# expect_equal # expect_error # expect_match # expect_true # expect_false ### test_that("require the pairwise keyword", { set.seed(123) a = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.3, 0.7)) b = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.7, 0.3)) df = data.frame( PId = factor(rep(1:30, times=2)), X = factor(c(rep("a",30), rep("b",30))), Y = factor(c(a,b)) ) suppressMessages({ m = glmer.mp(Y ~ X + (1|PId), data=df) expect_error(glmer.mp.con(m, ~ X, adjust="none"), "glmer.mp.con requires the 'pairwise' keyword") }) }) ### test_that("require a glmerMod model", { set.seed(123) a = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.3, 0.7)) b = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.7, 0.3)) df = data.frame( PId = factor(rep(1:30, times=2)), X = factor(c(rep("a",30), rep("b",30))), Y = factor(c(a,b)), Z = round(rnorm(60, mean=200, sd=40), digits=2) ) suppressMessages({ m = lme4::lmer(Z ~ X + (1|PId), data=df) expect_error(glmer.mp.con(m, pairwise ~ X, adjust="none"), "glmer.mp.con requires a model created by glmer.mp.") }) }) ### test_that("require a model with an alt factor", { set.seed(123) a = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.3, 0.7)) b = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.7, 0.3)) df = data.frame( PId = factor(rep(1:30, times=2)), X = factor(c(rep("a",30), rep("b",30))), Y = factor(c(a,b)) ) suppressMessages({ m = lme4::glmer(Y ~ X + (1|PId), data=df, family=binomial) expect_error(glmer.mp.con(m, pairwise ~ X, adjust="none"), "glmer.mp.con requires a model created by glmer.mp.") }) }) ### test_that("ensure all contrast terms are in model", { set.seed(123) a = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.3, 0.7)) b = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.7, 0.3)) df = data.frame( PId = factor(rep(1:30, times=2)), X1 = factor(c(rep("a",30), rep("b",30))), X2 = factor(c(rep("c",20), rep("d",20), rep("e",20))), X3 = factor(c(rep("f",15), rep("g",15), rep("h",15), rep("i",15))), Y = factor(c(a,b)) ) suppressMessages({ m = glmer.mp(Y ~ X1*X2 + (1|PId), data=df) expect_error(glmer.mp.con(m, pairwise ~ X3, adjust="none"), "glmer.mp.con requires formula terms to be present in the model.") }) }) ### test_that("ensure contrast terms are factors", { set.seed(123) a = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.3, 0.7)) b = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.7, 0.3)) df = data.frame( PId = factor(rep(1:30, times=2)), X1 = factor(c(rep("a",30), rep("b",30))), X2 = round(rnorm(60, mean=40, sd=10), digits=1), Y = factor(c(a,b)) ) suppressMessages({ m = glmer.mp(Y ~ X1*X2 + (1|PId), data=df) expect_error(glmer.mp.con(m, pairwise ~ X1*X2, adjust="none"), "glmer.mp.con requires formula terms to be factors") }) }) ### test_that("match p-values for within-Ss. contrast", { set.seed(123) a = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.3, 0.7)) b = sample(c("yes","no"), size=30, replace=TRUE, prob=c(0.7, 0.3)) df = data.frame( PId = factor(rep(1:30, times=2)), X = factor(c(rep("a",30), rep("b",30))), Y = factor(c(a,b)) ) suppressMessages({ m1 = lme4::glmer(Y ~ X + (1|PId), data=df, family=binomial) m2 = glmer.mp(Y ~ X + (1|PId), data=df) c1 = emmeans::emmeans(m1, pairwise ~ X, adjust="none") c2 = glmer.mp.con(m2, pairwise ~ X, adjust="none") expect_true(abs(as.data.frame(c1$contrasts)$p.value - c2$contrasts$p.value) <= 0.05) }) })