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‎DESCRIPTION

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@@ -1,8 +1,8 @@
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Package: caMST
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Type: Package
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Title: Mixed Computerized Adaptive Multistage Testing
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Version: 0.1.5
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Date: 2021-08-17
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Version: 0.1.6
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Date: 2022-06-24
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Authors@R: person("Anthony", "Raborn", email = "anthony.w.raborn@gmail.com", role = c("aut","cre"))
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Description: Provides functions to more easily analyze computerized adaptive tests. Currently, functions for computerized
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adaptive tests (CAT), computer adaptive multistage tests (CMT), and mixed computer adaptive multistage tests (McaMST)
@@ -21,4 +21,4 @@ Imports: catR,
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Suggests: testthat,
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knitr,
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rmarkdown
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RoxygenNote: 7.1.1
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RoxygenNote: 7.2.0

‎R/multistage_test_function.R

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#' \item{final.theta.estimate}{A numeric vector of the final theta estimates using the \code{method} provided in \code{function.call}.}
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#' \item{eap.theta}{A numeric vector of the final theta estimates using the expected a posteriori (EAP) theta estimate from \code{catR::eapEst}.}
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#' \item{final.theta.Baker}{A numeric vector of the final theta estimates using an iterative maximum likelihood estimation procedure as described in chapter 5 of Baker (2001).}
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#' \item{final.theta.SEM}{A numeric vector of the final standard error of measurement (SEM) estimates using the \code{mstR::semTheta} function.}
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#' \item{final.theta.SEM}{A numeric vector of the final standard error of measurement (SEM) estimates using the \code{catR::semTheta} function.}
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#' \item{final.items.seen}{A matrix of the final items seen by each individual using the supplied item names. `NA` values indicate that an individual wasn't given any items to answer after the last specified item in their row.}
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#' \item{final.responses}{A matrix of the responses to the items seen in \code{final.items.seen}. \code{NA} values indicate that the individual didn't answer the question in the supplied response file or wasn't given any more items to answer.}
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#' \item{transition.matrix}{The \code{transition_matrix} originally supplied to the function.}
@@ -138,7 +138,7 @@ multistage_test <-
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seen.items = c(seen.items, which(next.module==1))
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current.responses = mst.responses[, seen.items]
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num.correct = sum(current.responses)
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current.theta = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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current.theta = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = as.numeric(current.responses),
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method = method)
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seen.modules = c(seen.modules, selected.module)
@@ -147,11 +147,11 @@ multistage_test <-
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final.responses[i, ] = as.numeric(mst.responses[, seen.items])
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final.items.seen[i, ] = seen.items
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final.modules.seen[i,] = seen.modules
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final.theta[i] = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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final.theta[i] = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = final.responses[i, ],
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method = method)
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final.theta.eap[i] = mstR::eapEst(it = mst_item_bank[seen.items, ], x = final.responses[i, ])
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final.theta.eap[i] = catR::eapEst(it = mst_item_bank[seen.items, ], x = final.responses[i, ])
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temp.iter = iterative.theta.estimate(
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initial_theta = initial_theta,
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)))
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final.theta.Baker[i] = temp.iter[1]
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final.theta.SEM[i] =
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mstR::semTheta(thEst = final.theta[i], it = mst_item_bank[seen.items, ],
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catR::semTheta(thEst = final.theta[i], it = mst_item_bank[seen.items, ],
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x = final.responses[i, ], model = model,
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method = method)
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@@ -196,7 +196,7 @@ multistage_test <-
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seen.items = c(seen.items, which(next.module==1))
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current.responses = mst.responses[, seen.items]
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num.correct = sum(current.responses[length(current.responses):(sum(next.module)+1)])
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current.theta = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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current.theta = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = as.numeric(current.responses),
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method = method)
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seen.modules = c(seen.modules, selected.module)
@@ -205,11 +205,11 @@ multistage_test <-
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final.responses[i, ] = as.numeric(mst.responses[, seen.items])
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final.items.seen[i, ] = seen.items
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final.modules.seen[i,] = seen.modules
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final.theta[i] = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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final.theta[i] = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = final.responses[i, ],
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method = method)
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final.theta.eap[i] = mstR::eapEst(it = mst_item_bank[seen.items, ], x = final.responses[i, ])
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final.theta.eap[i] = catR::eapEst(it = mst_item_bank[seen.items, ], x = final.responses[i, ])
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temp.iter = iterative.theta.estimate(
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initial_theta = initial_theta,
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)))
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final.theta.Baker[i] = temp.iter[1]
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final.theta.SEM[i] =
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mstR::semTheta(thEst = final.theta[i], it = mst_item_bank[seen.items, ],
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catR::semTheta(thEst = final.theta[i], it = mst_item_bank[seen.items, ],
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x = final.responses[i, ], model = model,
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method = method)
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# end loop for this person; repeat loop for next
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model = model,
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theta = initial_theta
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)
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current.responses = mst.responses[, first.module$items]
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current.responses = mst.responses[i, first.module$items]
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seen.modules = first.module$module
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seen.items = first.module$items
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first.theta.est = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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first.theta.est = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = current.responses,
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method = method)
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criterion = module_select
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)
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seen.items = c(seen.items, next.module$items)
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current.responses = response_matrix[, seen.items]
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current.theta = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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current.responses = response_matrix[i, seen.items]
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current.theta = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = current.responses,
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method = method)
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seen.modules = c(seen.modules, next.module$module)
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final.responses[i, ] = as.numeric(mst.responses[, seen.items])
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final.items.seen[i, ] = seen.items
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final.modules.seen[i, ] = seen.modules
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final.theta[i] = mstR::thetaEst(it = mst_item_bank[seen.items, ],
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final.theta[i] = catR::thetaEst(it = mst_item_bank[seen.items, ],
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x = final.responses[i, ],
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method = method)
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final.theta.eap[i] = mstR::eapEst(it = mst_item_bank[seen.items, ], x = final.responses[i, ])
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final.theta.eap[i] = catR::eapEst(it = mst_item_bank[seen.items, ], x = final.responses[i, ])
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temp.iter = iterative.theta.estimate(
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initial_theta = initial_theta,
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)))
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final.theta.Baker[i] = temp.iter[1]
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final.theta.SEM[i] =
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mstR::semTheta(thEst = final.theta[i], it = mst_item_bank[seen.items, ],
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catR::semTheta(thEst = final.theta[i], it = mst_item_bank[seen.items, ],
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x = final.responses[i, ], model = model,
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method = method)
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# end loop for this person; repeat loop for next

‎README.Rmd

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@@ -86,7 +86,7 @@ data.frame("True Theta" = example_thetas,
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1.96*resultsMLWMI@final.theta.SEM)
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```
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The theta estimates under CMT using the default module selection are close except for person 4, whose estimate falls outside of the 95% confidence interval. Using the "MLWMI" module selection does not work very well at all for this data, though.
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The theta estimates under CMT using the default module selection are close as all the estimates fall within the 95% confidence interval. Using the "MLWMI" module selection produces different, but similarly accurate estimates.
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### Mixed Computerized Adaptive Multistage Testing (Mca-MST)
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data.frame("True Theta" = example_thetas,
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"Estimated Theta" = catResults@final.theta.estimate,
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"CI95 Lower Bound" = catResults@final.theta.estimate -
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1.96*results@final.theta.SEM,
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1.96*catResults@final.theta.SEM,
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"CI95 Upper Bound" = catResults@final.theta.estimate +
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1.96*results@final.theta.SEM)
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1.96*catResults@final.theta.SEM)
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```
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