Do visual predictive check (VPC) plots for nlme-based non-linear mixed effect models
vpc_nlmixr_nlme(fit, nsim = 100, condition = NULL, ...)
vpcNlmixrNlme(fit, nsim = 100, condition = NULL, ...)
# S3 method for nlmixrNlme
vpc(sim, ...)Arguments
- fit
nlme fit object
- nsim
number of simulations
- condition
conditional variable
- ...
Additional arguments
- sim
this is usually a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm. However it can also be an object like a nlmixr or xpose object
Value
Called for its side effects of creating a VPC
Examples
specs <- list(fixed=lKA+lCL+lV~1, random = pdDiag(lKA+lCL~1), start=c(lKA=0.5, lCL=-3.2, lV=-1))
fit <- nlme_lin_cmpt(theo_md, par_model=specs, ncmt=1, verbose=TRUE)
#>
#> **Iteration 1
#> LME step: Loglik: -420.4062, nlminb iterations: 1
#> reStruct parameters:
#> ID1 ID2
#> 5.434250 5.693011
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 300.8155
#> fixed effects: 0.2384012 1.057595 3.395177
#> iterations: 7
#> Convergence crit. (must all become <= tolerance = 1e-05):
#> fixed reStruct
#> 4.025732 4.507369
#>
#> **Iteration 2
#> LME step: Loglik: -422.6508, nlminb iterations: 5
#> reStruct parameters:
#> ID1 ID2
#> 0.9829979 1.4302117
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 300.5795
#> fixed effects: 0.2352729 1.058965 3.393164
#> iterations: 3
#> Convergence crit. (must all become <= tolerance = 1e-05):
#> fixed reStruct
#> 0.013296643 0.004640838
#>
#> **Iteration 3
#> LME step: Loglik: -422.666, nlminb iterations: 1
#> reStruct parameters:
#> ID1 ID2
#> 0.9821654 1.4298669
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 300.5795
#> fixed effects: 0.2352729 1.058965 3.393164
#> iterations: 1
#> Convergence crit. (must all become <= tolerance = 1e-05):
#> fixed reStruct
#> 0 0
vpc_nlmixr_nlme(fit, nsim = 100, condition = NULL)
