Nothing
logit <- function(p) {
logit = log(p/(1 - p))
return(logit)
}
ExternalBinaryLogisticBiplot <- function(Pco, IncludeConst=TRUE, penalization=0.2, freq=NULL, tolerance = 1e-05, maxiter = 100) {
if (!(Pco$TypeData=="Binary")) stop("Data must be Binary for a External Binary Logistic Biplot")
n=dim(Pco$Data)[1]
p=dim(Pco$Data)[2]
dimens=dim(Pco$RowCoordinates)[2]
x=Pco$RowCoordinates
Pco$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=Pco$Data[,i]
fit=RidgeBinaryLogistic(y,x,tolerance = tolerance, maxiter = maxiter, penalization=penalization, cte=IncludeConst)
Pco$ColumnParameters[i,]=t(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)
}
rownames(Pco$ColumnParameters)=colnames(Pco$Data)
colnames(Pco$ColumnParameters)=paste("b",0:dimens, sep="")
esp = cbind(rep(1,n), Pco$RowCoordinates) %*% t(Pco$ColumnParameters)
pred = exp(esp)/(1 + exp(esp))
esp0 = matrix(rep(1,n), n,1) %*% Pco$ColumnParameters[, 1]
pred0 = exp(esp0)/(1 + exp(esp0))
d1 = -2 * apply(Pco$Data * log(pred0) + (1 - Pco$Data) * log(1 - pred0),2,sum)
d2 = -2 * apply(Pco$Data * log(pred) + (1 - Pco$Data) * log(1 - pred),2,sum)
d = d1 - d2
dd = sqrt(rowSums(cbind(1,Pco$ColumnParameters[, 2:(dimens + 1)])^2))
Res$Loadings = diag(1/dd) %*% Pco$ColumnParameters[, 2:(dimens + 1)]
Res$Tresholds = Pco$ColumnParameters[, 1]/d
Res$Communalities = rowSums(Res$Loadings^2)
Pco$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(Pco$Data)
Pco$VarInfo=Res
Pco$ClusterType="us"
Pco$Clusters = as.factor(matrix(1,nrow(Pco$RowCoordinates), 1))
Pco$ClusterColors="blue"
Pco$ClusterNames="Cluster1"
class(Pco)="External.Binary.Logistic.Biplot"
return(Pco)
}
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