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test.diff.cor.R
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#' Test for difference in correlation
#'
#' This functions tests the hypothesis of no difference in correlations. It uses
#' the Fisher \eqn{Z} transform (\code{atanh}) to test the null hypothesis
#' of no difference in correlations. See details.
#'
#' @details
#' The function uses the Fisher \eqn{Z} transform (\code{atanh}) of
#' correlations to test that the hypotheses of no difference in correlation.
#' The computed \eqn{Z}-score is
#' \deqn{\frac{Z_1 - Z_2}{\sqrt{1/(n_1 - 3) + 1/(n_2 - 3))}}}{
#' (Z_1 - Z_2)/ sqrt(1/(n_1 - 3) + 1/(n_2 - 3))}
#' where \eqn{Z_1} and \eqn{Z_2} are the Fisher transformed correlations.
#' It performs the test for all correlations in the correlation matrix.
#'
#' @param X1 A \code{numeric} \code{matrix} of observations.
#' @param X2 A \code{numeric} \code{matrix} of observations.
#' @param cor1 A \code{numeric} \code{matrix} of correlation coefficients in
#' the first group. May be omitted if \code{X1} is provided.
#' @param cor2 A \code{numeric} \code{matrix} of correlation coefficients in
#' the second group. May be omitted if \code{X2} is provided.
#' @param n1 \code{integer} of length 1. The number of samples in group 1.
#' @param n2 \code{integer} of length 1. The number of samples in group 2.
#' @param alternative The alternative hypothesis.
#' @param conf.level The confidence level used in the computed confidence
#' intervals.
#' @param null A matrix of number giving the difference in correlation under
#' the null hypothesis.
#' @return A list of matrices or vector containing:
#' \item{\code{LCL}}{The lower confidence interval limit.}
#' \item{\code{UCL}}{The upper confidence interval limit.}
#' \item{\code{z}}{A numeric matrix of Z-scores for the hypothesis.}
#' \item{\code{p.val}}{A numeric matrix of the P-values.}
#' with an attribute giving the alternative hypothesis.
#' @references
#' \url{http://core.ecu.edu/psyc/wuenschk/docs30/CompareCorrCoeff.pdf}
#' @author Anders Ellern Bilgrau <anders.ellern.bilgrau (at) gmail.com>
#' @seealso
#' Similar usage to \code{\link[stats]{cor.test}} (but NOT the same thing).\cr
#' This is a vectorised version of \code{\link{test.diff.cor.single}}.
#' @examples
#' n1 <- 8
#' n2 <- 10
#' X1 <- createData(n = n1, m = 5)
#' X2 <- createData(n = n2, m = 5)
#'
#' print(cor1 <- cor(X1))
#' print(cor2 <- cor(X2))
#'
#' test.diff.cor(X1, X2)
#'
#' # Directly supplied correlation matrices
#' test.diff.cor(cor1 = cor1, cor2 = cor2, n1 = n1, n2 = n2)
#'
#' test.diff.cor(X1, X2, alternative = "less")
#' @export test.diff.cor
test.diff.cor <- function(X1, X2,
cor1 = cor(X1),
cor2 = cor(X2),
n1 = nrow(X1),
n2 = nrow(X2),
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95,
null = 0) {
if (!missing(X1) & !missing(X2)) {
stopifnot(ncol(X1) == ncol(X2))
}
alternative <- match.arg(alternative)
# Fisher z transform the correlations (atanh is the Fisher Z transform)
Z1 <- atanh(cor1)
Z2 <- atanh(cor2)
# Compute Z.scores for diff. coexpression
estimate <- Z1 - Z2
diag(estimate) <- 0
se <- sqrt(1/(n1 - 3) + 1/(n2 - 3))
# Perform the Fisher test
ans <- fisher.test.cor(estimate = estimate, mean = atanh(null), se = se,
alternative = alternative, conf.level = conf.level)
attr(ans, "alternative") <- alternative
return(ans)
}
#' Test for difference in correlation
#'
#' This functions uses the Fisher Z-transform (atanh) to test the null hypothesis
#' of no difference in correlations between x1 and y1 versus x2 and y2.
