selec_stepwise: stepwise model selection

In yorickuser/contselec: Model selection procesure by contracting explanatry variables, with check of each explanatry variable's strength.

Description

This function conducts stepwise model selection, using the best model selected by "selec_bestsub" as the initial model.

Usage

selec_stepwise(result, data0, edge_param_number, family, scale01 = FALSE)

Arguments

result	data.frame : result output of "selec_bestsub"
data0	data.frame : explained variable "y" and uncontracted explanatory variables.
edge_param_number	real number: this parameter constrains the number of explanatory variables in the subset model selection and stepwise model selection, so that [sample size]/[number of free parameter] >= "edge_param_number".
family	character : error distribution. Possible choices are "glmm_poisson", "poisson","gaussian".
scale01	TRUE/FALSE : if TRUE, explanatory variables are scaled in advance so that their ranges are from 0 to 1.

Value

list(result=result,bestmodel=bestmodel)

Author(s)

Hiroshi C. Ito

Examples

data(Cars93, package = "MASS");
data=Cars93;
data=data[complete.cases(data),];
data=data[,sapply(data[1,],is.numeric)];
con=contract(data,edge_cor=0.9,edge_explain=0.6,target="Horsepower");
data1=con$data1;
for(i in 2:ncol(data1))data1[,i]=(data1[,i]-min(data1[,i]))/(max(data1[,i])-min(data1[,i]));

res_sb=selec_bestsub(con$data0,data1,con$group,edge_param_number=3,repeatn=50,family="gaussian",target=con$target, use_pforeach=FALSE,perm=TRUE,daic=2.0);
print(res_sb$result);

res_ss=selec_stepwise(res_sb$result,con$data0,edge_param_number=3,family=res_sb$bestmodel$family,scale01=TRUE);
print(put_sigmark(res_ss$result));

yorickuser/contselec documentation built on July 25, 2021, 8:14 a.m.