similar to: Inserting weights in ltm package

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 <-

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]]

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

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

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")

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 Xavier, the reason you observe this feature is that in the 'constraint' argument you should specify the values under the additive parameterization, 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

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. After working with the 3.12 flavor of gluster I learned nfs-ganesha was replaced with storhaug in 3.11.Have people been successfully utilizing storhaug and does any documentation exist for using it in a ha nfs configuration or is ha in gluster so rarely used it's a non-issue for most gluster users? Going through the lists, it was recommended in November to not use storhaug and if

Dear developers of R-project, I have such data height of male persons 181, 178, 182, 160, 187, 193, 184, 184, 175, 178, 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

On 20/12/2017 16:51, Craig Lesle wrote: > 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 ??? ???

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