test_that("ddfMLR - examples at help page", { # skip_on_cran() # skip_on_os("linux") # loading data data(GMATtest, GMATkey) Data <- GMATtest[, 1:20] # items group <- GMATtest[, "group"] # group membership variable key <- GMATkey # correct answers # testing both DIF effects with adjacent category logit model expect_snapshot((fit1 <- ddfMLR(Data, group, focal.name = 1, key))) # saveRDS(fit1, file = "tests/testthat/fixtures/ddfMLR_fit1.rds") fit1_expected <- readRDS(test_path("fixtures", "ddfMLR_fit1.rds")) expect_equal(fit1, fit1_expected) # graphical devices fit1_plot1 <- plot(fit1, item = "Item1", group.names = c("Group 1", "Group 2"))[[1]] vdiffr::expect_doppelganger("ddfMLR_fit1_plot1", fit1_plot1) fit1_plot2 <- plot(fit1, item = 1)[[1]] vdiffr::expect_doppelganger("ddfMLR_fit1_plot2", fit1_plot2) # estimated parameters # saveRDS(coef(fit1), file = "tests/testthat/fixtures/ddfMLR_fit1_coef1.rds") fit1_coef1_expected <- readRDS(test_path("fixtures", "ddfMLR_fit1_coef1.rds")) expect_equal(coef(fit1), fit1_coef1_expected) # saveRDS(coef(fit1, SE = TRUE), file = "tests/testthat/fixtures/ddfMLR_fit1_coef2.rds") fit1_coef2_expected <- readRDS(test_path("fixtures", "ddfMLR_fit1_coef2.rds")) expect_equal(coef(fit1, SE = TRUE), fit1_coef2_expected) # with SE # saveRDS(coef(fit1, SE = TRUE, simplify = TRUE), file = "tests/testthat/fixtures/ddfMLR_fit1_coef3.rds") fit1_coef3_expected <- readRDS(test_path("fixtures", "ddfMLR_fit1_coef3.rds")) expect_equal(coef(fit1, SE = TRUE, simplify = TRUE), fit1_coef3_expected) # with SE, simplified # AIC, BIC, log-likelihood expect_snapshot(AIC(fit1)) expect_snapshot(BIC(fit1)) expect_snapshot(logLik(fit1)) # AIC, BIC, log-likelihood for the first item expect_snapshot(AIC(fit1, item = 1)) expect_snapshot(BIC(fit1, item = 1)) expect_snapshot(logLik(fit1, item = 1)) #' # testing both DDF effects with Benjamini-Hochberg adjustment method expect_snapshot((fit2 <- ddfMLR(Data, group, focal.name = 1, key, p.adjust.method = "BH"))) # saveRDS(fit2, file = "tests/testthat/fixtures/ddfMLR_fit2.rds") fit2_expected <- readRDS(test_path("fixtures", "ddfMLR_fit2.rds")) expect_equal(fit2, fit2_expected) # testing both DDF effects with item purification expect_snapshot((fit3 <- ddfMLR(Data, group, focal.name = 1, key, purify = TRUE))) # saveRDS(fit3, file = "tests/testthat/fixtures/ddfMLR_fit3.rds") fit3_expected <- readRDS(test_path("fixtures", "ddfMLR_fit3.rds")) expect_equal(fit3, fit3_expected) # testing uniform DDF effects expect_snapshot((fit4 <- ddfMLR(Data, group, key, focal.name = 1, type = "udif"))) # saveRDS(fit4, file = "tests/testthat/fixtures/ddfMLR_fit4.rds") fit4_expected <- readRDS(test_path("fixtures", "ddfMLR_fit4.rds")) expect_equal(fit4, fit4_expected) # testing non-uniform DDF effects expect_snapshot((fit5 <- ddfMLR(Data, group, key, focal.name = 1, type = "udif"))) # saveRDS(fit5, file = "tests/testthat/fixtures/ddfMLR_fit5.rds") fit5_expected <- readRDS(test_path("fixtures", "ddfMLR_fit5.rds")) expect_equal(fit5, fit5_expected) # testing