#' Two-Sample Goodman and Kruskal's Gamma Test
#'
#' Calculate test for two different independent Goodman and Kruskal's Gamma values.
#'
#' @param gamma.g1 Scalar - Group 1 gamma.
#' @param se.est.gamma.g1 Scalar - Group 1 gamma estimated standard error.
#' @param gamma.g2 Scalar - Group 2 gamma.
#' @param se.est.gamma.g2 Scalar - Group 2 gamma estimated standard error.
#' @param alternative The alternative hypothesis to use for the test computation.
#' @param conf.level The confidence level for this test, between 0 and 1.
#'
#' @return Hypothesis test result showing results of test.
cor.goodman.kruskal.gamma.twosample.independent.simple <- function(
gamma.g1
,se.est.gamma.g1
,gamma.g2
,se.est.gamma.g2
,alternative = c("two.sided", "greater", "less")
,conf.level = .95
) {
validate.htest.alternative(alternative = alternative)
z <- (gamma.g1 - gamma.g2)/sqrt(se.est.gamma.g1 + se.est.gamma.g2)
p.value <- if (alternative[1] == "two.sided") {
tmp<-pnorm(z)
min(tmp,1-tmp)*2
} else if (alternative[1] == "greater") {
pnorm(z,lower.tail = FALSE)
} else if (alternative[1] == "less") {
pnorm(z,lower.tail = TRUE)
} else {
NA
}
retval<-list(data.name = "independent sample G values",
statistic = z,
estimate = c(G.g1 = gamma.g1
,se.est.g1 = se.est.gamma.g1
,G.g2 = gamma.g2
,se.est.g2 = se.est.gamma.g2
),
parameter = 0,
p.value = p.value,
null.value = 0,
alternative = alternative[1],
method = "Two-Sample Independent Goodman and Kruskal's Gamma (G)",
conf.int = c(NA, NA)
)
names(retval$statistic) <- "z"
names(retval$null.value) <- "difference"
names(retval$parameter) <- "null hypothesis difference"
attr(retval$conf.int, "conf.level") <- conf.level
class(retval)<-"htest"
retval
}
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