similar to: Protocol for data inclusion in new packages

I have just submitted MiscPsycho to CRAN. MiscPsycho contains functions for miscellaneous psychometrics that may be useful for applied psychometricians. MML estimation already exists in the ltm package. Hence, a jml option is provided for users who prefer this method. The jml function gives back rasch difficulties and the same Infit and Outfit statistics as Winsteps. Also, jml is known to return

I have just submitted MiscPsycho to CRAN. MiscPsycho contains functions for miscellaneous psychometrics that may be useful for applied psychometricians. MML estimation already exists in the ltm package. Hence, a jml option is provided for users who prefer this method. The jml function gives back rasch difficulties and the same Infit and Outfit statistics as Winsteps. Also, jml is known to return

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

Hi Xavier, the reason you observe this feature is that in the 'constraint' argument you should specify the values under the additive parameterization, i.e., when in the second column of the matrix supplied in 'constraint' you specify 2, then you need to provide the easiness parameters (not the difficulty parameters) in the third column. Check the Details section of ?tpm() and

Dear R-users, I'd like to announce the release of the new version of package 'ltm' (i.e., ltm_0.9-0 soon available from CRAN) for Item Response Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under various IRT models. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ability estimation and

Dear R-users, I'd like to announce the release of the new version of package 'ltm' (i.e., ltm_0.9-0 soon available from CRAN) for Item Response Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under various IRT models. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ability estimation and

I've been building, checking, and installing a package all day today with no errors. I have even been able to bring the package into R on my machine and use it all day doing test runs. However, now, when I run Rcmd check, I get errors. Keep in mind not one thing has changed. I haven't touched any of the functions or .Rd files, yet I get the error below at this point and the package cannot

Dear all, I'm working on ability estimates using Rasch model. Using the "ltm" package, the procedure is quite simple: ## Factor Scores for the Rasch model fit <- rasch(LSAT) factor.scores(fit) What about Partial Credit Model (PCM)? For PCM I use PCM function from eRm package. Is there any similar function like factor.scores to estimate ability scores using PCM model? Best,

The function factor.scores is used with package ltm and others to estimate IRT type scores for various models. It inherits objects of class grm, gpcm and a few others. What I would like to do is to use the factor.scores function, but feed it my own item parameters (from a bifactor model where the 2PL parameters are adjusted for the bifactor structure). Does anybody have an idea of how this might

I have the following item response theory (IRT) likelihood that I want to maximize w.r.t. to theta (student ability). L(\theta) = \prod(p(x)) Where p(x) is the 3-parameter logistic model when items are scored dichotomously (x_{ij} = 0 or 1) and p(x) is Muraki's generalized partial credit model when items are scored polytomously (x_{ij} = 0 \ldots J). Now, I wrote the following two functions

Hello All, I was wondering how I can get the overall Pearson chi^2 test of model fit with its df and p value in the LTM package for the 2PL models. Thanks, -- Davood Tofighi Department of Psychology Arizona State University [[alternative HTML version deleted]]

Dear R-users, I'd like to announce the release of the new version of package `ltm' (i.e., ltm_0.8-0 soon available from CRAN) for Item Response Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under IRT, including the Rasch model, the Two-Parameter Logistic model, Birnbaum's Three-Parameter model, the Latent Trait model

Dear R-users, I'd like to announce the release of the new version of package `ltm' (i.e., ltm_0.8-0 soon available from CRAN) for Item Response Theory analyses. This package provides a flexible framework for analyzing dichotomous and polytomous data under IRT, including the Rasch model, the Two-Parameter Logistic model, Birnbaum's Three-Parameter model, the Latent Trait model

Hi, What package would you recommend for analyzing the validity/reliability of multiple choice tests. Doing things such as classical test analysis, factor analysis, item response theory. I've used psychometric (item.exam), MiscPsycho (alpha.Summary), and ltm (rcor.test). MiscPsycho reported the numbers most similar to what I get in SPSS: corrected point biserial correlations,

Dear useRs, does any of you have suggestions on how to conduct a proper DIF analysis starting from a model of class gpcm (from the wonderful package ltm by prof. Rizopoulos)? difR will handle only dichotomous items, and I have a mix of dicho- and polytomous ones (that's why I chose the partial credit model). I also found the package lordif, but I'm not really sure if that's what I

Hola a todos, Os envío una consulta que considero sencilla pero me está resultando imposible de resolver. Si ejecutáis el siguiente código, obtendréis la gráfica que os adjunto: library(ltm) modelo <- rasch(LSAT) plot(modelo, main="Curva probabilidad pregunta 1",legend = TRUE, cx = "bottomright", items=1,xlab="Conocimiento",ylab="Probabilidad") Resulta

Version 1.5 of the MiscPsycho package had been uploaded to CRAN (should hit mirrors in a day or so). This package has a set of functions that may be useful for psychometric applications. The package has been updated to include the following: 1) All functions (where appropriate) now use standard formula arguments 2) All functions now use S3 print and summary methods 3) Help files have been

The first version of the package plink has been uploaded to CRAN. plink is a package for conducting unidimensional IRT scaling and chain linking for multiple groups for single-format or mixed-format common items. The package supports eight IRT models and four calibration methods. Dichotomous Models: 1PL, 2PL, 3PL Polytomous Models: -Graded response model -Partial credit model -Generalized

The first version of the package plink has been uploaded to CRAN. plink is a package for conducting unidimensional IRT scaling and chain linking for multiple groups for single-format or mixed-format common items. The package supports eight IRT models and four calibration methods. Dichotomous Models: 1PL, 2PL, 3PL Polytomous Models: -Graded response model -Partial credit model -Generalized

An updated version of the package plink has been uploaded to CRAN. This is a major revision that now includes multidimensional models and methods. plink is a package for conducting unidimensional and multidimensional IRT-based test linking using separate calibration methods for multiple groups for single-format or mixed-format common items. The package supports sixteen IRT models and eleven