xpose.gam: Stepwise GAM search for covariates on a parameter (Xpose 4)

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Stepwise GAM search for covariates on a parameter (Xpose 4)

Description

Function takes an Xpose object and performs a generalized additive model (GAM) stepwise search for influential covariates on a single model parameter.

Usage

xpose.gam(
  object,
  parnam = xvardef("parms", object)[1],
  covnams = xvardef("covariates", object),
  trace = TRUE,
  scope = NULL,
  disp = object@[e-mail]$disp,
  start.mod = object@[e-mail]$start.mod,
  family = "gaussian",
  wts.data = [e-mail],
  wts.col = NULL,
  steppit = object@[e-mail]$steppit,
  subset = xsubset(object),
  onlyfirst = object@[e-mail]$onlyfirst,
  medianNorm = object@[e-mail]$medianNorm,
  nmods = object@[e-mail]$nmods,
  smoother1 = object@[e-mail]$smoother1,
  smoother2 = object@[e-mail]$smoother2,
  smoother3 = object@[e-mail]$smoother3,
  smoother4 = object@[e-mail]$smoother4,
  arg1 = object@[e-mail]$arg1,
  arg2 = object@[e-mail]$arg2,
  arg3 = object@[e-mail]$arg3,
  arg4 = object@[e-mail]$arg4,
  excl1 = object@[e-mail]$excl1,
  excl2 = object@[e-mail]$excl2,
  excl3 = object@[e-mail]$excl3,
  excl4 = object@[e-mail]$excl4,
  extra = object@[e-mail]$extra,
  ...
)

Arguments

object	An xpose.data object.
parnam	ONE (and only one) model parameter name.
covnams	Covariate names to test on parameter.
trace	TRUE if you want GAM output to screen.
scope	Scope of the GAM search.
disp	If dispersion should be used in the GAM object.
start.mod	Starting model.
family	Assumption for the parameter distribution.
wts.data	Weights on the least squares fitting of parameter vs. covariate. Often one can use the variances of the individual parameter values as weights. This data frame must have column with name ID and any subset variable as well as the variable defined by the wts.col.
wts.col	Which column in the wts.data to use.
steppit	TRUE for stepwise search, false for no search.
subset	Subset on data.
onlyfirst	TRUE if only the first row of each individual's data is to be used.
medianNorm	Normalize to the median of parameter and covariates.
nmods	Number of models to examine.
smoother1	Smoother for each model.
smoother2	Smoother for each model.
smoother3	Smoother for each model.
smoother4	Smoother for each model.
arg1	Argument for model 1.
arg2	Argument for model 2.
arg3	Argument for model 3.
arg4	Argument for model 4.
excl1	Covariate exclusion from model 1.
excl2	Covariate exclusion from model 2.
excl3	Covariate exclusion from model 3.
excl4	Covariate exclusion from model 4.
extra	Extra exclusion criteria.
...	Used to pass arguments to more basic functions.

Value

Returned is a step.Gam object. In this object the step-wise-selected model is returned, with up to two additional components. There is an "anova" component corresponding to the steps taken in the search, as well as a "keep" component if the "keep=" argument was supplied in the call.

Author(s)

E. Niclas Jonsson, Mats Karlsson, Andrew Hooker & Justin Wilkins

See Also

step.gam

Other GAM functions: GAM_summary_and_plot, xp.get.disp(), xp.scope3(), xpose.bootgam(), xpose4-package

Examples

## Run a GAM using the example xpose database 
gam_ka <- xpose.gam(simpraz.xpdb, parnam="KA")

## Summarize GAM
xp.summary(gam_ka)

## GAM residuals of base model vs. covariates
xp.plot(gam_ka)

## An Akaike plot of the results
xp.akaike.plot(gam_ka)

## Studentized residuals
xp.ind.stud.res(gam_ka)

## Individual influence on GAM fit
xp.ind.inf.fit(gam_ka)

## Individual influence on GAM terms
xp.ind.inf.terms(gam_ka)

## Individual parameters to GAM fit
xp.cook(gam_ka)

xpose4 documentation built on May 31, 2022, 5:07 p.m.