   2.4512e-01   2.3329e-02   7.8596e-06   2.8196e-01
#> 271:   -9.7554e-01   2.5128e+00   2.4639e+00   2.2985e+00   5.1004e+00   1.9799e-01   2.4524e-01   2.3309e-02   7.9252e-06   2.8198e-01
#> 272:   -9.7549e-01   2.5129e+00   2.4640e+00   2.2987e+00   5.1004e+00   1.9807e-01   2.4520e-01   2.3295e-02   8.1836e-06   2.8196e-01
#> 273:   -9.7545e-01   2.5128e+00   2.4640e+00   2.2987e+00   5.1004e+00   1.9800e-01   2.4529e-01   2.3285e-02   8.0805e-06   2.8196e-01
#> 274:   -9.7542e-01   2.5129e+00   2.4640e+00   2.2987e+00   5.1004e+00   1.9785e-01   2.4551e-01   2.3283e-02   8.0412e-06   2.8200e-01
#> 275:   -9.7539e-01   2.5131e+00   2.4641e+00   2.2987e+00   5.1004e+00   1.9774e-01   2.4558e-01   2.3270e-02   8.0026e-06   2.8203e-01
#> 276:   -9.7547e-01   2.5130e+00   2.4642e+00   2.2988e+00   5.1004e+00   1.9772e-01   2.4557e-01   2.3262e-02   7.9645e-06   2.8206e-01
#> 277:   -9.7537e-01   2.5130e+00   2.4643e+00   2.2987e+00   5.1005e+00   1.9791e-01   2.4562e-01   2.3278e-02   8.0253e-06   2.8208e-01
#> 278:   -9.7539e-01   2.5131e+00   2.4644e+00   2.2988e+00   5.1005e+00   1.9797e-01   2.4578e-01   2.3275e-02   7.9303e-06   2.8208e-01
#> 279:   -9.7541e-01   2.5131e+00   2.4645e+00   2.2989e+00   5.1005e+00   1.9806e-01   2.4586e-01   2.3288e-02   8.1389e-06   2.8202e-01
#> 280:   -9.7560e-01   2.5128e+00   2.4647e+00   2.2988e+00   5.1004e+00   1.9811e-01   2.4593e-01   2.3304e-02   8.1980e-06   2.8204e-01
#> 281:   -9.7562e-01   2.5126e+00   2.4648e+00   2.2989e+00   5.1004e+00   1.9818e-01   2.4599e-01   2.3328e-02   8.2567e-06   2.8206e-01
#> 282:   -9.7576e-01   2.5126e+00   2.4647e+00   2.2990e+00   5.1005e+00   1.9813e-01   2.4601e-01   2.3322e-02   8.7480e-06   2.8202e-01
#> 283:   -9.7599e-01   2.5125e+00   2.4646e+00   2.2989e+00   5.1005e+00   1.9801e-01   2.4606e-01   2.3319e-02   8.8069e-06   2.8204e-01
#> 284:   -9.7629e-01   2.5125e+00   2.4645e+00   2.2988e+00   5.1005e+00   1.9803e-01   2.4614e-01   2.3334e-02   8.7119e-06   2.8205e-01
#> 285:   -9.7638e-01   2.5125e+00   2.4644e+00   2.2989e+00   5.1005e+00   1.9803e-01   2.4625e-01   2.3313e-02   8.7693e-06   2.8206e-01
#> 286:   -9.7648e-01   2.5124e+00   2.4643e+00   2.2988e+00   5.1005e+00   1.9809e-01   2.4624e-01   2.3303e-02   8.8572e-06   2.8203e-01
#> 287:   -9.7664e-01   2.5123e+00   2.4642e+00   2.2988e+00   5.1005e+00   1.9812e-01   2.4622e-01   2.3294e-02   8.8219e-06   2.8206e-01
#> 288:   -9.7665e-01   2.5123e+00   2.4641e+00   2.2988e+00   5.1005e+00   1.9818e-01   2.4616e-01   2.3314e-02   8.7311e-06   2.8206e-01
#> 289:   -9.7676e-01   2.5123e+00   2.4642e+00   2.2988e+00   5.1005e+00   1.9814e-01   2.4608e-01   2.3316e-02   8.7860e-06   2.8208e-01
#> 290:   -9.7680e-01   2.5124e+00   2.4642e+00   2.2987e+00   5.1005e+00   1.9821e-01   2.4608e-01   2.3341e-02   8.7521e-06   2.8210e-01
#> 291:   -9.7696e-01   2.5124e+00   2.4641e+00   2.2986e+00   5.1005e+00   1.9825e-01   2.4600e-01   2.3363e-02   8.8059e-06   2.8212e-01
#> 292:   -9.7707e-01   2.5124e+00   2.4641e+00   2.2987e+00   5.1005e+00   1.9830e-01   2.4619e-01   2.3357e-02   8.8592e-06   2.8213e-01
#> 293:   -9.7712e-01   2.5124e+00   2.4642e+00   2.2987e+00   5.1005e+00   1.9839e-01   2.4613e-01   2.3343e-02   8.8262e-06   2.8216e-01
#> 294:   -9.7733e-01   2.5122e+00   2.4642e+00   2.2988e+00   5.1005e+00   1.9838e-01   2.4610e-01   2.3342e-02   9.0325e-06   2.8214e-01
#> 295:   -9.7746e-01   2.5121e+00   2.4641e+00   2.2987e+00   5.1004e+00   1.9826e-01   2.4615e-01   2.3342e-02   9.0848e-06   2.8215e-01
#> 296:   -9.7736e-01   2.5121e+00   2.4641e+00   2.2987e+00   5.1004e+00   1.9821e-01   2.4623e-01   2.3345e-02   9.0524e-06   2.8218e-01
#> 297:   -9.7742e-01   2.5121e+00   2.4641e+00   2.2987e+00   5.1005e+00   1.9815e-01   2.4630e-01   2.3361e-02   9.1036e-06   2.8219e-01
#> 298:   -9.7746e-01   2.5121e+00   2.4641e+00   2.2986e+00   5.1005e+00   1.9809e-01   2.4633e-01   2.3387e-02   9.1545e-06   2.8220e-01
#> 299:   -9.7746e-01   2.5121e+00   2.4640e+00   2.2986e+00   5.1005e+00   1.9811e-01   2.4633e-01   2.3392e-02   9.1229e-06   2.8222e-01
#> 300:   -9.7757e-01   2.5120e+00   2.4639e+00   2.2987e+00   5.1005e+00   1.9813e-01   2.4643e-01   2.3405e-02   9.0917e-06   2.8225e-01
#> 301:   -9.7758e-01   2.5119e+00   2.4638e+00   2.2987e+00   5.1004e+00   1.9818e-01   2.4648e-01   2.3391e-02   9.2859e-06   2.8223e-01
#> 302:   -9.7770e-01   2.5119e+00   2.4638e+00   2.2986e+00   5.1005e+00   1.9818e-01   2.4647e-01   2.3382e-02   9.3317e-06   2.8225e-01
#> 303:   -9.7780e-01   2.5119e+00   2.4637e+00   2.2986e+00   5.1004e+00   1.9818e-01   2.4656e-01   2.3390e-02   9.2889e-06   2.8226e-01
#> 304:   -9.7783e-01   2.5120e+00   2.4636e+00   2.2987e+00   5.1004e+00   1.9837e-01   2.4652e-01   2.3391e-02   9.3373e-06   2.8227e-01
#> 305:   -9.7790e-01   2.5120e+00   2.4637e+00   2.2987e+00   5.1004e+00   1.9850e-01   2.4659e-01   2.3368e-02   9.3807e-06   2.8229e-01
#> 306:   -9.7784e-01   2.5120e+00   2.4636e+00   2.2988e+00   5.1005e+00   1.9873e-01   2.4657e-01   2.3361e-02   9.5679e-06   2.8228e-01
#> 307:   -9.7783e-01   2.5121e+00   2.4636e+00   2.2988e+00   5.1005e+00   1.9891e-01   2.4658e-01   2.3372e-02   9.6156e-06   2.8229e-01
#> 308:   -9.7792e-01   2.5121e+00   2.4635e+00   2.2987e+00   5.1005e+00   1.9907e-01   2.4662e-01   2.3383e-02   9.6629e-06   2.8230e-01
#> 309:   -9.7799e-01   2.5122e+00   2.4635e+00   2.2987e+00   5.1005e+00   1.9919e-01   2.4655e-01   2.3392e-02   9.6334e-06   2.8232e-01
#> 310:   -9.7800e-01   2.5121e+00   2.4637e+00   2.2987e+00   5.1005e+00   1.9924e-01   2.4657e-01   2.3390e-02   9.6041e-06   2.8234e-01
#> 311:   -9.7804e-01   2.5121e+00   2.4638e+00   2.2987e+00   5.1005e+00   1.9925e-01   2.4664e-01   2.3385e-02   9.5602e-06   2.8235e-01
#> 312:   -9.7813e-01   2.5121e+00   2.4637e+00   2.2987e+00   5.1004e+00   1.9920e-01   2.4670e-01   2.3366e-02   9.5315e-06   2.8237e-01
#> 313:   -9.7814e-01   2.5122e+00   2.4637e+00   2.2987e+00   5.1004e+00   1.9920e-01   2.4687e-01   2.3344e-02   9.5032e-06   2.8238e-01
#> 314:   -9.7824e-01   2.5122e+00   2.4636e+00   2.2987e+00   5.1004e+00   1.9919e-01   2.4692e-01   2.3334e-02   9.4751e-06   2.8240e-01
#> 315:   -9.7833e-01   2.5120e+00   2.4636e+00   2.2987e+00   5.1004e+00   1.9926e-01   2.4681e-01   2.3323e-02   9.5193e-06   2.8241e-01
#> 316:   -9.7841e-01   2.5119e+00   2.4636e+00   2.2987e+00   5.1004e+00   1.9927e-01   2.4683e-01   2.3316e-02   9.5633e-06   2.8242e-01