both DDF effects with total score as matching criterion expect_snapshot((fit6 <- ddfMLR(Data, group, key, focal.name = 1, match = "score"))) # saveRDS(fit6, file = "tests/testthat/fixtures/ddfMLR_fit6.rds") fit6_expected <- readRDS(test_path("fixtures", "ddfMLR_fit6.rds")) expect_equal(fit6, fit6_expected) }) test_that("ddfMLR - checking inputs", { # skip_on_cran() # skip_on_os("linux") # loading data data(GMATtest, GMATkey) Data <- GMATtest[, 1:20] # items group <- GMATtest[, "group"] # group membership variable key <- GMATkey # correct answers # different dimensions expect_error(ddfMLR(Data, group[-c(1:3)], key, focal.name = 1)) expect_error(ddfMLR(Data, group, key, match = group[-c(1:3)], focal.name = 1)) expect_error(ddfMLR(Data[1:1999, 1:2], group, key, focal.name = 1)) expect_error(ddfMLR(Data[1:1999, 1], group, key, focal.name = 1)) expect_error(ddfMLR(Data, group, key[-1], focal.name = 1)) expect_error(ddfMLR(Data[, 1], group, key, focal.name = 1)) # TODO: this should not be an error? # too many NAs expect_error(ddfMLR(Data = matrix(NA, ncol = 2, nrow = 2000), group, key[1:2], focal.name = 1)) expect_error(ddfMLR( Data = cbind(c(Data[1:1000, 1], rep(NA, 1000)), c(Data[1:1000, 2], rep(NA, 1000))), group = c(rep(NA, 1000), group[1001:2000]), key[1:2], focal.name = 1 )) # invalid type of DIF expect_error(ddfMLR(Data, group, key, focal.name = 1, type = "xxx")) # invalid match expect_error(ddfMLR(Data, group, key, focal.name = 1, match = "dscore")) # invalid significance level expect_error(ddfMLR(Data, group, key, focal.name = 1, alpha = 30)) # invalid combination of matching and purification expect_error(ddfMLR(Data, group, key, focal.name = 1, purify = TRUE, match = GMATtest$criterion)) # deprecated parametrization expect_warning(ddfMLR(Data, group, key, focal.name = 1, parametrization = "is")) # different ways to input group fit1 <- ddfMLR(Data, group, key, focal.name = 1) fit2 <- ddfMLR(GMATtest[, c("group", paste0("Item", 1:20))], "group", key, focal.name = 1) fit3 <- ddfMLR(GMATtest[, c("group", paste0("Item", 1:20))], 1, key, focal.name = 1) expect_equal(fit1, fit2) expect_equal(fit1, fit3) # invalid group set.seed(42) expect_error(ddfMLR(Data, rbinom(nrow(Data), 4, prob = runif(nrow(Data))), key, focal.name = 1)) }) test_that("ddfMLR S3 methods - checking inputs", { # skip_on_cran() # skip_on_os("linux") # loading data data(GMATtest, GMATkey) Data <- GMATtest[, 1:20] # items group <- GMATtest[, "group"] # group membership variable key <- GMATkey # correct answers fit1 <- ddfMLR(Data, group, key, focal.name = 1) # plot - invalid item argument expect_error(plot(fit1, item = "Item25")[[1]]) expect_error(plot(fit1, item = 33)[[1]]) expect_error(plot(fit1, item = list("Item2"))[[1]]) expect_error(plot(fit1, item = c(1, 42))[[1]]) # plot - invalid length of group.names expect_warning(plot(fit1, item = 3, group.names = letters[1:3])[[1]]) expect_warning(plot(fit1, item = 3, group.names = letters[1])[[1]]) # coef - invalid SE expect_error(coef(fit1, SE = "yes")) # coef - invalid simplify expect_error(coef(fit1, simplify = "no")) # coef - invalid IRTpars expect_error(coef(fit1, IRTpars = list())) # coef - invalid CI expect_error(coef(fit1, CI = 95)) # logLik - invalid item expect_error(logLik(fit1, item = "Item25")) expect_error(logLik(fit1, item = 33)) expect_error(logLik(fit1, item = list("Item2"))) expect_error(logLik(fit1, item = c(1, 42))) # AIC - invalid item expect_error(AIC(fit1, item = "Item25")) expect_error(AIC(fit1, item = 33)) expect_error(AIC(fit1, item = list("Item2"))) expect_error(AIC(fit1, item = c(1, 42))) # BIC - invalid item expect_error(BIC(fit1, item = "Item25")) expect_error(BIC(fit1, item = 33)) expect_error(BIC(fit1, item = list("Item2"))) expect_error(BIC(fit1, item = c(1, 42))) # predict - invalid item expect_error(predict(fit1, item = "Item25")) expect_error(predict(fit1, item = 33)) expect_error(predict(fit1, item = list("Item2"))) expect_error(predict(fit1, item = c(1, 42))) # predict - invalid dimensions expect_error(predict(fit1, item = "Item2", group = c(0, 1), match = c(-1, 0, 1))) }) test_that("testing paper code - R Journal 2020 - generated data", { # skip_on_cran() # skip_on_os("linux") set.seed(42) # discrimination a <- matrix(rep(runif(30, -2, -0.5), 2), ncol = 6) a[1:5, c(3, 6)] <- NA # difficulty b <- matrix(rep(runif(30, -3, 1), 2), ncol = 6) b[1:5, c(3, 6)] <- NA a[1, 4] <- a[1, 1] - 1 a[1, 5] <- a[1, 2] + 1 b[6, 4] <- b[6, 1] - 1 b[6, 5] <- b[6, 2] - 1.5 DataDDF <- genNLR(N = 1000, itemtype = "nominal", a = a, b = b) expect_snapshot(head(DataDDF)) # testing both DIF effects with adjacent category logit model expect_snapshot((fit1 <- ddfMLR(DataDDF, group = "group", focal.name = 1, key = rep("A", 10)))) # saveRDS(fit1, file = "tests/testthat/fixtures/ddfMLR_RJournal_fit1.rds") fit1_expected <- readRDS(test_path("fixtures", "ddfMLR_RJournal_fit1.rds")) expect_equal(fit1, fit1_expected) fit1_plot <- plot(fit1, item = fit1$DDFitems, group.names = c("Group 1", "Group 2")) vdiffr::expect_doppelganger("ddfMLR_RJournal_fit1_plot1", fit1_plot[[1]]) vdiffr::expect_doppelganger("ddfMLR_RJournal_fit1_plot2", fit1_plot[[2]]) }) test_that("testing paper code - R Journal 2020 - LearningToLearn", { # skip_on_cran() # skip_on_os("linux") data(LearningToLearn, package = "ShinyItemAnalysis") # nominal data for changes between 6th and 9th grade LtL6_change <- LearningToLearn[, c("track", paste0("Item6", LETTERS[1:8], "_changes"))] expect_snapshot(summary(LtL6_change[, 1:4])) # standardized total score achieved in Grade 6 zscore6 <- LearningToLearn$score_6 expect_snapshot((fitex4 <- ddfMLR( Data = LtL6_change, group = "track", focal.name = "AS", key = rep("11", 8), match = zscore6 ))) expect_equal(fitex4$DDFitems, c(2, 5)) plot1 <- plot(fitex4, item = fitex4$DDFitems) vdiffr::expect_doppelganger("ddfMLR_RJournal_plot3", plot1[[1]]) vdiffr::expect_doppelganger("ddfMLR_RJournal_plot4", plot1[[2]]) })