search for: ltm

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") mirt<-mirt(as.data.frame(pcmdat),1,itemtype="graded",constrain=list(1,5,...

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 ad...

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-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 <- rmvlogis(nrow(object$X), coef(object)) liFits[[i]] <- ltm(rndDat ~ z1) } distr <- sort(sapply(liFits, function(x)return(x$log.Lik))) return(max(which(distr <= object$log.Lik))/length(dist...

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 T...

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 T...

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 estimatio...

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 estimatio...

...rameterization, 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 the following for an illustration: library(ltm) fit <- tpm(LSAT) cf <- coef(fit) constr <- rbind( cbind(1:3, c(1,1,1), cf[1:3, 1]), cbind(1:3, c(3,3,3), cf[1:3, 3]), cbind(1:3, c(2,2,2), - cf[1:3, 2] * cf[1:3, 3]) ) fit2 <- tpm(LSAT, constraint = constr) coef(fit)[1:3, ] coef(fit2)[1:3, ] I hope it's more clear...

...//gluster-users/2017-November/032950.html To me that shift from using nfs-ganesha to storhaug was a huge change to be absorbed for a minor release version and was made bigger when storhaug does not appear to be ready go given the recommendation to go back and use 3.10. With the release of 3.12 ltm and now 3.13 stm, when 4.0 is released 3.10 is shown to be at eol; Version Status ???? Release_Date EOL_Version EOL_Date 3.10 ?? LTM ??? ??? 2017-02-27 ?? 4.0 3.11 ?? EOL ??? ??? 2017-05-30 ?? 3.12 ??? ?? 2017-8-30 3.12 ?? LTM ??? ??? 2017-08-30??? 4.3 3.13 ?? STM ??? ??? 2017-12-7???? 4.0 4.0 ??...

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 scali...

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 scali...

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...

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...

In my thought utc_offset function in src/lib/utc-offset.c returns wrong offset value when the year in localtime and GMT different. /* max offset of 24 hours */ if (ltm.tm_yday < gtm.tm_yday) offset = -24 * 60; else if (ltm.tm_yday > gtm.tm_yday) offset = 24 * 60; else offset = 0; I think this code should be modified as followings. /* max offset of 24 hours */ if ((ltm.tm_year == gtm.tm_year && ltm.tm_yday < gtm.tm_yday) || (ltm.tm_year...

...184, 184, 174, 185, 175 height of female persons 164, 165, 160, 174, 167, 161, 164, 165, 169, 175, 165, 155, 172, 164, 172, 166, 160, 159, 158, 173 I want to determine if height is dependent on gender, and I need to find, as I understand, point biserial correlation. I had tried to use package "ltm", and obtained coefficient which is about 0.74. I also had tried to calculate it manually (with some MS Excel help for simple calculations), using equation given in Internet (e.g., in Wikipedia; this equation is also given in some copy-books) and obtained coefficient is about 0.77. "ltm&q...

Hola a todos, Estoy usando la librería ltm, de teoría de respuesta al ítem, para crear gráficos. Resulta que añado unas líneas horizontales en los valores de 0.25, 0.50 y 0.75. Me gustaría que en el gráfico, desaparezcan las marcas de 0,.2, 0.4, 0.6 y 0.8 y aparezcan en 0.25, 0.50 y 0.75. He probado varias cosas que he visto por Googe pero...

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', &...

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', &...

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...