#> 317:   -9.7847e-01   2.5120e+00   2.4636e+00   2.2987e+00   5.1004e+00   1.9936e-01   2.4679e-01   2.3307e-02   9.5980e-06   2.8244e-01
#> 318:   -9.7860e-01   2.5120e+00   2.4636e+00   2.2987e+00   5.1004e+00   1.9947e-01   2.4681e-01   2.3295e-02   9.5708e-06   2.8245e-01
#> 319:   -9.7862e-01   2.5120e+00   2.4635e+00   2.2987e+00   5.1004e+00   1.9956e-01   2.4684e-01   2.3287e-02   9.5438e-06   2.8247e-01
#> 320:   -9.7876e-01   2.5120e+00   2.4634e+00   2.2987e+00   5.1004e+00   1.9953e-01   2.4701e-01   2.3288e-02   9.5171e-06   2.8249e-01
#> 321:   -9.7878e-01   2.5119e+00   2.4635e+00   2.2987e+00   5.1004e+00   1.9954e-01   2.4718e-01   2.3296e-02   9.4906e-06   2.8250e-01
#> 322:   -9.7876e-01   2.5120e+00   2.4634e+00   2.2987e+00   5.1004e+00   1.9958e-01   2.4725e-01   2.3288e-02   9.4644e-06   2.8252e-01
#> 323:   -9.7885e-01   2.5121e+00   2.4634e+00   2.2987e+00   5.1005e+00   1.9954e-01   2.4732e-01   2.3285e-02   9.5057e-06   2.8253e-01
#> 324:   -9.7894e-01   2.5122e+00   2.4633e+00   2.2986e+00   5.1005e+00   1.9958e-01   2.4740e-01   2.3290e-02   9.4799e-06   2.8254e-01
#> 325:   -9.7901e-01   2.5120e+00   2.4631e+00   2.2986e+00   5.1004e+00   1.9965e-01   2.4751e-01   2.3288e-02   9.4544e-06   2.8256e-01
#> 326:   -9.7913e-01   2.5120e+00   2.4630e+00   2.2987e+00   5.1005e+00   1.9974e-01   2.4752e-01   2.3283e-02   9.6126e-06   2.8254e-01
#> 327:   -9.7935e-01   2.5119e+00   2.4629e+00   2.2986e+00   5.1005e+00   1.9974e-01   2.4761e-01   2.3269e-02   9.7704e-06   2.8252e-01
#> 328:   -9.7943e-01   2.5119e+00   2.4629e+00   2.2986e+00   5.1005e+00   1.9978e-01   2.4773e-01   2.3263e-02   9.7451e-06   2.8254e-01
#> 329:   -9.7944e-01   2.5119e+00   2.4629e+00   2.2987e+00   5.1005e+00   1.9984e-01   2.4779e-01   2.3250e-02   9.7199e-06   2.8255e-01
#> 330:   -9.7954e-01   2.5118e+00   2.4629e+00   2.2987e+00   5.1005e+00   1.9985e-01   2.4799e-01   2.3233e-02   9.6950e-06   2.8256e-01
#> 331:   -9.7963e-01   2.5118e+00   2.4629e+00   2.2987e+00   5.1005e+00   1.9986e-01   2.4802e-01   2.3240e-02   1.0719e-05   2.8256e-01
#> 332:   -9.7969e-01   2.5118e+00   2.4629e+00   2.2987e+00   5.1005e+00   1.9981e-01   2.4804e-01   2.3237e-02   1.0693e-05   2.8257e-01
#> 333:   -9.7972e-01   2.5118e+00   2.4629e+00   2.2987e+00   5.1005e+00   1.9972e-01   2.4803e-01   2.3239e-02   1.0850e-05   2.8256e-01
#> 334:   -9.7968e-01   2.5118e+00   2.4629e+00   2.2987e+00   5.1005e+00   1.9967e-01   2.4797e-01   2.3227e-02   1.0798e-05   2.8257e-01
#> 335:   -9.7968e-01   2.5118e+00   2.4628e+00   2.2987e+00   5.1005e+00   1.9969e-01   2.4785e-01   2.3215e-02   1.0953e-05   2.8255e-01
#> 336:   -9.7978e-01   2.5118e+00   2.4627e+00   2.2987e+00   5.1005e+00   1.9967e-01   2.4780e-01   2.3202e-02   1.0962e-05   2.8256e-01
#> 337:   -9.7988e-01   2.5117e+00   2.4626e+00   2.2987e+00   5.1005e+00   1.9969e-01   2.4781e-01   2.3190e-02   1.1115e-05   2.8255e-01
#> 338:   -9.7996e-01   2.5117e+00   2.4626e+00   2.2987e+00   5.1005e+00   1.9977e-01   2.4776e-01   2.3186e-02   1.1269e-05   2.8253e-01
#> 339:   -9.8003e-01   2.5117e+00   2.4626e+00   2.2987e+00   5.1005e+00   1.9982e-01   2.4776e-01   2.3193e-02   1.1243e-05   2.8254e-01
#> 340:   -9.8014e-01   2.5116e+00   2.4626e+00   2.2987e+00   5.1005e+00   1.9981e-01   2.4776e-01   2.3190e-02   1.1218e-05   2.8256e-01
#> 341:   -9.8014e-01   2.5115e+00   2.4626e+00   2.2987e+00   5.1005e+00   1.9990e-01   2.4774e-01   2.3181e-02   1.1193e-05   2.8257e-01
#> 342:   -9.8014e-01   2.5115e+00   2.4626e+00   2.2987e+00   5.1005e+00   2.0005e-01   2.4770e-01   2.3167e-02   1.1232e-05   2.8258e-01
#> 343:   -9.8020e-01   2.5116e+00   2.4625e+00   2.2987e+00   5.1005e+00   2.0004e-01   2.4774e-01   2.3143e-02   1.1207e-05   2.8259e-01
#> 344:   -9.8024e-01   2.5115e+00   2.4625e+00   2.2987e+00   5.1005e+00   2.0009e-01   2.4780e-01   2.3124e-02   1.1183e-05   2.8260e-01
#> 345:   -9.8017e-01   2.5115e+00   2.4625e+00   2.2987e+00   5.1005e+00   2.0013e-01   2.4788e-01   2.3102e-02   1.1329e-05   2.8259e-01
#> 346:   -9.8014e-01   2.5114e+00   2.4625e+00   2.2987e+00   5.1005e+00   2.0021e-01   2.4797e-01   2.3081e-02   1.1305e-05   2.8260e-01
#> 347:   -9.8022e-01   2.5114e+00   2.4626e+00   2.2987e+00   5.1004e+00   2.0018e-01   2.4794e-01   2.3073e-02   1.1308e-05   2.8261e-01
#> 348:   -9.8022e-01   2.5115e+00   2.4626e+00   2.2987e+00   5.1005e+00   2.0024e-01   2.4793e-01   2.3074e-02   1.1311e-05   2.8262e-01
#> 349:   -9.8032e-01   2.5114e+00   2.4626e+00   2.2987e+00   5.1005e+00   2.0023e-01   2.4793e-01   2.3078e-02   1.1314e-05   2.8263e-01
#> 350:   -9.8039e-01   2.5114e+00   2.4625e+00   2.2987e+00   5.1005e+00   2.0023e-01   2.4798e-01   2.3083e-02   1.1290e-05   2.8265e-01
#> 351:   -9.8042e-01   2.5113e+00   2.4624e+00   2.2987e+00   5.1005e+00   2.0028e-01   2.4802e-01   2.3084e-02   1.1327e-05   2.8265e-01
#> 352:   -9.8055e-01   2.5112e+00   2.4624e+00   2.2987e+00   5.1005e+00   2.0028e-01   2.4802e-01   2.3090e-02   1.1363e-05   2.8266e-01
#> 353:   -9.8054e-01   2.5112e+00   2.4624e+00   2.2987e+00   5.1005e+00   2.0030e-01   2.4807e-01   2.3086e-02   1.1340e-05   2.8267e-01
#> 354:   -9.8062e-01   2.5112e+00   2.4623e+00   2.2987e+00   5.1005e+00   2.0032e-01   2.4806e-01   2.3090e-02   1.1479e-05   2.8265e-01
#> 355:   -9.8069e-01   2.5111e+00   2.4623e+00   2.2987e+00   5.1005e+00   2.0037e-01   2.4800e-01   2.3096e-02   1.1617e-05   2.8264e-01
#> 356:   -9.8073e-01   2.5109e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0037e-01   2.4801e-01   2.3097e-02   1.1755e-05   2.8262e-01
#> 357:   -9.8071e-01   2.5109e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0052e-01   2.4802e-01   2.3098e-02   1.1732e-05   2.8264e-01
#> 358:   -9.8076e-01   2.5109e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0064e-01   2.4803e-01   2.3098e-02   1.1869e-05   2.8262e-01
#> 359:   -9.8075e-01   2.5108e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0066e-01   2.4809e-01   2.3084e-02   1.1846e-05   2.8263e-01
#> 360:   -9.8069e-01   2.5108e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0063e-01   2.4818e-01   2.3084e-02   1.1823e-05   2.8264e-01
#> 361:   -9.8065e-01   2.5108e+00   2.4623e+00   2.2987e+00   5.1005e+00   2.0063e-01   2.4828e-01   2.3078e-02   1.1801e-05   2.8266e-01
#> 362:   -9.8066e-01   2.5108e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0064e-01   2.4831e-01   2.3079e-02   1.1749e-05   2.8266e-01
#> 363:   -9.8066e-01   2.5108e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0065e-01   2.4832e-01   2.3085e-02   1.1727e-05   2.8267e-01
