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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

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

Dear R users, I'd like to announce the new version of the package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch and two-parameter logistic models) under the Item Response Theory approach. Three main extra features have been added: (i) now both ltm() and rasch() permit general fixed-value constraints (e.g., useful for scaling purposes), (ii)

Dear R users, I'd like to announce the new version of the package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch and two-parameter logistic models) under the Item Response Theory approach. Three main extra features have been added: (i) now both ltm() and rasch() permit general fixed-value constraints (e.g., useful for scaling purposes), (ii)

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

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

Afternoon R help, I want to run Rasch/IRT analyses using the ltm package, however, I am using large scale survey data which requires weighting for accurate results. I attempted to create a weighted object to insert into the formulae of the ltm packages, however, the survey data only includes 30 replicate weights and a sampling weight. The svrepdesign requires additional information such as

Hola! Starting with rw1071, just after starting Rgui the scope is not with Rconsole, but some other place. This means I have to do a mouse click in the Rconsole before starting to write the first command. Since I always forget this, I end up writing the first command twice. This is of course only a minor nuisance, but why is focus no longer set to Rconsole window when start-up? This is on

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,

Hi all Guess it''s time for me to dig a bit deeper into the ruby language. Can you recommend some good starting books on ruby programming? Thanks. -- Med venlig hilsen Juri Rischel Jensen Fab:IT ApS Vesterbrogade 50 DK-1620 København Tlf: 70 202 407 / Fax: 33 313 640 www.fab-it.dk / juri@fab-it.dk

Hola! I will want to identify pixels in an image with the mouse, for so getting the image data from the matrix(es), for use in subsequent discriminant analysis. But the following bombs R: (windows XP, rw1071) > str(baboon) list() - attr(*, "size")= int [1:2] 512 512 - attr(*, "cellres")= num [1:2] 1 1 - attr(*, "bbox")= num [1:4] 0 0 512 512 - attr(*,

In the near future I will release MiscPsycho, a package that contains various functions useful for applied psychometricians. I would like to include some data sets for distribution in the package, but have not created any of these on my own, but have used data distributed in other packages such as the LSAT data in the ltm package. Is it appropriate for me to distribute a data set in the package I

The NEWS file for R-1.7.0 says: table() now allows exclude= with factor arguments (requested by Michael Friendly). But in a recent R-1.7.1beta: > test <- c(1,2,3,4,5,1,2,3,4,5,NA,NA,1,2,3,4,5) > table(test) test 1 2 3 4 5 3 3 3 3 3 > table(test, exclude=NULL) test 1 2 3 4 5 <NA> 3 3 3 3 3 2 > table( as.factor(test), exclude=NULL) 1 2

Hello R, I am looking for non-parametric simulation in IRT. Is there any IRT package that does non-parametric simulation? helen L [[alternative HTML version deleted]]

Have I been sleeping in class? rw1071 from CRAN, windows XP incidencia is made by a call to tapply > class(incidencia) [1] "array" > incidencia <- unclass(incidencia) > class(incidencia) [1] "array" Kjetil Halvorsen

hello R, This is about simulation in psychomtrics in IRT in R. I want to simulate b parameters(item difficulty) with moments of fixed values of mean, st.d, skewness and kurtosis. Is there any specific IRT package in R with those functions to control those moments? I have seen other programs that can control mean and st.d but not skewness and kurtosis. Thank you, helen L [[alternative HTML

This is a message for whoever maintains "chisq.test": For an outcome more extreme than 2000 simulations, a Monte Carlo p-value of "< 2.2e-16" was printed. Ripley said the proper p-value for such cases should be 1/(B+1) = 1/2001. This can be easily fixed by adding "if(PVAL==0)PVAL <- 1/(B+1)" right after the following line in the code for chisq.test (in R

I'm new to 'R', so forgive me if I've used the wrong term. I'd like to model some Likert-style items using Rasch methods. I've been unable to find mention of these for 'R'. Would someone happen to know of any? Thanks in any case. Bill ------------ "It is the time that you have wasted for your rose that makes your rose so important."--St-Exupery