Nothing
summary.ICA.BinBin <- function(object, ..., Object){
options(digits = 4)
if (missing(Object)){Object <- object}
if (Object$Monotonicity!="General"){
# if (missing(Object)){Object <- object}
Object$R2_H <- na.exclude(Object$R2_H)
Object$Theta_T <- na.exclude(Object$Theta_T)
Object$Theta_S <- na.exclude(Object$Theta_S)
mode <- function(data) {
x <- data
z <- density(x)
mode_val <- z$x[which.max(z$y)]
fit <- list(mode_val= mode_val)
}
cat("\nFunction call:\n\n")
print(Object$Call)
cat("\n# Total number of valid Pi vectors")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat(dim(Object$Pi.Vectors)[1])
cat("\n\n\n# R2_H results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) R2_H: ", format(round(mean(Object$R2_H), 4), nsmall = 4), " (", format(round(sd(Object$R2_H), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$R2_H), 4), nsmall = 4), "; max: ", format(round(max(Object$R2_H), 4), nsmall = 4), "]", sep="")
cat("\nMode R2_H: ", format(round(mode(Object$R2_H)$mode_val, 4), nsmall = 4))
cat("\n\nQuantiles of the R2_H distribution: \n\n")
quant <- quantile(Object$R2_H, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
cat("\n\n\n# R_H results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) R_H: ", format(round(mean(sqrt(Object$R2_H)), 4), nsmall = 4), " (", format(round(sd(sqrt(Object$R2_H)), 4), nsmall = 4), ")",
" [min: ", format(round(min(sqrt(Object$R2_H)), 4), nsmall = 4), "; max: ", format(round(max(sqrt(Object$R2_H)), 4), nsmall = 4), "]", sep="")
cat("\nMode R_H: ", format(round(mode(sqrt(Object$R2_H))$mode_val, 4), nsmall = 4))
cat("\n\nQuantiles of the R_H distribution: \n\n")
quant <- quantile(sqrt(Object$R2_H), probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
# cat("\n\n# C3 results summary")
# cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
# cat("Mean (SD) C3: ", format(round(mean(Object$C3), 4), nsmall = 4), " (", format(round(sd(Object$C3), 4), nsmall = 4), ")",
# " [min: ", format(round(min(Object$C3), 4), nsmall = 4), "; max: ", format(round(max(Object$C3), 4), nsmall = 4), "]", sep="")
# cat("\nMode C3: ", format(round(mode(Object$C3)$mode_val, 4), nsmall = 4))
# cat("\n\nQuantiles of the C3 distribution: \n\n")
# quant <- quantile(Object$C3, probs = c(.05, .10, .20, .50, .80, .90, .95))
# print(quant)
cat("\n\n# Theta_T results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) Theta_T: ", format(round(mean(Object$Theta_T), 4), nsmall = 4), " (", format(round(sd(Object$Theta_T), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$Theta_T), 4), nsmall = 4), "; max: ", format(round(max(Object$Theta_T), 4), nsmall = 4), "]", sep="")
if (Object$Theta_T[1]!=Inf) {
cat("\nMode Theta_T: ", format(round(mode(Object$Theta_T)$mode_val, 4), nsmall = 4))}
cat("\n\nQuantiles of the Theta_T distribution: \n\n")
quant <- quantile(Object$Theta_T, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
cat("\n\n# Theta_S results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) Theta_S: ", format(round(mean(Object$Theta_S), 4), nsmall = 4), " (", format(round(sd(Object$Theta_S), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$Theta_S), 4), nsmall = 4), "; max: ", format(round(max(Object$Theta_S), 4), nsmall = 4), "]", sep="")
if (Object$Theta_S[1]!=Inf) {
cat("\nMode Theta_S: ", format(round(mode(Object$Theta_S)$mode_val, 4), nsmall = 4))}
cat("\n\nQuantiles of the Theta_S distribution: \n\n")
quant <- quantile(Object$Theta_S, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
}
if (Object$Monotonicity=="General"){
Object$R2_H <- na.exclude(Object$R2_H)
Object$Theta_T <- na.exclude(Object$Theta_T)
Object$Theta_S <- na.exclude(Object$Theta_S)
cat("\nFunction call:\n\n")
print(Object$Call)
cat("\n# Number of valid Pi vectors")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Total:", dim(Object$Pi.Vectors)[1])
cat("\n\nIn the different montonicity scenarios:")
print(table(Object$Pi.Vectors$Monotonicity))
results <- cbind.data.frame(Object$Pi.Vectors$Monotonicity, Object$R2_H)
cat("\n\n# Summary of results obtained in different monotonicity scenarios")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("# R2_H results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~\n\nMean:\n")
print(tapply(results[,2], list(results[,1]), mean))
pc50 <- function(x=x){
quantile(x = x, probs = .5, na.rm = T)}
cat("\nMedian:\n")
print(tapply(results[,2], list(results[,1]), pc50))
mode <- function(data) {
x <- data
z <- density(x)
mode_val <- z$x[which.max(z$y)]
fit <- list(mode_val= mode_val)
}
cat("\nMode:\n")
print(data.frame(t(tapply(results[,2], list(results[,1]), mode)), row.names=""))
cat("\nSD:\n")
print(tapply(results[,2], list(results[,1]), sd))
cat("\nMin:\n")
print(tapply(results[,2], list(results[,1]), min))
cat("\nMax:\n")
print(tapply(results[,2], list(results[,1]), max))
results <- cbind.data.frame(Object$Pi.Vectors$Monotonicity, Object$Theta_T)
cat("\n\n# Theta_T results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~\n\nMean:\n")
print(tapply(results[,2], list(results[,1]), mean))
cat("\nMedian:\n")
print(tapply(results[,2], list(results[,1]), pc50))
cat("\nSD:\n")
print(tapply(results[,2], list(results[,1]), sd))
cat("\nMin:\n")
print(tapply(results[,2], list(results[,1]), min))
cat("\nMax:\n")
print(tapply(results[,2], list(results[,1]), max))
results <- cbind.data.frame(Object$Pi.Vectors$Monotonicity, Object$Theta_S)
cat("\n\n# Theta_S results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~\n\nMean:\n")
print(tapply(results[,2], list(results[,1]), mean))
cat("\nMedian:\n")
print(tapply(results[,2], list(results[,1]), pc50))
cat("\nSD:\n")
print(tapply(results[,2], list(results[,1]), sd))
cat("\nMin:\n")
print(tapply(results[,2], list(results[,1]), min))
cat("\nMax:\n")
print(tapply(results[,2], list(results[,1]), max))
}
}
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