#> 364:   -9.8075e-01   2.5107e+00   2.4622e+00   2.2987e+00   5.1005e+00   2.0066e-01   2.4827e-01   2.3093e-02   1.1946e-05   2.8269e-01
#> 365:   -9.8086e-01   2.5107e+00   2.4621e+00   2.2987e+00   5.1005e+00   2.0068e-01   2.4820e-01   2.3101e-02   1.2078e-05   2.8267e-01
#> 366:   -9.8093e-01   2.5106e+00   2.4621e+00   2.2987e+00   5.1005e+00   2.0069e-01   2.4817e-01   2.3104e-02   1.2056e-05   2.8268e-01
#> 367:   -9.8102e-01   2.5106e+00   2.4621e+00   2.2987e+00   5.1004e+00   2.0074e-01   2.4820e-01   2.3106e-02   1.2003e-05   2.8269e-01
#> 368:   -9.8094e-01   2.5106e+00   2.4620e+00   2.2987e+00   5.1004e+00   2.0088e-01   2.4823e-01   2.3114e-02   1.1982e-05   2.8270e-01
#> 369:   -9.8090e-01   2.5107e+00   2.4620e+00   2.2987e+00   5.1004e+00   2.0103e-01   2.4824e-01   2.3129e-02   1.2015e-05   2.8270e-01
#> 370:   -9.8089e-01   2.5108e+00   2.4620e+00   2.2987e+00   5.1004e+00   2.0111e-01   2.4821e-01   2.3128e-02   1.2048e-05   2.8271e-01
#> 371:   -9.8086e-01   2.5109e+00   2.4620e+00   2.2987e+00   5.1005e+00   2.0110e-01   2.4823e-01   2.3114e-02   1.2027e-05   2.8272e-01
#> 372:   -9.8088e-01   2.5109e+00   2.4620e+00   2.2987e+00   5.1004e+00   2.0113e-01   2.4825e-01   2.3103e-02   1.2154e-05   2.8270e-01
#> 373:   -9.8091e-01   2.5109e+00   2.4619e+00   2.2987e+00   5.1004e+00   2.0116e-01   2.4817e-01   2.3108e-02   1.2281e-05   2.8269e-01
#> 374:   -9.8093e-01   2.5110e+00   2.4619e+00   2.2987e+00   5.1004e+00   2.0122e-01   2.4818e-01   2.3105e-02   1.2259e-05   2.8270e-01
#> 375:   -9.8095e-01   2.5110e+00   2.4619e+00   2.2987e+00   5.1004e+00   2.0123e-01   2.4820e-01   2.3096e-02   1.2251e-05   2.8271e-01
#> 376:   -9.8097e-01   2.5110e+00   2.4619e+00   2.2987e+00   5.1004e+00   2.0118e-01   2.4819e-01   2.3084e-02   1.2291e-05   2.8274e-01
#> 377:   -9.8101e-01   2.5110e+00   2.4619e+00   2.2987e+00   5.1004e+00   2.0116e-01   2.4814e-01   2.3084e-02   1.2283e-05   2.8275e-01
#> 378:   -9.8114e-01   2.5110e+00   2.4618e+00   2.2987e+00   5.1004e+00   2.0115e-01   2.4812e-01   2.3073e-02   1.2218e-05   2.8276e-01
#> 379:   -9.8120e-01   2.5111e+00   2.4618e+00   2.2987e+00   5.1004e+00   2.0127e-01   2.4812e-01   2.3065e-02   1.2210e-05   2.8277e-01
#> 380:   -9.8126e-01   2.5110e+00   2.4618e+00   2.2987e+00   5.1004e+00   2.0144e-01   2.4811e-01   2.3067e-02   1.2190e-05   2.8278e-01
#> 381:   -9.8134e-01   2.5110e+00   2.4618e+00   2.2987e+00   5.1004e+00   2.0157e-01   2.4814e-01   2.3062e-02   1.2169e-05   2.8279e-01
#> 382:   -9.8138e-01   2.5110e+00   2.4618e+00   2.2987e+00   5.1004e+00   2.0162e-01   2.4819e-01   2.3041e-02   1.2162e-05   2.8280e-01
#> 383:   -9.8142e-01   2.5111e+00   2.4618e+00   2.2987e+00   5.1004e+00   2.0158e-01   2.4821e-01   2.3030e-02   1.2155e-05   2.8281e-01
#> 384:   -9.8143e-01   2.5111e+00   2.4617e+00   2.2987e+00   5.1004e+00   2.0154e-01   2.4817e-01   2.3023e-02   1.2136e-05   2.8282e-01
#> 385:   -9.8155e-01   2.5110e+00   2.4617e+00   2.2987e+00   5.1004e+00   2.0153e-01   2.4815e-01   2.3027e-02   1.2116e-05   2.8282e-01
#> 386:   -9.8155e-01   2.5109e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0153e-01   2.4817e-01   2.3031e-02   1.2118e-05   2.8280e-01
#> 387:   -9.8160e-01   2.5109e+00   2.4617e+00   2.2987e+00   5.1004e+00   2.0157e-01   2.4817e-01   2.3034e-02   1.2235e-05   2.8279e-01
#> 388:   -9.8165e-01   2.5109e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0157e-01   2.4816e-01   2.3031e-02   1.2186e-05   2.8280e-01
#> 389:   -9.8172e-01   2.5108e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0153e-01   2.4816e-01   2.3032e-02   1.2167e-05   2.8281e-01
#> 390:   -9.8181e-01   2.5108e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0156e-01   2.4818e-01   2.3025e-02   1.2148e-05   2.8281e-01
#> 391:   -9.8182e-01   2.5109e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0160e-01   2.4816e-01   2.3016e-02   1.2200e-05   2.8284e-01
#> 392:   -9.8181e-01   2.5109e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0161e-01   2.4815e-01   2.3013e-02   1.2181e-05   2.8285e-01
#> 393:   -9.8182e-01   2.5109e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0160e-01   2.4822e-01   2.3005e-02   1.2211e-05   2.8285e-01
#> 394:   -9.8184e-01   2.5109e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0158e-01   2.4829e-01   2.2995e-02   1.2192e-05   2.8286e-01
#> 395:   -9.8184e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0160e-01   2.4828e-01   2.2989e-02   1.2173e-05   2.8287e-01
#> 396:   -9.8179e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0169e-01   2.4830e-01   2.2983e-02   1.2121e-05   2.8287e-01
#> 397:   -9.8176e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0175e-01   2.4834e-01   2.2982e-02   1.2102e-05   2.8287e-01
#> 398:   -9.8181e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0181e-01   2.4839e-01   2.2972e-02   1.2084e-05   2.8288e-01
#> 399:   -9.8177e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0185e-01   2.4846e-01   2.2972e-02   1.2065e-05   2.8289e-01
#> 400:   -9.8172e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0192e-01   2.4847e-01   2.2969e-02   1.2047e-05   2.8290e-01
#> 401:   -9.8169e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0199e-01   2.4843e-01   2.2968e-02   1.2029e-05   2.8290e-01
#> 402:   -9.8170e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0200e-01   2.4845e-01   2.2972e-02   1.2011e-05   2.8291e-01
#> 403:   -9.8170e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0202e-01   2.4850e-01   2.2978e-02   1.2118e-05   2.8290e-01
#> 404:   -9.8170e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0202e-01   2.4858e-01   2.2982e-02   1.2112e-05   2.8291e-01
#> 405:   -9.8171e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0200e-01   2.4856e-01   2.2988e-02   1.2219e-05   2.8289e-01
#> 406:   -9.8170e-01   2.5112e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0204e-01   2.4852e-01   2.2992e-02   1.2201e-05   2.8290e-01
#> 407:   -9.8170e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0203e-01   2.4852e-01   2.2981e-02   1.2307e-05   2.8289e-01
#> 408:   -9.8177e-01   2.5111e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0204e-01   2.4855e-01   2.2983e-02   1.2354e-05   2.8291e-01
#> 409:   -9.8179e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0205e-01   2.4859e-01   2.2989e-02   1.2336e-05   2.8292e-01
