include templated code (currently undocumented)

Author: philchalmers

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: mirt
-Version: 1.44.3
+Version: 1.44.4
 Type: Package
 Title: Multidimensional Item Response Theory
 Authors@R: c( person("Phil", family="Chalmers", email =

R/RCI.R

@@ -198,29 +198,21 @@ RCI <- function(mod_pre, predat, postdat,
                               SE=pse, z=z,
                               p=pnorm(abs(z), lower.tail = FALSE)*2)
         } else {
-            stop('multidimensional IRT models not suppported in RCI()', call.=FALSE)
-            # fs_pre <- fscores(mod_pre, response.pattern=predat, ...)
-            # fs_post <- fscores(mod_post, response.pattern=postdat, ...)
-            # fs_acov <- fs_pre_acov <- fscores(mod_pre, response.pattern = predat,
-            #                                   return.acov=TRUE, ...)
-            # fs_post_acov <- fscores(mod_post, response.pattern=postdat,
-            #                         return.acov=TRUE, ...)
-            # for(i in 1L:length(fs_acov))
-            #     fs_acov[[i]] <- fs_pre_acov[[i]] + fs_post_acov[[i]]
-            # diff <- fs_post[,1L:nfact] - fs_pre[,1L:nfact]
-            # joint <- vector('list', nrow(diff))
-            # for(i in 1:nrow(diff))
-            #     joint[[i]] <- wald.test(diff[i,], covB = fs_acov[[i]],
-            #                             L = diag(ncol(diff)))
-            # joint <- do.call(rbind, joint)
-            # SEs <- do.call(rbind, lapply(fs_acov, \(x) sqrt(diag(x))))
-            # z <- diff/SEs
-            # converge_pre <- converge_post <- rep(TRUE, length(z))
-            # converge_pre[attr(fs_pre, 'failed2converge')] <- FALSE
-            # converge_post[attr(fs_post, 'failed2converge')] <- FALSE
-            # ret <- data.frame(diff=diff, converged=converge_pre & converge_post,
-            #                   joint, z=z,
-            #                   p=pnorm(abs(z), lower.tail = FALSE)*2)
+            # TODO document this later, and include an example
+            stopifnot("Must have exactly 2 latent traits" =
+                          extract.mirt(mod_pre, 'nfact') == 2)
+            if(!missing(postdat))
+                stop('Only mod_pre and predat are required for multidimensional model')
+            fs <- fscores(mod_pre, response.pattern=predat, ...)
+            diff <- fs[,1] - fs[,2]
+            pse <- sqrt(fs[,3]^2 + fs[,4]^2)
+            z <- diff/pse
+            converge <- rep(TRUE, length(z))
+            converge[attr(fs, 'failed2converge')] <- FALSE
+            ret <- data.frame(pre.score=fs[,1], post.score=fs[,2],
+                              converged=converge, diff,
+                              SE=pse, z=z,
+                              p=pnorm(abs(z), lower.tail = FALSE)*2)
         }
     }