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#' @method summary bootstrapValidation_Bin
summary.bootstrapValidation_Bin <-
function(object, ...)
{
digits = 3
conf.int = 0.95
cilow = (1.0-conf.int)/2;
cihig = 1.0-cilow;
ciAccuracy <- as.vector(quantile(object$train.accuracy, probs = c(cilow,0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
ciSensitivity <- as.vector(quantile(object$train.sensitivity, probs = c(cilow,0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
ciSpecificity <- as.vector(quantile(object$train.specificity, probs = c(cilow,0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
citROC <- as.vector(quantile(object$train.ROCAUC, probs = c(cilow,0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
bootAuc <- pROC::roc( as.vector(object$outcome), object$boot.model$linear.predictors,plot=FALSE,ci=TRUE,boot.n=object$loops,quiet = TRUE);
cat("\nModel Cross-Validation with Improvement in Predicted Probability\n\n")
cat("Number of Cases:", sum(object$outcome), "\t Number of Controls", sum(object$outcome==0), "\n\n")
cat("Number of Bootstraps:", length(object$train.ROCAUC), "\t Sampled Fraction", object$fraction, "\n\n")
performance <- vector();
cat(l1 <- sprintf("Blind Accuracy: %8.3f : Bootstrapped Accuracy: %8.3f (%8.3f to %8.3f) \n",object$blind.accuracy,ciAccuracy[2],ciAccuracy[1], ciAccuracy[3]));
performance <- append(performance,l1);
cat(l1 <- sprintf("Blind Sensitivity: %8.3f : Bootstrapped Sensitivity: %8.3f (%8.3f to %8.3f) \n",object$blind.sensitivity,ciSensitivity[2],ciSensitivity[1], ciSensitivity[3]));
performance <- append(performance,l1);
cat(l1 <- sprintf("Blind Specificity: %8.3f : Bootstrapped Specificity: %8.3f (%8.3f to %8.3f) \n",object$blind.specificity,ciSpecificity[2],ciSpecificity[1], ciSpecificity[3]));
performance <- append(performance,l1);
cat(l1 <- sprintf("Blind ROC AUC: %8.3f : Bootstrapped ROC AUC: %8.3f (%8.3f to %8.3f) \n",object$blind.ROCAUC$auc,object$boot.ROCAUC$auc,bootAuc$ci[1],bootAuc$ci[3]));
performance <- append(performance,l1);
cat(l1 <- sprintf("Blind ROC AUC: %8.3f : ModelBootstrap ROC AUC: %8.3f (%8.3f to %8.3f) \n\n",object$blind.ROCAUC$auc,citROC[2],citROC[1],citROC[3]));
performance <- append(performance,l1);
performance.table <- rbind(c(object$blind.accuracy,ciAccuracy[2],ciAccuracy[1], ciAccuracy[3]));
performance.table <- rbind(performance.table,c(object$blind.sensitivity,ciSensitivity[2],ciSensitivity[1], ciSensitivity[3]));
performance.table <- rbind(performance.table,c(object$blind.specificity,ciSpecificity[2],ciSpecificity[1], ciSpecificity[3]));
performance.table <- rbind(performance.table,c(object$blind.ROCAUC$auc,object$boot.ROCAUC$auc,bootAuc$ci[1],bootAuc$ci[3]));
performance.table <- rbind(performance.table,c(object$blind.ROCAUC$auc,citROC[2],citROC[1],citROC[3]));
colnames(performance.table) <- c("Blind","Train","LCI","UCI")
rownames(performance.table) <- c("Accuracy","Sensitivity","Specificity","ROCAUC 1","ROCAUC 2")
# smry <- summary(object$boot.model, ...);
meancoef <- colMeans(object$s.coef,na.rm = TRUE);
ncoef <- length(meancoef);
lowci <- numeric(ncoef);
topci <- numeric(ncoef);
zidimedian <- numeric(ncoef);
idimedian <- numeric(ncoef);
idilowci <- numeric(ncoef);
iditopci <- numeric(ncoef);
znrimedian <- numeric(ncoef);
nrimedian <- numeric(ncoef);
nrilowci <- numeric(ncoef);
nritopci <- numeric(ncoef);
soffset <- ncoef-ncol(object$IDIs);
for ( i in 1:ncoef)
{
ci <- as.vector(quantile(object$s.coef[,i], probs = c(cilow, cihig), na.rm = TRUE,names = FALSE, type = 7));
lowci[i] <- ci[1];
topci[i] <- ci[2];
j <- i-soffset;
if (j>0)
{
ci <- as.vector(quantile(object$IDIs[,j], probs = c(cilow, 0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
idimedian[i] <- ci[2];
idilowci[i] <- ci[1];
iditopci[i] <- ci[3];
ci <- as.vector(quantile(object$z.IDIs[,j], probs = c(cilow, 0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
zidimedian[i] <- ci[2];
ci <- as.vector(quantile(object$NRIs[,j], probs = c(cilow, 0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
nrimedian[i] <- ci[2];
nrilowci[i] <- ci[1];
nritopci[i] <- ci[3];
ci <- as.vector(quantile(object$z.NRIs[,j], probs = c(cilow, 0.5, cihig), na.rm = TRUE,names = FALSE, type = 7));
znrimedian[i] <- ci[2];
}
}
p <- meancoef;
p <- cbind(p,lowci);
p <- cbind(p,topci);
p <- cbind(p,idimedian);
p <- cbind(p,idilowci);
p <- cbind(p,iditopci);
p <- cbind(p,zidimedian);
p <- cbind(p,nrimedian);
p <- cbind(p,nrilowci);
p <- cbind(p,nritopci);
p <- cbind(p,znrimedian);
print(p)
print(colnames(object$s.coef))
cnames <- c("Coef","Low CI","High CI","Median IDI","Low IDI","High IDI","z IDI","Median NRI","Low NRI","High NRI","z NRI");
colnames (p) <- cnames;
rownames(p) <- colnames(object$s.coef);
print(p, digits = digits);
result <- list(performance = performance,
coef = p,
performance.table = performance.table);
return (result);
}
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