#> 410:   -9.8178e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0206e-01   2.4865e-01   2.2992e-02   1.2319e-05   2.8293e-01
#> 411:   -9.8177e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0209e-01   2.4870e-01   2.3001e-02   1.2301e-05   2.8293e-01
#> 412:   -9.8176e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0210e-01   2.4876e-01   2.2992e-02   1.2405e-05   2.8292e-01
#> 413:   -9.8176e-01   2.5111e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0206e-01   2.4874e-01   2.2984e-02   1.2509e-05   2.8291e-01
#> 414:   -9.8172e-01   2.5112e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0206e-01   2.4873e-01   2.2980e-02   1.2552e-05   2.8293e-01
#> 415:   -9.8174e-01   2.5111e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0207e-01   2.4878e-01   2.2978e-02   1.2534e-05   2.8294e-01
#> 416:   -9.8176e-01   2.5111e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0206e-01   2.4882e-01   2.2970e-02   1.2561e-05   2.8294e-01
#> 417:   -9.8177e-01   2.5111e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0206e-01   2.4885e-01   2.2968e-02   1.2544e-05   2.8295e-01
#> 418:   -9.8175e-01   2.5111e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0209e-01   2.4881e-01   2.2962e-02   1.2646e-05   2.8294e-01
#> 419:   -9.8170e-01   2.5112e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0214e-01   2.4883e-01   2.2955e-02   1.2747e-05   2.8293e-01
#> 420:   -9.8170e-01   2.5112e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0214e-01   2.4885e-01   2.2938e-02   1.2849e-05   2.8291e-01
#> 421:   -9.8172e-01   2.5112e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0217e-01   2.4888e-01   2.2926e-02   1.2887e-05   2.8294e-01
#> 422:   -9.8174e-01   2.5112e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0221e-01   2.4885e-01   2.2908e-02   1.2913e-05   2.8294e-01
#> 423:   -9.8177e-01   2.5112e+00   2.4618e+00   2.2987e+00   5.1005e+00   2.0225e-01   2.4887e-01   2.2897e-02   1.2896e-05   2.8295e-01
#> 424:   -9.8181e-01   2.5112e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0225e-01   2.4887e-01   2.2889e-02   1.2884e-05   2.8295e-01
#> 425:   -9.8182e-01   2.5111e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0226e-01   2.4886e-01   2.2882e-02   1.2910e-05   2.8295e-01
#> 426:   -9.8188e-01   2.5111e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0222e-01   2.4888e-01   2.2887e-02   1.3010e-05   2.8294e-01
#> 427:   -9.8190e-01   2.5110e+00   2.4617e+00   2.2987e+00   5.1005e+00   2.0223e-01   2.4892e-01   2.2888e-02   1.3174e-05   2.8295e-01
#> 428:   -9.8192e-01   2.5110e+00   2.4616e+00   2.2987e+00   5.1005e+00   2.0224e-01   2.4899e-01   2.2880e-02   1.3157e-05   2.8296e-01
#> 429:   -9.8193e-01   2.5110e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0223e-01   2.4899e-01   2.2876e-02   1.3140e-05   2.8296e-01
#> 430:   -9.8199e-01   2.5111e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0219e-01   2.4899e-01   2.2873e-02   1.3127e-05   2.8297e-01
#> 431:   -9.8205e-01   2.5110e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0216e-01   2.4902e-01   2.2871e-02   1.3225e-05   2.8296e-01
#> 432:   -9.8213e-01   2.5110e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0216e-01   2.4906e-01   2.2870e-02   1.3208e-05   2.8296e-01
#> 433:   -9.8225e-01   2.5109e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0220e-01   2.4904e-01   2.2868e-02   1.3233e-05   2.8297e-01
#> 434:   -9.8227e-01   2.5109e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0217e-01   2.4908e-01   2.2865e-02   1.3187e-05   2.8296e-01
#> 435:   -9.8227e-01   2.5108e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0222e-01   2.4913e-01   2.2853e-02   1.3212e-05   2.8297e-01
#> 436:   -9.8225e-01   2.5109e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0220e-01   2.4913e-01   2.2853e-02   1.3308e-05   2.8296e-01
#> 437:   -9.8229e-01   2.5109e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0215e-01   2.4920e-01   2.2846e-02   1.3404e-05   2.8294e-01
#> 438:   -9.8234e-01   2.5109e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0216e-01   2.4930e-01   2.2832e-02   1.3388e-05   2.8295e-01
#> 439:   -9.8233e-01   2.5109e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0223e-01   2.4934e-01   2.2819e-02   1.3371e-05   2.8296e-01
#> 440:   -9.8233e-01   2.5108e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0230e-01   2.4928e-01   2.2811e-02   1.3355e-05   2.8296e-01
#> 441:   -9.8238e-01   2.5107e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0231e-01   2.4929e-01   2.2806e-02   1.3339e-05   2.8297e-01
#> 442:   -9.8240e-01   2.5107e+00   2.4615e+00   2.2987e+00   5.1005e+00   2.0234e-01   2.4931e-01   2.2805e-02   1.3323e-05   2.8298e-01
#> 443:   -9.8244e-01   2.5106e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0237e-01   2.4930e-01   2.2800e-02   1.3348e-05   2.8298e-01
#> 444:   -9.8244e-01   2.5106e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0236e-01   2.4931e-01   2.2796e-02   1.3303e-05   2.8298e-01
#> 445:   -9.8242e-01   2.5106e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0240e-01   2.4926e-01   2.2789e-02   1.3287e-05   2.8298e-01
#> 446:   -9.8241e-01   2.5107e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0247e-01   2.4926e-01   2.2788e-02   1.3380e-05   2.8297e-01
#> 447:   -9.8245e-01   2.5107e+00   2.4614e+00   2.2987e+00   5.1005e+00   2.0250e-01   2.4928e-01   2.2794e-02   1.3365e-05   2.8298e-01
#> 448:   -9.8251e-01   2.5107e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0251e-01   2.4930e-01   2.2797e-02   1.3394e-05   2.8300e-01
#> 449:   -9.8255e-01   2.5107e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0249e-01   2.4933e-01   2.2798e-02   1.3380e-05   2.8300e-01
#> 450:   -9.8259e-01   2.5107e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0247e-01   2.4932e-01   2.2796e-02   1.3420e-05   2.8303e-01
#> 451:   -9.8262e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0240e-01   2.4933e-01   2.2792e-02   1.3404e-05   2.8303e-01
#> 452:   -9.8266e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0239e-01   2.4932e-01   2.2794e-02   1.3389e-05   2.8304e-01
#> 453:   -9.8263e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0240e-01   2.4931e-01   2.2791e-02   1.3373e-05   2.8304e-01
#> 454:   -9.8261e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0240e-01   2.4929e-01   2.2791e-02   1.3463e-05   2.8303e-01
#> 455:   -9.8266e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0240e-01   2.4929e-01   2.2799e-02   1.3448e-05   2.8304e-01
