xpose4 (version 4.5.3)

xpose.gam: 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 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,
           ...)
           
xp.get.disp(gamdata,
           parnam,
           covnams,
           family = "gaussian",
           ...)

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.

gamdata

Data for the GAM. A data frame.

…

Used to pass arguments to more basic functions.

Value

Returned is a step.gam object

Details

xpose.gam performs a stepwise GAM search for influential covariates. xp.get.disp is a helper function for calculating dispersion, and is not intended to be used by itself.

See Also

xp.gam, step.gam

Examples

## We expect to find the required NONMEM run and table files for run
## 5 in the current working directory
xpdb <- xpose.data(5)

## Here we load the example xpose database 
data(simpraz.xpdb)
xpdb <- simpraz.xpdb

xpose.gam(xpdb, parnam="CL", covnams = xvardef("covariates", xpdb))