Nothing
gx.pearson <-
function(xx, log = FALSE, ifclr = FALSE, ifwarn = TRUE)
{
# Function to compute Pearson correlation coefficients and their
# significance for a matrix, rejecting any rows containing NAs,
# and display the results to 3 significant figures.
#
# NOTE: Prior to using this function the data frame/matrix containing the
# variables, xx, must be run through ltdl.fix.df to convert any <dl -ve
# values to positive half that value, and set zero2na = TRUE if it is
# required to convert any zero values or other numeric codes representing
# blanks to NAs.
#
if(!is.matrix(xx)) stop(deparse(substitute(xx)), " is not a Matrix")
# Remove any vectors containing NAs
temp.x <- remove.na(xx)
x <- temp.x$x
if(ifclr) log <- FALSE
if(log) {
x <- log10(x)
cat("Data have been Log10 transformed\n")
}
else if(ifclr) {
x <- clr(x, ifwarn = ifwarn)
cat("Data have been Centred Log-Ratio transformed\n")
}
#Convert data to SNDs and compute correlation matrix
z <- scale(x)
r <- (t(z) %*% z)/(temp.x$n - 1)
df.t <- temp.x$n - 2
df.term <- sqrt(df.t)
for(i in 2:temp.x$m) {
for(j in 1:(i - 1))
r[i, j] <- pt((abs(r[i, j]) * df.term)/
sqrt(1 - r[i, j] * r[i, j]), df.t)
}
r <- round(r, 3)
for(i in 1:temp.x$m) r[i,i] <- NA
cat("Pearson Correlation Coefficients and their Statistical Significance,",
"\nupper and lower triangles, respectively,",
paste("for matrix ", deparse(substitute(xx)), ", N = ", temp.x$n, "\n\n", sep = ""))
print(r, na.print = " ")
cat("\n")
invisible()
}
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