AddBinVars2Biplot <- function(bip, Y, IncludeConst=TRUE, penalization=0.2, freq=NULL, tolerance = 1e-05, maxiter = 100) {
n=dim(Y)[1]
p=dim(Y)[2]
dimens=dim(bip$RowCoordinates)[2]
x=bip$RowCoordinates
Pco=list()
Pco$ColumnParameters=matrix(0,p,dimens+1)
Res=list()
Res$Deviances=matrix(0,p,1)
Res$Dfs=matrix(0,p,1)
Res$pvalues=matrix(0,p,1)
Res$Bonferroni=matrix(0,p,1)
Res$Nagelkerke=matrix(0,p,1)
Res$R2=matrix(0,p,1)
Res$PercentsCorrec=matrix(0,p,1)
Pco$DevianceTotal=0
Pco$p=1
Pco$TotalPercent=0
for (i in 1:p){
y=Y[,i]
fit=RidgeBinaryLogistic(y,x,tolerance = tolerance, maxiter = maxiter, penalization=penalization, cte=IncludeConst)
if (IncludeConst)
Pco$ColumnParameters[i,]=fit$beta
else
Pco$ColumnParameters[i,]=c(0,fit$beta)
Res$Deviances[i]=fit$Dif
Res$Dfs[i]=fit$df
Res$pvalues[i]=fit$p
Res$Bonferroni[i]=(fit$p * p)* ((fit$p * p)<=1) + (((fit$p * p)>1))
Res$Nagelkerke[i]=fit$Nagelkerke
Res$R2[i]=fit$R2
Res$PercentsCorrec[i]=fit$PercentCorrect
Pco$TotalPercent=Pco$TotalPercent+sum(y==fit$Prediction)
}
if (IncludeConst)
rownames(Pco$ColumnParameters)=colnames(Pco$Data)
Pco$TotalPercent=Pco$TotalPercent/(n*p)
Pco$DevianceTotal=sum(Res$Deviances)
Pco$TotalDf=sum(Res$Dfs)
Pco$p=1-pchisq(Pco$DevianceTotal, df = Pco$TotalDf)
Res=as.data.frame(Res)
rownames(Res)=colnames(Y)
Pco$VarInfo=Res
class(Pco)="BinSupVarsBiplot"
bip$BinSupVarsBiplot=Pco
return(bip)
}
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