#> 456:   -9.8269e-01   2.5106e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0241e-01   2.4928e-01   2.2805e-02   1.3537e-05   2.8303e-01
#> 457:   -9.8274e-01   2.5106e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0241e-01   2.4931e-01   2.2817e-02   1.3522e-05   2.8303e-01
#> 458:   -9.8271e-01   2.5106e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0243e-01   2.4932e-01   2.2825e-02   1.3507e-05   2.8304e-01
#> 459:   -9.8270e-01   2.5106e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0243e-01   2.4931e-01   2.2822e-02   1.3492e-05   2.8304e-01
#> 460:   -9.8269e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0245e-01   2.4926e-01   2.2817e-02   1.3477e-05   2.8305e-01
#> 461:   -9.8269e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0244e-01   2.4920e-01   2.2820e-02   1.3565e-05   2.8304e-01
#> 462:   -9.8265e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0245e-01   2.4918e-01   2.2820e-02   1.3652e-05   2.8303e-01
#> 463:   -9.8261e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0243e-01   2.4925e-01   2.2810e-02   1.3677e-05   2.8304e-01
#> 464:   -9.8259e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0244e-01   2.4920e-01   2.2809e-02   1.3700e-05   2.8305e-01
#> 465:   -9.8257e-01   2.5108e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0248e-01   2.4917e-01   2.2815e-02   1.3652e-05   2.8306e-01
#> 466:   -9.8251e-01   2.5109e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0249e-01   2.4916e-01   2.2818e-02   1.3795e-05   2.8306e-01
#> 467:   -9.8249e-01   2.5109e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0246e-01   2.4914e-01   2.2815e-02   1.3780e-05   2.8307e-01
#> 468:   -9.8244e-01   2.5109e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0246e-01   2.4913e-01   2.2813e-02   1.3866e-05   2.8306e-01
#> 469:   -9.8236e-01   2.5109e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0244e-01   2.4914e-01   2.2809e-02   1.3824e-05   2.8306e-01
#> 470:   -9.8233e-01   2.5110e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0243e-01   2.4918e-01   2.2809e-02   1.3796e-05   2.8307e-01
#> 471:   -9.8229e-01   2.5109e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0246e-01   2.4922e-01   2.2800e-02   1.3820e-05   2.8309e-01
#> 472:   -9.8231e-01   2.5109e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0246e-01   2.4928e-01   2.2799e-02   1.3805e-05   2.8309e-01
#> 473:   -9.8232e-01   2.5108e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0247e-01   2.4929e-01   2.2800e-02   1.3827e-05   2.8309e-01
#> 474:   -9.8230e-01   2.5108e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0249e-01   2.4928e-01   2.2796e-02   1.3813e-05   2.8310e-01
#> 475:   -9.8228e-01   2.5109e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0252e-01   2.4926e-01   2.2797e-02   1.3835e-05   2.8310e-01
#> 476:   -9.8229e-01   2.5108e+00   2.4613e+00   2.2987e+00   5.1005e+00   2.0250e-01   2.4932e-01   2.2801e-02   1.3821e-05   2.8310e-01
#> 477:   -9.8231e-01   2.5108e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0250e-01   2.4933e-01   2.2807e-02   1.3806e-05   2.8311e-01
#> 478:   -9.8242e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0250e-01   2.4935e-01   2.2811e-02   1.3792e-05   2.8311e-01
#> 479:   -9.8247e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0253e-01   2.4932e-01   2.2812e-02   1.3777e-05   2.8312e-01
#> 480:   -9.8250e-01   2.5107e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0257e-01   2.4932e-01   2.2817e-02   1.3761e-05   2.8312e-01
#> 481:   -9.8253e-01   2.5107e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0257e-01   2.4933e-01   2.2819e-02   1.3748e-05   2.8313e-01
#> 482:   -9.8256e-01   2.5107e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0256e-01   2.4932e-01   2.2819e-02   1.3735e-05   2.8313e-01
#> 483:   -9.8256e-01   2.5107e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0258e-01   2.4933e-01   2.2818e-02   1.3720e-05   2.8314e-01
#> 484:   -9.8255e-01   2.5108e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0262e-01   2.4931e-01   2.2822e-02   1.3705e-05   2.8314e-01
#> 485:   -9.8252e-01   2.5108e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0266e-01   2.4928e-01   2.2821e-02   1.3691e-05   2.8315e-01
#> 486:   -9.8251e-01   2.5108e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0271e-01   2.4930e-01   2.2822e-02   1.3677e-05   2.8315e-01
#> 487:   -9.8248e-01   2.5108e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0274e-01   2.4929e-01   2.2828e-02   1.3757e-05   2.8314e-01
#> 488:   -9.8250e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0272e-01   2.4935e-01   2.2828e-02   1.3743e-05   2.8314e-01
#> 489:   -9.8252e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0270e-01   2.4939e-01   2.2831e-02   1.3734e-05   2.8313e-01
#> 490:   -9.8253e-01   2.5107e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0273e-01   2.4942e-01   2.2839e-02   1.3721e-05   2.8313e-01
#> 491:   -9.8252e-01   2.5108e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0271e-01   2.4947e-01   2.2845e-02   1.3799e-05   2.8312e-01
#> 492:   -9.8250e-01   2.5108e+00   2.4612e+00   2.2987e+00   5.1005e+00   2.0273e-01   2.4953e-01   2.2848e-02   1.3786e-05   2.8313e-01
#> 493:   -9.8250e-01   2.5108e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0275e-01   2.4955e-01   2.2851e-02   1.3773e-05   2.8313e-01
#> 494:   -9.8252e-01   2.5107e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0281e-01   2.4961e-01   2.2854e-02   1.3758e-05   2.8314e-01
#> 495:   -9.8253e-01   2.5107e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0285e-01   2.4964e-01   2.2852e-02   1.3744e-05   2.8314e-01
#> 496:   -9.8257e-01   2.5108e+00   2.4611e+00   2.2987e+00   5.1005e+00   2.0286e-01   2.4963e-01   2.2854e-02   1.3821e-05   2.8313e-01
#> 497:   -9.8262e-01   2.5107e+00   2.4610e+00   2.2987e+00   5.1005e+00   2.0284e-01   2.4963e-01   2.2856e-02   1.3899e-05   2.8312e-01
#> 498:   -9.8259e-01   2.5108e+00   2.4610e+00   2.2987e+00   5.1005e+00   2.0281e-01   2.4965e-01   2.2860e-02   1.3860e-05   2.8313e-01
#> 499:   -9.8260e-01   2.5108e+00   2.4610e+00   2.2987e+00   5.1005e+00   2.0283e-01   2.4966e-01   2.2861e-02   1.3881e-05   2.8313e-01
#> 500:   -9.8267e-01   2.5108e+00   2.4610e+00   2.2987e+00   5.1005e+00   2.0282e-01   2.4964e-01   2.2870e-02   1.3957e-05   2.8312e-01
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 
#> 
#> [====|====|====|====|====|====|====|====|====|====] 0:20:09

