check_interaction: Checks interactions in the result output of "cont_selec"

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

Description

This function check interactions among statistically-strong explanatory variables in the result output of "cont_selec"

Usage

check_interaction(res, edge_param_number = 3, edge_cor = 1,
  omit_contracted = TRUE, repeatn = 50, use_pforeach = TRUE,
  perm = TRUE, check_spa_cor = FALSE)

Arguments

res	list : result output of "cont_selec".
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".
edge_cor	real number: explanatory variables in result with absolute correlations higher than "edge_cor" are printed out.
omit_contracted	TRUE/FALSE : if TRUE, this function checks interactions among only explanatory variables that are uncontracted ones (i.e., res$group$ngid==1).
repeatn	integer : the number of model evaluations proccessed as a single job for each CPU core (This parameter is meaningfur only when parallel proccessing is conducted by library "pforeach").
use_pforeach	TRUE/FALSE : if TRUE, pforeach is used instead of foreach.
perm	TRUE/FALSE : if TRUE, permutation tests for explained variables are conducted.
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.
check_spa_cor_nmodel	integer : for the top [check_spa_cor_nmodel] models, spatial correlation of residuals are checked.

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)];
res=cont_selec(data,target="Horsepower",edge_cor=0.9,edge_explain=0.6,
               edge_param_number=3,family="gaussian",use_pforeach=FALSE);

res_int=check_interaction(res,edge_cor=0.9,use_pforeach=FALSE)
plot_each_effect(res_int)
plot_combined_effect(res_int,sign_effect=-1)
plot_combined_effect(res_int,sign_effect=1)

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