#'
#' @param x1 numeric vector, x-values for the first sample.
#' @param y1 numeric vector, y-values for the first sample.
#' @param x2 numeric vector, x-values for the second sample.
#' @param y2 numeric vector, y-values for the second sample.
#' @return A numeric vector giving correlation for each group,
#' size-estimate and standard error, confidence intervals and p-values.
#' @details
#' The \code{alternative} argument specifies the alternative hypothesis given
#' below.
#' \tabular{rcl}{
#' \tab \tab
#' H0: \code{cor(x1, y1) = cor(x2, y2)} \cr
#' \code{"two.sided"} \tab =>\tab
#' H1: \code{cor(x1, y1) != cor(x2, y2)} \cr
#' \code{"greater"} \tab =>\tab
#' H1: \code{cor(x1, y1) > cor(x2, y2)} \cr
#' \code{"less"} \tab =>\tab
#' H1: \code{cor(x1, y1) < cor(x2, y2)}
#' }
#' @author Anders Ellern Bilgrau <anders.ellern.bilgrau (at) gmail.com>
#' @seealso
#' Similar usage to \code{\link[stats]{cor.test}} in \code{stats}, however
#' not the same! \cr
#' See \code{\link{test.diff.cor}} for a vectorised version.
#' @examples
#' x1 <- rnorm(100)
#' y1 <- rnorm(100)
#' x2 <- rnorm(110)
#' y2 <- 4*x2 + 0.5*rnorm(110) + 1
#'
#' plot(x1, y1, xlim = range(x1, x2), ylim = range(y1, y2))
#' abline(lm(y1 ~ x1))
#' points(x2, y2, col = "red")
#' abline(lm(y2 ~ x2), col = "red")
#'
#' diff.test <- correlateR:::test.diff.cor.single
#' round(data.frame(
#' two = diff.test(x1, y1, x2, y2, alternative = "two.sided"),
#' les = diff.test(x1, y1, x2, y2, alternative = "less"),
#' gre = diff.test(x1, y1, x2, y2, alternative = "greater")), 2)
#'
#' round(diff.test(x1, y1, x1, y1, alternative = "two.sided"), 3)
#' round(diff.test(x1, y1, x1, y1, alternative = "less"), 3)
#' round(diff.test(x1, y1, x1, y1, alternative = "less"), 3)
#' @keywords internal
test.diff.cor.single <-
function(x1, y1, x2, y2,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95) {
alternative <- match.arg(alternative)
stopifnot(length(x1) == length(y1))
stopifnot(length(x2) == length(y2))
n1 <- length(x1)
n2 <- length(x2)
z1 <- atanh(stats::cor(x1, y1))
z2 <- atanh(stats::cor(x2, y2))
var1 <- 1/(n1 - 3)
var2 <- 1/(n2 - 3)
se <- sqrt(var1 + var2)
estimate <- z1 - z2
z <- estimate/se
if (alternative == "two.sided") {
q <- -qnorm((1 - conf.level)/2)
lower <- tanh(estimate - q*se)
upper <- tanh(estimate + q*se)
p.val <- 2*pnorm(-abs(z))
} else if (alternative == "greater") {
q <- -qnorm((1 - conf.level))
lower <- tanh(estimate - q*se)
upper <- 1
p.val <- 1 - pnorm(z)
} else if (alternative == "less") {
q <- -qnorm((1 - conf.level))
lower <- -1
upper <- tanh(estimate + q*se)
p.val <- pnorm(z)
} else {
stop("alternative not found")
}
ans <- c(r1 = tanh(z1), r2 = tanh(z2),
estimate = estimate, se = se, z = z,
lower = lower, upper = upper, p.val = p.val)
attr(ans, "alternative") <- alternative
return(ans)
}