similar to: mirt vs. eRm vs. ltm vs. winsteps

I have found that specifying different "sizes" in the sample command has a funny effect on the random sampling. The code below is a condensed version of a function I wrote to simulate a bootstrap method. For simplicity, I eliminated the internal bootstrap loop, but kept a statement to draw one bootstrap sample, because this is where the problem occurs. The output (mean(y)^2) should be

Hello I'm trying to implement a grid search for a threshold autoregressive model, it is a model in which the regression coefficients are different according to the regimes (under the lower threshold, between lower and upper, over the upper threshold). Estimation of the threshold is made with Conditional least squares: once the threshold is given, the usual parameters are computed with

I realized that I may not have answered the question you were asking and that no one else has responded. I can across a similar problem and may have an answer to your question now. If you have both the wavelet coefficients and the scaling coefficients then create a fake sequence of the same length as the original and decompose that sequence using wd form wavethersh with the same wavelet family

Dear All, does anybody know of a package (working under Linux) for multilevel IRT modelling? I'd love to do this without having to go on WINSTEPS or the like.. thanks for the attention! Federico Andreis ----- Dr. Federico Andreis Universit? degli Studi di Milano-Bicocca, PhD Student MEB Department, Karolinska Institutet, Stockholm, Visiting PhD Student -- View this message in context:

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)) +

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 users >From what I understand, the joint maximum likelihood procedure for Rasch (availabe in the package MiscPsycho) in R can only be used on binary data. I was wondering if the code is currently being adapted for application to ordinal data? I'm trying to replicate results obtained from Winsteps in R. Best wishes denn -- View this message in context:

Hi all I am not an experienced user of R. I am trying to perform DIF analysis using Lordif and I get the follow error: > GroupDIF <- lordif(resp.data=Resp, group=Group, criterion="R2", pseudo.R2="McFadden", R2.change=0.02) Iteration: 500, Log-Lik: -137340.437, Max-Change: 0.00119 EM cycles terminated after 500 iterations. (mirt) | Iteration: 1, 14 items flagged for DIF

Alternatively, anyone know where I could get the binaries for latest Samba 4.x branch for AIX? ---------------------------------------- > From: mirza.dedic at outlook.com > To: samba at lists.samba.org > Date: Mon, 30 Mar 2015 14:05:03 -0700 > Subject: [Samba] Samba on AIX, issues setting read-only flag on files through Windows Explorer? > > Hello list :) > > I am having

I'm trying to kick off some of the rust and learn some of the R packages for Rasch modeling. When I tried using the eRm package, I get item difficulty estimates for all my items accept the first (in terms of order) item. #Begin code library(eRm) r.simulation <- sim.rasch(20,100) r.data <- r.simulation$items #eRm results erm.rasch <- RM(r.data) names(erm.rasch) erm.items <-

Hi, I am trying to do an LRSM Rasch analysis for 72-item data. 67 of the items are binary, and 5 are ternary. I have tried it with and without a specified matrix, and cannot get it to work. The primary problems I am having are: 1. Without a matrix, I get an error: Error in `rownames<-`(`*tmp*`, value = betanames) : length of 'dimnames' [1] not equal to array extent This is from

Dear useRs, new and updated features in eRm 0.9-6 for extended Rasch modeling: - infit and outfit mean-square statistics added to functions personfit() and itemfit(). - new method: plotPImap() plots a person-item map (cf. Bond & Fox, 2007), i.e., a map of locations of item (and threshold) parameters, and the distribution of person parameters. - new options in plotGOF(): 'conf':

Dear useRs, new and updated features in eRm 0.9-6 for extended Rasch modeling: - infit and outfit mean-square statistics added to functions personfit() and itemfit(). - new method: plotPImap() plots a person-item map (cf. Bond & Fox, 2007), i.e., a map of locations of item (and threshold) parameters, and the distribution of person parameters. - new options in plotGOF(): 'conf':

When I try to create a Rasch simulation of data using the sim.rasch function, I get more items than I intend #My code library(eRm) #Number of items k <- 20 #Number of participants n <- 100 #Create Rasch Data #sim.rasch(persons, items, seed = NULL, cutpoint = "randomized") r.simulation <- sim.rasch(n,k) I end up with 20 participants and 100 items, but the instructions say

Hello all, I have just started using fitting the PCM (Partial Credit Model) using eRm and have 2 problems which I cannot solve, I have checked everywhere on the net, but no joy: Firstly, I have fit a PCM model to 10 variables with differing response categories (3 for the first 6 items, 4 for the following 2 and 2 for the last two items). mat1 <- matrix(c(rasch_bart$bart_bowel,

Hi, I am running the non-parametric rasch model tests using eRm. I have a reasonably large dataset for this type of exercise (110 items, 248 persons). I run: > allb2=as.matrix(allb) > rsample <- rsampler(allb2, ctr) > t102<-NPtest(rsample, method="T10") #global test, subgroup inv and receive error message as follows. "Error in m[idx1, idx2] <- 1 : subscript out

Dear R Users, I am using the LLTM and the LRSM functions in the eRm package to do repeated measurements where there are 2 measurement points for a list of 10 items. I am trying to get ability estimates but am having trouble. I don't think that it is appropriate to use the pmat function since the person parameters are based on all 20 items. Rather, I think it would be more appropriate to

Hey people, I''m starting a new project and just finished creating the ERM diagram with about 30 tables. There are quite some tables with foreign keys so I''m not sure how to start creating the controllers/models. How should I start creating controllers/models? Should I first create all tables without foreign key? Should I just start somewhere and add them radomly? I''m

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