Now that we have run 3 different estimation methods, we can compare the results side-by-side

library(huxtable)

as_hux("lognormal"=cmt2fit.logn, "proportional"=cmt2fit.prop, "add+prop"=cmt2fit.add.prop)
lognormal proportional add+prop
lka -0.998     -0.992     -0.983    
(0.054)    (0.052)    (0.052)   
lv 2.469 *** 2.448 *** 2.461 ***
(0.056)    (0.049)    (0.049)   
lcl 2.553 *** 2.509 *** 2.511 ***
(0.047)    (0.047)    (0.047)   
lq 2.376 *** 2.293 *** 2.299 ***
(0.040)    (0.035)    (0.036)   
lvp 5.176 *** 5.110 *** 5.100 ***
(0.038)    (0.034)    (0.034)   
sd__eta.ka 0.447     0.452     0.450    
sd__eta.v 0.203     0.169     0.151    
sd__eta.cl 0.500     0.500     0.500    
logn.sd 0.320                      
prop.sd          0.283     0.283    
add.sd                   0.000    
N 2207         2207         2207        
Objective Function -6533.378     -6730.322     -6730.371    
logLik 1492.219     1590.691     1590.715    
AIC -2966.438     -3163.382     -3161.430    
*** p < 0.001; ** p < 0.01; * p < 0.05.

