Return Adjusted adaptive lasso coefficients after finding optimal t

Return Adjusted adaptive lasso coefficients after finding optimal t

Usage

adjustedlassoCoefficients(
  fit,
  varsVec,
  covarsVec,
  catvarsVec,
  constraint = 1e-08,
  stratVar = NULL,
  ...
)

Arguments

fit

nlmixr2 fit.

varsVec

character vector of variables that need to be added

covarsVec

character vector of covariates that need to be added

catvarsVec

character vector of categorical covariates that need to be added

constraint

theta cutoff. below cutoff then the theta will be fixed to zero.

stratVar

A variable to stratify on for cross-validation.

...

Other parameters to be passed to optimalTvaluelasso

Value

return data frame of final lasso coefficients

Author

Vishal Sarsani

Examples

if (FALSE) { # \dontrun{
one.cmt <- function() {
  ini({
    tka <- 0.45; label("Ka")
    tcl <- log(c(0, 2.7, 100)); label("Cl")
    tv <- 3.45; label("V")
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    add.sd <- 0.7
  })
  model({
    ka <- exp(tka + eta.ka)
    cl <- exp(tcl + eta.cl)
    v <- exp(tv + eta.v)
    linCmt() ~ add(add.sd)
  })
}

d <- nlmixr2data::theo_sd
d$SEX <-0
d$SEX[d$ID<=6] <-1

fit <- nlmixr2(one.cmt, d, est = "saem", control = list(print = 0))
varsVec <- c("ka","cl","v")
covarsVec <- c("WT")
catvarsVec <- c("SEX")

# Adaptive Lasso coefficients:

lassoDf <- adjustedlassoCoefficients(fit,varsVec,covarsVec,catvarsVec)
} # }