# 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

 ```1 2``` ```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.

`mAdd1` and `mDrop1`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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.