CorReg (version 0.15.8)

CorReg-package: Algorithms for regression with correlated covariates

Description

Sequential linear regression based on a structural equation model(explicit correlations). It permits to face highly correlated datasets.

Details

CorReg: see www.correg.org for article and Phd Thesis about CorReg.

References

coming soon. see http://www.correg.org

Examples

#dataset generation
   base=mixture_generator(n=15,p=10,ratio=0.4,tp1=1,tp2=1,tp3=1,positive=0.5,
                          R2Y=0.8,R2=0.9,scale=TRUE,max_compl=3,lambda=1)
   X_appr=base$X_appr
   Y_appr=base$Y_appr
   Y_test=base$Y_test
   X_test=base$X_test

   TrueZ=base$Z#True generative structure (binary matrix)

   #density estimation for the MCMC
   density=density_estimation(X=X_appr,nbclustmax=10,detailed=TRUE)
   Bic_null_vect=density$BIC_vect

   #MCMC to find the structure
   res=structureFinder(X=X_appr,verbose=0,reject=0,Maxiter=900,
               nbini=30,candidates=-1,Bic_null_vect=Bic_null_vect,star=TRUE,p1max=15,clean=TRUE)
   hatZ=res$Z_opt
   hatBic=res$bic_opt

   #BIC comparison between true and found structure
   bicopt_vect=BicZ(X=X_appr,Z=hatZ,Bic_null_vect=Bic_null_vect)
   bicopt_vrai=BicZ(X=X_appr,Z=TrueZ,Bic_null_vect=Bic_null_vect)
   sum(bicopt_vect);sum(bicopt_vrai)

   #interpretation of found and true structure ordered by increasing R2
   readZ(Z=hatZ,crit="R2",X=X_appr,output="all",order=1)# <NA>line : name of subregressed covariate
   readZ(Z=TrueZ,crit="R2",X=X_appr,output="all",order=1)# <NA>line : name of subregressed covariate

   #Structure comparison
   compZ=compare_struct(trueZ=TrueZ,Zalgo=hatZ)#qualitative comparison

   #Regression coefficients estimation
    select="NULL"
   resY=correg(X=X_appr,Y=Y_appr,Z=hatZ,compl=TRUE,expl=TRUE,pred=TRUE,
               select=select,K=10,returning=TRUE)

   MSE_complete=MSE_loc(Y=Y_test,X=X_test,intercept=TRUE,A=resY$compl$A)
   MSE_explicative=MSE_loc(Y=Y_test,X=X_test,intercept=TRUE,A=resY$expl$A)
   MSE_predictive=MSE_loc(Y=Y_test,X=X_test,intercept=TRUE,A=resY$pred$A)
   MSE_vrai=MSE_loc(Y=Y_test,X=X_test,intercept=TRUE,A=base$A)

   MSE=data.frame(MSE_complete,MSE_explicative,MSE_predictive,MSE_vrai)
   MSE#estimated structure
   compZ$true_left;compZ$false_left
   barplot(as.matrix(MSE),main="MSE on validation dataset", sub=paste("select=",select))
  abline(h=MSE_complete,col="red")