add tests before breaking again

tests/tests/test-18-LLTM.R

@@ -21,7 +21,7 @@ test_that('LLTM', {
     ifit <- itemfit(lltm)
     expect_equal(ifit$S_X2[1:3], c(20.06072, 20.90161, 23.48163), tol=1e-2)
     eap <- fscores(lltm)
-    expect_equal(eap[1:3], c(1.0141828, -0.2427042, -0.2427042))
+    expect_equal(eap[1:3], c(1.0141828, -0.2427042, -0.2427042), tol=1e-2)
 
     # using unconditional modeling for first four items
     itemdesign.sub <- itemdesign[5:nrow(itemdesign), , drop=FALSE]
@@ -34,6 +34,46 @@ test_that('LLTM', {
 })
 
 test_that('MLTM', {
+    set.seed(42)
+
+    as <- matrix(rep(1,60), ncol=2)
+    as[11:18,1] <- as[1:9,2] <- 0
+    d1 <- rep(c(3,1),each = 6)  # first easy, then medium, last difficult for first trait
+    d2 <- rep(c(0,1,2),times = 4)    # difficult to easy
+    d <- rnorm(18)
+    ds <- rbind(cbind(d1=NA, d2=d), cbind(d1, d2))
+    dat <- simdata(as, ds, 2500,
+                   itemtype = c(rep('dich', 18), rep('partcomp', 12)))
+
+    # unconditional model
+    syntax <- "theta1 = 1-9, 19-30
+           theta2 = 10-30
+           COV = theta1*theta2"
+    itemtype <- c(rep('Rasch', 18), rep('PC1PL', 12))
+    mod <- mirt(dat, syntax, itemtype=itemtype, verbose=FALSE)
+    expect_equal(as.numeric(coef(mod, simplify=TRUE)$items[19:21, c('d1', 'd2')]),
+                 c(2.86919500, 3.71533102, 3.23797689, 0.01323629, 0.83160425, 1.90030392), tol=1e-2)
+    expect_equal(logLik(mod), -43860.17, tol=1e-2)
+
+    # MLTM design only for PC1PL items
+    itemdesign <- data.frame(t1_difficulty= factor(d1, labels=c('medium', 'easy')),
+                             t2_difficulty=factor(d2, labels=c('hard', 'medium', 'easy')))
+    rownames(itemdesign) <- colnames(dat)[19:30]
+    itemdesign
+
+    # fit MLTM design, leaving first 18 items as 'Rasch' type
+    mltm <- mirt(dat, syntax, itemtype=itemtype, itemdesign=itemdesign,
+                 item.formula = list(theta1 ~ 0 + t1_difficulty,
+                                     theta2 ~ 0 + t2_difficulty), SE=TRUE, verbose=FALSE)
+    expect_equal(extract.mirt(mltm, 'condnum'), 36.33511, tol=1e-2)
+    expect_equal(anova(mltm, mod)$p[2], 0.592, tol=1e-2)
+    cfs <- coef(mltm, simplify=TRUE)$items
+    expect_equal(sort(unique(as.vector(cfs[,1:5]))),
+                 c(-0.07838095, 0.00000000,  0.92424686,  1.03069049,  1.85666146,  3.18998660), tol=1e-2)
+    fs <- fscores(mltm)
+    expect_equal(as.vector(fs[1:3,]), c(-2.0019607,1.138449,0.149316,
+                                        -0.4814751,0.3697978,1.7928627), tol=1e-2)
+
 
 })