Create a gradient function based on gill numerical differences

nlmixrEval_(theta, md5)

nlmixrUnscaled_(theta, md5)

nlmixrGrad_(theta, md5)

nlmixrParHist_(md5)

nlmixrGradFun(
  what,
  envir = parent.frame(),
  which,
  thetaNames,
  gillRtol = sqrt(.Machine$double.eps),
  gillK = 10L,
  gillStep = 2,
  gillFtol = 0,
  useColor = crayon::has_color(),
  printNcol = floor((getOption("width") - 23)/12),
  print = 1
)

Arguments

theta: for the internal functions theta is the parameter values
md5: the md5 identifier for the internal gradient function information.
what: either a function or a non-empty character string naming the function to be called.
envir: an environment within which to evaluate the call. This will be most useful if what is a character string and the arguments are symbols or quoted expressions.
which: Which parameters to calculate the forward difference and optimal forward difference interval
thetaNames: Names for the theta parameters
gillRtol: The relative tolerance used for Gill 1983 determination of optimal step size.
gillK: The total number of possible steps to determine the optimal forward/central difference step size per parameter (by the Gill 1983 method). If 0, no optimal step size is determined. Otherwise this is the optimal step size determined.
gillStep: When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration the new step size = (prior step size)*gillStep
gillFtol: The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates.
useColor: Boolean indicating if focei can use ASCII color codes
printNcol: Number of columns to printout before wrapping parameter estimates/gradient
print: Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations.

Value

A list with `eval`, `grad`, `hist` and `unscaled` functions. This is an internal module used with dynmodel

Examples


func0 <- function(x){ sum(sin(x))  }

## This will printout every interation or when print=X
gf <- nlmixrGradFun(func0)

## x
x <- (0:10)*2*pi/10;
gf$eval(x)
#> |-----+---------------+-----------+-----------+-----------+-----------|
#> |    #| Objective Fun |        t1 |        t2 |        t3 |        t4 |
#> |.....................|        t5 |        t6 |        t7 |        t8 |
#> |.....................|        t9 |       t10 |       t11 |...........|
#> |    1|-2.2916210e-08 |     0.000 |    0.6283 |     1.257 |     1.885 |
#> |.....................|     2.513 |     3.142 |     3.770 |     4.398 |
#> |.....................|     5.027 |     5.655 |     6.283 |...........|
#> [1] -2.291621e-08
gf$grad(x)
#> |    G|    Gill Diff. |     1.000 |    0.8090 |    0.3090 |   -0.3090 |
#> |.....................|   -0.8090 |    -1.000 |   -0.8090 |   -0.3090 |
#> |.....................|    0.3090 |    0.8090 |     1.000 |...........|
#>  [1]  1.0000000  0.8090168  0.3090166 -0.3090170 -0.8090170 -1.0000000
#>  [7] -0.8090170 -0.3090170  0.3090170  0.8090170  1.0000000

## x2
x2 <- x+0.1
gf$eval(x2)
#> |    2|    0.10014530 |    0.1000 |    0.7283 |     1.357 |     1.985 |
#> |.....................|     2.613 |     3.241 |     3.870 |     4.498 |
#> |.....................|     5.127 |     5.755 |     6.383 |...........|
#> [1] 0.1001453
gf$grad(x2)
#> |    F| Forward Diff. |    0.9950 |    0.7463 |    0.2125 |   -0.4024 |
#> |.....................|   -0.8637 |   -0.9950 |   -0.7463 |   -0.2125 |
#> |.....................|    0.4024 |    0.8637 |    0.9950 |...........|
#>  [1]  0.9950042  0.7462946  0.2125259 -0.4024205 -0.8636559 -0.9950354
#>  [7] -0.7462947 -0.2125260  0.4024204  0.8636559  0.9949954

## Gives the parameter history as a data frame
gf$hist()
#>   iter               type          objf        t1        t2        t3
#> 1    1           Unscaled -2.291621e-08 0.0000000 0.6283185 1.2566371
#> 2    2           Unscaled  1.001453e-01 0.1000000 0.7283185 1.3566371
#> 3    1    Gill83 Gradient            NA 1.0000000 0.8090168 0.3090166
#> 4    2 Forward Difference            NA 0.9950042 0.7462946 0.2125259
#>           t4         t5         t6         t7        t8        t9       t10
#> 1  1.8849556  2.5132741  3.1415926  3.7699112  4.398230 5.0265482 5.6548667
#> 2  1.9849556  2.6132741  3.2412792  3.8699112  4.498230 5.1265482 5.7548667
#> 3 -0.3090170 -0.8090170 -1.0000000 -0.8090170 -0.309017 0.3090170 0.8090170
#> 4 -0.4024205 -0.8636559 -0.9950354 -0.7462947 -0.212526 0.4024204 0.8636559
#>         t11
#> 1 6.2831853
#> 2 6.3831853
#> 3 1.0000000
#> 4 0.9949954