Nothing
summary.PCA.ContCont <- function(object, ..., Object){
if (missing(Object)){Object <- object}
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$Call)
cat("\n\n# Total number of matrices that can be formed by the specified vectors and/or scalars")
cat("\n# of the correlations in the function call")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat(Object$Total.Num.Matrices)
cat("\n\n# Total number of positive definite matrices")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat(nrow(Object$Pos.Def))
cat("\n\n\n# Predictive causal association (PCA) results summary")
cat("\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n")
cat("Mean (SD) PCA: ", format(round(mean(Object$PCA), 4), nsmall = 4), " (", format(round(sd(Object$PCA), 4), nsmall = 4), ")",
" [min: ", format(round(min(Object$PCA), 4), nsmall = 4), "; max: ", format(round(max(Object$PCA), 4), nsmall = 4), "]", sep="")
cat("\nMode PCA: ", format(round(mode(Object$PCA)$mode_val, 4), nsmall = 4))
cat("\n\nQuantiles of the PCA distribution: \n\n")
quant <- quantile(Object$PCA, probs = c(.05, .10, .20, .50, .80, .90, .95))
print(quant)
}
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