similar to: Graded Response Model Simulation (SAS code conversion)

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

Dear R-List, I tried to fit a partial credit model using the "pcmdat" from eRm-package comparing the results of mirt, eRm, ltm and winsteps. The results where quite different, though. I cannot figure out what went wrong and I do not know which result I can rely on. This is what I did in R library(mirt) #load(file="u3.RData")

Using the grm function (graded response IRT model) in the ltm package I receive the following error: Error: subscript out of bounds for several scales I'd like to examine. Here's a small example that if run a few times will likley produce the error at least once ch<-array(round(runif(50,1,5)),c(10,5)) grm(ch,start.val="random") ## or

Hi, this is my first time using R. I want to simulate the following process: "in a population of size N, there are i individuals bearing genotype A, the number of those bearing A is j in the next generation, which following a binominal distribution (choose j from 2*N, the p is i/2*N), to plot the probability of the next generations, my script is as follows. It cannot run successfully,

Dear R-users, I'd like to announce the new version of package 'ltm' for Item Response Theory analysis. The function grm() (along with supporting methods, i.e., anova, margins, factor.scores, etc.) has been added for fitting the Graded Response Model for ordinal polytomous manifest variables. An extra feature of the plot method for classes 'grm', 'ltm' and

Dear R-users, I'd like to announce the new version of package 'ltm' for Item Response Theory analysis. The function grm() (along with supporting methods, i.e., anova, margins, factor.scores, etc.) has been added for fitting the Graded Response Model for ordinal polytomous manifest variables. An extra feature of the plot method for classes 'grm', 'ltm' and

Hi All, I am a beginner in R, and I am planning to learn it by finishing a project. The simulation involves in multiple criteria. Specifically, it is based on Graded Response Model according to IRT. Also, it needs to generate multiple datasets which fit different levels of criteria, like multiple groups (group number=2,3,4,6,9, 12), different sample size for each group (N=250, 500, and 1000),

Hi all, I have been trying to add the line: h = n - p0 + 1; just after h = n / 2 + 1; (line 131) in the source code (the original paper mention this is possible for p0&lt;n). with p0 declared as an int (actually i used the same declaration protocol as n everywhere in the code). The &quot;new&quot; source compiles, but when i give it reasonable values of p0, it runs unto

Is there a way to generate identical random samples using R's runif function and SAS's ranuni function? I have assigned the same seed values in both software packages, but the following results show different results. Thanks! R === > set.seed(6) > random <- runif(10) > random [1] 0.6062683 0.9376420 0.2643521 0.3800939 0.8074834 0.9780757 0.9579337 [8] 0.7627319 0.5096485

Hi my friends, I have an issue with ability estimates when running GRM of IRT. I have responses from 242 subjects but got 183 ability estimates. Below is what I did to get the estimates. 1) I have a csv file "P1.csv" and I imported it into R and loaded the "ltm" package by doing: p1<-read.table("P1.csv",header=TRUE,sep=",") library(ltm) 2) I

Hello, Does anyone knows how to deal with zero subscript in R. I have this code: for (i in 1:nitems){ + for (j in 1:ncat-1) { + draw<-matrix(rnorm(nitems*(ncat-1),seed1,seed2),nitems,(ncat-1)) + d<-( sigma_d*draw ) + mu_d + draw<-matrix(rtnorm((nitems*(ncat-1)),mean = seed1, sd = seed2, lower = .1, upper = 1.5),nitems,(ncat-1)) +

Hi Mark: I had also used sample and tabulate for generating multinomial and found it to be quite slow. So I had written a multinomial random numbers generator based on the GENMUL subroutine from "ranlib", which in turn is based on the algorithm from Luc Devroye's book on "Non-Uniform Random Variate Generation" You may want to compare this with your hybrid algorithm and

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

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

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several

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

Okay, so I'm fairly retarded, and asked a question about finding the T-Value in the Fisher Exact method. I suppose what I'm truly after can best be explained by the Biddle Consulting site that has a program setup to deal with this kind of thing. Unfortunately, it is not currently functioning, and has not been for some time. As a result, I'm trying to figure out how to do this on my

I had an open session working in R and I regularly save the image with save.image(). However, after a period of inactivity of a few days. I quit the session. Now, when I try to restart R and load the same image I get the following result: > load("y:/grm/sdata/.rdata1") Error in load("y:/grm/sdata/.rdata1") : an xdr real data read error occured Can someone explain the cause