selec_bestsub_stepwise: conducts best subset model selection, permutation test and...

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

Description

This function conducts best subset model selection, permutation tests and stepwise model selection. Whether explanatory variables in the best model have statistically strong effect on the explained variable is examined.

Usage

selec_bestsub_stepwise(data0, data1, group, pos_x = NULL, pos_y = NULL,
  edge_param_number = 3, repeatn = 100, family = "glmm_poisson",
  check_spa_cor = FALSE, target = "", use_pforeach = TRUE,
  perm = TRUE, nrep = 10000)

Arguments

data0	data.frame : uncontracted data
data1	data.frame : constracted data
group	(list(gid,gid_data1,ngid,vname1_orig,vname0_orig)) : information about contraction of "data0" into "data1".
pos_x	array of numeric : x-coordinates of spatial position for each sampled data in "data00".
pos_y	array of numeric : y-coordinates of spatial position for each sampled data in "data00".
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".
repeatn	integer : the number of model evaluations proccessed as a single job for each CPU core (This parameter is meaningful only when "use_pforeach" is TRUE).
family	character : error distribution. Possible choices are "glmm_poisson", "poisson","gaussian".
check_spa_cor	TRUE/FALSE : if TRUE, the best model with residuals significantly correlated with c(pos_x, pos_y) are removed repeatedly until the correlation becomes non-significant.
target	character : arbitrary name for explained variable "y".
use_pforeach	TRUE/FALSE : if TRUE, pforeach is used instead of foreach in best subset selection and permutation tests (when "perm" is TRUE).
perm	TRUE/FALSE : if TRUE, permutation tests for explained variables are conducted.
nrep	integer : number of resampling in the permutation test.

Details

Details.

Value

list(target=target,result=result,data0=data0,data1=data1,group=group, family=family,bestmodels=res_sb$bestmodels,bestmodel_stepwise=res_ss$bestmodel, bestmodel=res_sb$bestmodel,pos_x=pos_x,pos_y=pos_y);

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=selec_bestsub_stepwise(con$data0,data1,con$group,
edge_param_number=3,repeatn=50,family="gaussian",
target=con$target, use_pforeach=FALSE,perm=TRUE);

print(res);
summary(res);

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