similar to: problem with the ltm package - 3PL model

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

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")

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]]

Yamaban wrote: > On Thu, 10 Dec 2015 16:00, m.roth at ... wrote: > >> We've started having a problem with a CentOS 7 server. It looses its >> IPv6 address, if I understand this issue correctly. We can get in, >> if we do ssh -4, though. >> >> In the logs, I'm seeing this about twice an hour: >> <warn> (pid 98466) unhandled DHCP event for

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-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

Hi Everybody, I have just been introduced to R and have used the ltm package for calibrating items under IRT. I have been able to get the item parameters using the fit function under different models. I am using the 2PL model. I have got the parameters for some 300 items from 15 different tests using the function fit. Now I need to automatically generate tests such that they have atleast a

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)

Dear R community, I hope someone is out there who has some insights into the ltm package. Specifically, I am seeking help for the testEquatingData function which is part of this package. Here is an example of my data: #install.packages("ltm", dependencies = TRUE) library(ltm) anchor<- as.data.frame(cbind(c(NA, NA, NA, NA, 1), c(NA, NA, NA, 1, NA), c(1,1,NA,NA,NA))) names(anchor)

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

Hello, my question is; for a specific item, how can I create a plot with both the empirical probability of correct response and the predicted probability as a function of the mean of the ability intervals (similar to a BILOG plot). Thank you -- View this message in context: http://r.789695.n4.nabble.com/IRT-ltm-function-plot-for-probabilities-tp2714881p2714881.html Sent from the R help mailing

I am using package LTM to estimate a Rasch model: irtestimates <- rasch(binRasch) I want to get a single vector containing theta estimates for all the rows (individuals) in my data matrix (hopefully in the same order as my data matrix) such that the length of the theta vector = the number of rows (participants) in my data matrix. I am using: theta.est <-

Dear R-users, I'd like to announce the release of the new version of package 'ltm' for analyzing multivariate dichotomous and polytomous data under the Item Response Theory approach. New features: * function tpm() (along with supporting methods, i.e., anova, plot, margins, factor.scores, etc.) has been added for fitting Birnbaum's Three Parameter Model. * grm() can now

Dear R-users, I'd like to announce the release of the new version of package 'ltm' for analyzing multivariate dichotomous and polytomous data under the Item Response Theory approach. New features: * function tpm() (along with supporting methods, i.e., anova, plot, margins, factor.scores, etc.) has been added for fitting Birnbaum's Three Parameter Model. * grm() can now

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 <-

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