runLinearModel: Run the linear model using the selected variables

In haimbar/SEMMS: Variable selection in Generalized Linear Models - an empirical Bayes approach

Description

Run glm with the selected variables (identified by nnt) as predictors.

Usage

runLinearModel(dat, nnt, distr, func = "glm")

Arguments

dat	The dataset, as generated by readInputFile().
nnt	The column numbers in the Z matrix of the non-null variables.
distr	The distribution to use when fitting the linear model (N=gaussian, P=poisson, or B=binomial).
func	The function to use when fitting the model: glm or lm (default=glm).

Value

Z	A numeric matrix of putative variables (in the columns)
mod	The object returned from glm or lm.
aic	The AIC of the model.
vif	The variance inflation factors (if more than one variable is used in the model).

Examples

## Not run: 
fn <- system.file("extdata", "AR1SIM.RData", package = "SEMMS", mustWork = TRUE)
dataYXZ <- readInputFile(fn, ycol=1, Zcols=2:100)
fittedSEMMS <- fitSEMMS(dataYXZ, mincor=0.8, nn=15, minchange= 1,
                        distribution="N",verbose=T,rnd=F)
fittedGLM <- runLinearModel(dataYXZ,fittedSEMMS$gam.out$nn, "N")
## End(Not run)

haimbar/SEMMS documentation built on Dec. 20, 2021, 2:44 p.m.