Create a gradient function based on gill numerical differences

Usage

nlmixr2Eval_(theta, md5)

nlmixr2Unscaled_(theta, md5)

nlmixr2Grad_(theta, md5)

nlmixr2ParHist_(md5)

nlmixr2GradFun(
  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: Max steps to determine the optimal forward/central difference step size per parameter (Gill 1983). `0` = no 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: Logical (or `NULL`) emit ANSI bold/color escapes in the iteration print. `NULL` (default) defers to [crayon::has_color()].
printNcol: Integer (or `NULL`) parameter columns per row before wrapping. `NULL` (default) uses `floor((getOption("width") - 23) / 12)`.
print: Either a scalar print-frequency (`0` = suppress, `1` (default) = every evaluation, `N` = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to `iterPrintControl(every = print, ncol = printNcol, useColor = useColor)`.

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 iteration or when print=X
gf <- nlmixr2GradFun(func0)

## x
x <- (0:10)*2*pi/10;
gf$eval(x)
#> [1] -1.224145e-16
gf$grad(x)
#>  [1]  1.0000000  0.8089234  0.3088979 -0.3091360 -0.8091106 -0.9999993
#>  [7] -0.8089234 -0.3088979  0.3091360  0.8091106  0.9999979

## x2
x2 <- x+0.1
gf$eval(x2)
#> [1] 0.09983342
gf$grad(x2)
#>  [1]  0.9949773  0.7461822  0.2123982 -0.4025390 -0.8637384 -0.9949001
#>  [7] -0.7461865 -0.2124014  0.4025369  0.8637373  0.9948221

## Gives the parameter history as a data frame
gf$hist()
#>   iter               type          objf        t1        t2        t3        t4
#> 1    1           Unscaled -1.224145e-16 0.0000000 0.6283185 1.2566371  1.884956
#> 2    2           Unscaled  9.983342e-02 0.1000000 0.7283185 1.3566371  1.984956
#> 3    1    Gill83 Gradient            NA 1.0000000 0.8089234 0.3088979 -0.309136
#> 4    2 Forward Difference            NA 0.9949773 0.7461822 0.2123982 -0.402539
#>           t5         t6         t7         t8        t9       t10       t11
#> 1  2.5132741  3.1415927  3.7699112  4.3982297 5.0265482 5.6548668 6.2831853
#> 2  2.6132741  3.2415927  3.8699112  4.4982297 5.1265482 5.7548668 6.3831853
#> 3 -0.8091106 -0.9999993 -0.8089234 -0.3088979 0.3091360 0.8091106 0.9999979
#> 4 -0.8637384 -0.9949001 -0.7461865 -0.2124014 0.4025369 0.8637373 0.9948221