Regular lasso model

Regular lasso model

regularmodel(
  fit,
  varsVec,
  covarsVec,
  catvarsVec,
  constraint = 1e-08,
  lassotype = c("regular", "adaptive", "adjusted"),
  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.

lassotype

must be 'regular' , 'adaptive', 'adjusted'

stratVar

A variable to stratify on for cross-validation.

...

Other parameters to be passed to optimalTvaluelasso

Value

return fit of the selected 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")

# Model fit with regular lasso coefficients:

lassoDf <- regularmodel(fit,varsVec,covarsVec,catvarsVec)
# Model fit with adaptive lasso coefficients:

lassoDf <- regularmodel(fit,varsVec,covarsVec,catvarsVec,lassotype='adaptive')
# Model fit with adaptive-adjusted lasso coefficients:

lassoDf <- regularmodel(fit,varsVec,covarsVec,catvarsVec, lassotype='adjusted')
} # }