Note that the additive and proportional model has the additive component approach zero. When comparing the objective functions of log-normal and proportional models, the proportional model has the lowest objective function value. (Since we modeled log-normal without data transformation it is appropriate to compare the AIC/Objective function values)

You may wish to see a visual comparison of the parameters:

library(dotwhisker)
## exponentiate=NA causes the parameters to be back-transformed
dwplot(list("lognormal"=cmt2fit.logn, "proportional"=cmt2fit.prop), exponentiate=NA) +
    ## Notice we can use some xgx functions post analysis as well,
    ## since they are ggplot helper functions
    xgx_scale_x_log10() +
    ggtitle("Comparison between lognormal and proportional models",
            "On backtransformed scales")

Model Diagnostics with ggPMX

## The controller then can be piped into a specific plot
ctr <- pmx_nlmixr(cmt2fit.logn, conts = c("WEIGHTB"), cats="TRTACT")
#> [====|====|====|====|====|====|====|====|====|====] 0:06:19

ctr %>% pmx_plot_npde_pred


## Modify graphical options and remove DRAFT label:
ctr %>% pmx_plot_npde_time(smooth = list(color="blue"), point = list(shape=4), is.draft=FALSE, 
          labels = list(x = "Time after first dose (days)", y = "Normalized PDE"))


ctr %>% pmx_plot_dv_ipred(scale_x_log10=TRUE, scale_y_log10=TRUE,filter=IPRED>0.001)


ctr %>% pmx_plot_dv_pred(scale_x_log10=TRUE, scale_y_log10=TRUE,filter=IPRED>0.001)


ctr %>% pmx_plot_abs_iwres_ipred


## For this display only show 1x1 individual plot for ID 110 for time < 12
ctr %>% pmx_plot_individual(1, filter=ID == 110 & TIME > 0 & TIME < 12, 
                            facets = list(nrow = 1, ncol = 1))


ctr %>% pmx_plot_iwres_dens


ctr %>% pmx_plot_eta_qq


## 0-12 hour VPC on semi-log scale
ctr %>% pmx_plot_vpc(filter=TIME > 0 & TIME < 12,
                     scale_y_log10=TRUE, 
                     bin=pmx_vpc_bin("jenks",n=5))


## 0-12 hour VPC on linear scale
ctr %>% pmx_plot_vpc(filter=TIME > 0 & TIME < 12,
                     scale_y_log10=FALSE, 
                     bin=pmx_vpc_bin("jenks",n=5))

                     
ctr %>% pmx_plot_eta_box

ctr %>% pmx_plot_eta_hist

ctr %>% pmx_plot_eta_matrix


## Create a report of all the plots you generated from the controller
if (!file.exists("nlmixr_report.docx")){
    ctr %>% pmx_report("nlmixr_report",".")
}
if (file.exists("nlmixr_report.Rmd"))
    unlink("nlmixr_report.Rmd") # Remove this file so it isn't confused with vignettes

This creates two reports with default settings, both a pdf and word document. The report can be customized by editting the default template to include project specificities (change labels, stratifications, filtering, etc.).

Simulation of a new scenario with RxODE

By creating events you can simply simulate a new scenario. Perhaps your drug development team wants to explore the 100 mg dose 3 times a day dosing to see what happens with the PK. You can simply simulate from the nlmixr model using a new event table created from RxODE.

