mStep: Model selection in multivariate multiple regression

In qtlmt: Tools for Mapping Multiple Complex Traits

Description

Select a multivariate multiple regression model via model selection.

Usage

mStep(object, scope, direction=c("both","backward","forward"),
   trace=FALSE, keep=TRUE, steps=1000, k=2, ...)

Arguments

object	initial model in model search.
scope	a single formula, which provides ‘upper’, or a list containing components ‘upper’ and ‘lower’, both formulae; defines the lower and upper bound. See step.
direction	forward selection, backward elimination or stepwise.
trace	whether to track the process for monitoring purpose.
keep	whether to return the change of terms and related statistics.
steps	maximum number of search steps.
k	penalty on a parameter. The selection criterion is the known "AIC" if k = 2 and is "BIC" if k = log(n) where "n" is the sample size.
...	additional arguments to update.

Value

a list with components of a lm object plus ‘keep’ if required.

See Also

mAdd1 and mDrop1

Examples

data(etrait)
mdf<- data.frame(traits,markers)
## Not run: 
mlm<- lm(cbind(T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11,T12,T13,T14,T15,T16) ~
   m1 + m2 + m3 + m4 + m5, data=mdf)

lw<- formula(paste("~ ", paste("m",1:3,collapse=" + ",sep="")))
up<- formula(paste("~", paste("m",1:15,collapse=" + ",sep="")))

ob<- mStep(mlm, scope=list(lower=lw), k=99, direction="backward", data=mdf)
of<- mStep(mlm, scope=list(upper=up), k=5, direction="forward", data=mdf)
o1<- mStep(mlm, scope=list(upper=up), k=5, direction="both", data=mdf)
o2<- mStep(o1, scope=list(upper=up), k=2, direction="forward", data=mdf)

## End(Not run)

Example output

R/qtlmt is loaded

qtlmt documentation built on May 2, 2019, 2:23 p.m.