R/EBlassoNEG.Binomial.R

In EBglmnet: Empirical Bayesian Lasso and Elastic Net Methods for Generalized Linear Models

EBlassoNEG.Binomial <-function(BASIS,Target,a_gamma,b_gamma,verbose = 0){
	N 				= nrow(BASIS);
	K 				= ncol(BASIS);
	Epis = FALSE;

	if (verbose>0)  cat("Empirical Bayes Lasso Logistic Model(Normal + Exponential + Gamma prior)\n");
	if(!Epis) {
		N_effect 		= K;
		Beta 			= rep(0,N_effect *4);

		output<-.C("fEBBinaryMainEff",
			BASIS 	= as.double(BASIS),
			Target 	= as.double(Target),
			a_gamma 	= as.double(a_gamma),
			b_gamma 	= as.double(b_gamma),
			logLikelihood = as.double(0),
			Beta 		= as.double(Beta),
			WaldScore 	= as.double(0),
			Intercept 	= as.double(rep(0,2)),
			N 		= as.integer(N),
			K 		= as.integer(K),
			verbose 	= as.integer(verbose),			
			PACKAGE="EBglmnet");
	}

	result 			= matrix(output$Beta,N_effect,4);

	ToKeep 			= which(result[,3]!=0);
	if(length(ToKeep)==0) { Blup = matrix(0,1,4)
	}else
	{
		nEff 	= length(ToKeep);
		Blup 		= result[ToKeep,,drop=FALSE];
	}
	
	#t- test:
	t 				= abs(Blup[,3])/(sqrt(Blup[,4])+ 1e-20);
pvalue 			= 2*(1- pt(t,df=(N-1)));
	blup 			= cbind(Blup[,2:4,drop = FALSE],t,pvalue); 			#M x 6
	colnames(blup) = c("predictor","beta","posterior variance","t-value","p-value");	

	hyperparameters = c(a_gamma, b_gamma);
	names(hyperparameters) = c("a", "b");
	fEBresult 			<- list(blup,output$logLikelihood,output$WaldScore,output$Intercept[1],hyperparameters);
	rm(list= "output")	
	names(fEBresult)		<-c("fit","logLikelihood","WaldScore","Intercept","hyperparameters")
	return(fEBresult)
	
}