In this case we wish to simulate with some variability and see what happens at steady state:

(ev <- et(amt=100, ii=8, ss=1))
#> ─────────────────────────── EventTable with 1 records ──────────────────────────
#>    1 dosing records (see x$get.dosing(); add with add.dosing or et)
#>    0 observation times (see x$get.sampling(); add with add.sampling or et)
#> ── First part of x: ────────────────────────────────────────────────────────────
#> # A tibble: 1 x 5
#>    time   amt    ii evid            ss
#>   <dbl> <dbl> <dbl> <evid>       <int>
#> 1     0   100     8 1:Dose (Add)     1
#> ────────────────────────────────────────────────────────────────────────────────

ev$add.sampling(seq(0, 8, length.out=100))
print(ev)
#> ────────────────────────── EventTable with 101 records ─────────────────────────
#>    1 dosing records (see $get.dosing(); add with add.dosing or et)
#>    100 observation times (see $get.sampling(); add with add.sampling or et)
#> ── First part of : ─────────────────────────────────────────────────────────────
#> # A tibble: 101 x 5
#>      time   amt    ii evid             ss
#>     <dbl> <dbl> <dbl> <evid>        <int>
#>  1 0         NA    NA 0:Observation    NA
#>  2 0        100     8 1:Dose (Add)      1
#>  3 0.0808    NA    NA 0:Observation    NA
#>  4 0.162     NA    NA 0:Observation    NA
#>  5 0.242     NA    NA 0:Observation    NA
#>  6 0.323     NA    NA 0:Observation    NA
#>  7 0.404     NA    NA 0:Observation    NA
#>  8 0.485     NA    NA 0:Observation    NA
#>  9 0.566     NA    NA 0:Observation    NA
#> 10 0.646     NA    NA 0:Observation    NA
#> # … with 91 more rows
#> ────────────────────────────────────────────────────────────────────────────────

A nlmixr model already includes information about the parameter estimates and can simulate without uncertainty in the population parameters or covariances, like what is done for a VPC.

If you wish to simulate 100 patients repeated by 100 different theoretical studies where you simulate from the uncertainty in the fixed parameter estimates and covariances you can very easily with nlmixr/RxODE:

set.seed(100)
sim1 <- simulate(cmt2fit.logn, events=ev, nSub=100, nStud=100)
#> [====|====|====|====|====|====|====|====|====|====] 0:02:49

print(sim1)
#> ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Solved RxODE object ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
#> ── Initial Conditions ($inits): ────────────────────────────────────────────────
#>      depot    central peripheral 
#>          0          0          0 
#> 
#> Simulation with uncertainty in:
#>   - parameters ($thetaMat for changes)
#>   - omega matrix ($omegaList)
#>   - sigma matrix ($sigmaList)
#> 
#> ── First part of data (object): ────────────────────────────────────────────────
#> # A tibble: 1,000,000 x 7
#>   sim.id   time ipred   sim depot central peripheral
#>    <int>  <dbl> <dbl> <dbl> <dbl>   <dbl>      <dbl>
#> 1      1 0      0.250 0.331 102.     1.96       105.
#> 2      1 0.0808 0.691 0.797  97.6    5.41       105.
#> 3      1 0.162  0.995 0.852  93.7    7.79       105.
#> 4      1 0.242  1.20  0.772  89.9    9.39       106.
#> 5      1 0.323  1.33  1.42   86.2   10.4        106.
#> 6      1 0.404  1.41  1.10   82.7   11.1        107.
#> # … with 999,994 more rows
#> ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

You may examine the simulated study information easily, as show in the RxODE printout:

head(sim1$thetaMat)
#>              lka           lv          lcl          lq           lvp
#> [1,] -0.01043193 -0.007611034 -0.053051023  0.02345737  0.0002913503
#> [2,] -0.01857990 -0.093889352 -0.050334923 -0.04110705  0.0198110505
#> [3,] -0.05901739 -0.010213278 -0.002949055  0.01338986  0.0921800430
#> [4,]  0.12315772  0.122623690  0.033201070  0.04442167 -0.0595848069
#> [5,] -0.03469916 -0.046088903 -0.016364821 -0.03015755  0.0147288196
#> [6,]  0.02872446  0.074789590 -0.080679098 -0.05292902 -0.0339947624

You can also see the covariance matricies that are simulated (note they come from an inverse Wishart distribution):

head(sim1$omegaList)
#> [[1]]
#>             [,1]         [,2]         [,3]
#> [1,] 0.218066183  0.002324327  0.012238585
#> [2,] 0.002324327  0.042358385 -0.006852241
#> [3,] 0.012238585 -0.006852241  0.285765709
#> 
#> [[2]]
#>              [,1]         [,2]         [,3]
#> [1,]  0.223865146 -0.004857413  0.018191272
#> [2,] -0.004857413  0.039843927 -0.009079676
#> [3,]  0.018191272 -0.009079676  0.236035903
#> 
#> [[3]]
#>             [,1]         [,2]         [,3]
#> [1,]  0.22024699 -0.010870387  0.023061513
#> [2,] -0.01087039  0.044112828 -0.009253676
#> [3,]  0.02306151 -0.009253676  0.245337212
#> 
#> [[4]]
#>             [,1]        [,2]        [,3]
#> [1,] 0.182328014 0.006104002 0.011988382
#> [2,] 0.006104002 0.042047254 0.005960284
#> [3,] 0.011988382 0.005960284 0.268431179
#> 
#> [[5]]
#>              [,1]         [,2]        [,3]
#> [1,]  0.220756776 -0.003540541 0.013888202
#> [2,] -0.003540541  0.042269002 0.001981767
#> [3,]  0.013888202  0.001981767 0.266068004
#> 
#> [[6]]
#>              [,1]        [,2]         [,3]
#> [1,]  0.198605502 -0.00774575  0.003669697
#> [2,] -0.007745750  0.04032928 -0.010448202
#> [3,]  0.003669697 -0.01044820  0.228310502
head(sim1$sigmaList)
#> [[1]]
#>           [,1]
#> [1,] 0.1011151
#> 
#> [[2]]
#>           [,1]
#> [1,] 0.1053129
#> 
#> [[3]]
#>           [,1]
#> [1,] 0.1039805
#> 
#> [[4]]
#>           [,1]
#> [1,] 0.1048026
#> 
#> [[5]]
#>            [,1]
#> [1,] 0.09919539
#> 
#> [[6]]
#>           [,1]
#> [1,] 0.1049618

It is also easy enough to create a plot to see what is going on with the simulation:


p1 <- plot(sim1) ## This returns a ggplot2 object

## you can tweak the plot by the standard ggplot commands
p1 + xlab("Time (hr)") + 
    ylab("Simulated Concentrations of TID steady state")


# And put the same plot on a semi-log plot
p1 + xlab("Time (hr)") + 
    ylab("Simulated Concentrations of TID steady state") +
    xgx_scale_y_log10()

For more complex simulations with variability you can also simulate dosing windows and sampling windows and use any tool you want to summarize it in the way you wish.