### Regression tests for the r x c x K problem, i.e., ### testing the independence of a factor ### 'y' and a factor factor 'x' (possibly blocked) suppressWarnings(RNGversion("3.5.2")) set.seed(290875) library("coin") isequal <- coin:::isequal options(useFancyQuotes = FALSE) ### generate data: 2 x 2 x K dat <- data.frame(x = gl(2, 50), y = gl(2, 50)[sample(1:100)], block = gl(10, 10)[sample(1:100)])[sample(1:100, 75),] ### Pearsons Chisq Test, asymptotic distribution ptwo <- chisq.test(table(dat$x, dat$y), correct = FALSE)$p.value stopifnot(isequal(pvalue(chisq_test(y ~ x, data = dat)), ptwo)) stopifnot(isequal(pvalue(chisq_test(table(dat$y, dat$x))), ptwo)) ### Cochran-Mantel-Haenzel Test, asymptotic distribution ptwo <- drop(mantelhaen.test(table(dat$x, dat$y, dat$block), correct = FALSE)$p.value) stopifnot(isequal(pvalue(cmh_test(y ~ x | block, data = dat)), ptwo)) stopifnot(isequal(pvalue(cmh_test(table(dat$y, dat$x, dat$block))), ptwo)) ### generate data: r x c x K dat <- data.frame(x = gl(4, 25), y = gl(4, 25)[sample(1:100)], block = gl(2, 50)[sample(1:100)]) ### Cochran-Mantel-Haenzel Test, asymptotic distribution ### (was wrong in R < 2.1.0) ptwo <- drop(mantelhaen.test(table(dat$y, dat$x, dat$block), correct = FALSE)$p.value) stopifnot(isequal(pvalue(cmh_test(y ~ x | block, data = dat)), ptwo)) stopifnot(isequal(pvalue(cmh_test(table(dat$y, dat$x, dat$block))), ptwo)) ### Linear-by-linear Test, asymptotic distribution lbl_test(y ~ x | block, data = dat) lbl_test(table(dat$y, dat$x, dat$block)) ### generate data: r x c x K dat <- data.frame(x = gl(4, 25), y = gl(5, 20)[sample(1:100)], block = gl(2, 50)[sample(1:100)]) ### Cochran-Mantel-Haenzel Test, asymptotic distribution ### (was wrong in R < 2.1.0) ptwo <- drop(mantelhaen.test(table(dat$y, dat$x, dat$block), correct = FALSE)$p.value) stopifnot(isequal(pvalue(cmh_test(y ~ x | block, data = dat)), ptwo)) stopifnot(isequal(pvalue(cmh_test(table(dat$y, dat$x, dat$block))), ptwo)) ### Linear-by-linear Test, asymptotic distribution lbl_test(y ~ x | block, data = dat) lbl_test(table(dat$y, dat$x, dat$block)) ### 2x2 table and maxstat x <- c(rep(1,51), rep(2,49)) y <- factor(c(rep(0,49), rep(1,51)))[sample(1:100)] sit <- as.vector(statistic(independence_test(table(x, y)))) stopifnot(isequal(as.vector(statistic(maxstat_test(y ~ x))), sit)) stopifnot(isequal(as.vector(statistic(maxstat_test(table(x, y)))), sit)) ### maxstat for multiple, ordered and unordered covariates dat <- data.frame(w = rnorm(100), x = runif(100), y = gl(4, 25)[sample(1:100)], z = ordered(gl(4, 25)[sample(1:100)])) mt <- maxstat_test(w ~ x, data = dat) mt est <- mt@estimates$estimate$cutpoint stopifnot(isequal(statistic(mt), abs(statistic(independence_test(w ~ (x <= est), data = dat))))) mt <- maxstat_test(w ~ y, data = dat) mt est <- mt@estimates$estimate$cutpoint xx <- dat$y %in% est stopifnot(isequal(statistic(mt), abs(statistic(independence_test(w ~ xx, data = dat))))) mt <- maxstat_test(w ~ z, data = dat) mt est <- mt@estimates$estimate$cutpoint xx <- dat$z <= est stopifnot(isequal(statistic(mt), abs(statistic(independence_test(w ~ xx, data = dat))))) mt <- maxstat_test(w ~ x + y + z, data = dat) mt est <- mt@estimates$estimate xsel <- dat[[est[[1]]]] if (is.factor(xsel) && !is.ordered(xsel)) { xx <- xsel %in% est[[2]] } else { xx <- xsel <= est[[2]] } stopifnot(isequal(statistic(mt), abs(statistic(independence_test(w ~ xx, data = dat))))) ### marginal homogeneity rating <- c("low", "moderate", "high") x <- as.table(matrix(c(20, 10, 5, 3, 30, 15, 0, 5, 40), ncol = 3, byrow = TRUE, dimnames = list(Rater1 = rating, Rater2 = rating))) ### test statistic W_0 = 13.76 ### see http://ourworld.compuserve.com/homepages/jsuebersax/mcnemar.htm stopifnot(all.equal(round(statistic(mh_test(x)), 2), 13.76))
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