search for: rasch

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'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) e...

...MLEs using optim which could replace the newton-raphson code I have below which also gives the MLEs. The function takes as input a vector x denoting whether a respondent answered an item correctly (x=1) or not (x=0). It also takes as input a vector b_vector, and these are parameters of test items (Rasch estimates in this case) For example, here is how my current function operates. > rasch.max(c(1,1,0,0), c(-1,.5,0,1)) theta is about 0.14 , se 1.063972 I'm not quite sure how to accomplish the same thing using optim. Can anyone offer a suggestion? rasch.max <- function(x, b_vector){...

I'm new to 'R', so forgive me if I've used the wrong term. I'd like to model some Likert-style items using Rasch methods. I've been unable to find mention of these for 'R'. Would someone happen to know of any? Thanks in any case. Bill ------------ "It is the time that you have wasted for your rose that makes your rose so important."--St-Exupery -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-...

Dear all, I'm working on ability estimates using Rasch model. Using the "ltm" package, the procedure is quite simple: ## Factor Scores for the Rasch model fit <- rasch(LSAT) factor.scores(fit) What about Partial Credit Model (PCM)? For PCM I use PCM function from eRm package. Is there any similar function like factor.scores to estimat...

...part of my function below. This is a psychometric problem for dichotomously scored test items where x is a vector of 1s or 0s denoting whether the respondent answered the item correctly (1) or otherwise (0), b is a vector of item difficulties, and theta is an ability estimate for the individual. rasch <- function(b,theta){ 1 / ( 1 + exp(b - theta)) } The function rasch gives the probability of a correct response to item i conditional on theta, the individuals ability estimate myfun <- function(x, b, theta){ sum(rasch(b, theta)^x * ( 1 - rasch(b,theta) )^(1-x) * dnorm(theta))...

...part of my function below. This is a psychometric problem for dichotomously scored test items where x is a vector of 1s or 0s denoting whether the respondent answered the item correctly (1) or otherwise (0), b is a vector of item difficulties, and theta is an ability estimate for the individual. rasch <- function(b,theta){ 1 / ( 1 + exp(b - theta)) } The function rasch gives the probability of a correct response to item i conditional on theta, the individuals ability estimate myfun <- function(x, b, theta){ sum(rasch(b, theta)^x * ( 1 - rasch(b,theta) )^(1-x) * dnorm(theta))...

...assessment (evaluating quantitative skills in biology students) for reliability. I used cronbach's alpha, but got a really low alpha which I think is likely due to my small number of questions (12) and the fact that the questions are of varying difficulty. After a lot of reading, it looks like Rasch models are probably a more appropriate tool for my question so I've figured out how to do that analysis in R using the eRm package. But now I'm having a really hard time interpreting the output and finding resources to help. Here are the results of my RM estimation: Conditional log-likelih...

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 &...

Full_Name: Axel Rasche Version: 2.1.0 OS: Win2000 Submission from: (NULL) (141.14.21.81) Hi, write.table does not accept the second statement with <col.names = NA, row.names = FALSE>. I do not see why this should not be possible. test = matrix(1:4, 2, 2, dimnames = list( c("a","b"), c(&...

Full_Name: Axel Rasche Version: 2.2.0 OS: Linux Submission from: (NULL) (141.14.21.81) Dear Debuggers, This is not a serious problem. Are 0/1 vectors intended to be used as index vectors? If yes, there is a bug. If not, it leads just to some funny behaviour rather than an error message. In the appendix is some simpl...

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]]

Hi, since one or two month I have a problem with Dovecot not updating the read-flag on mails using IMAP. I receive new mails, but when reading, they still unread on other devices (but shown as read on the first device). When I remember correct, this problem doesn't come with an update of dovecot, but just occured at some time. But I've updated dovecot since then a few times.

...ix (dat) with some values within the matrix missing. In my real world problem, the matrix is huge, and most values are missing. The function in question is called derivs() and is below. But, let me step through the inefficient portions. First, I create a matrix of some predicted probabilities as: rasch <- function(theta,b) 1/ (1 + exp(b-theta)) mat <- apply(as.matrix(theta), 1, rasch, b_vector) However, I only need those predicted probabilities in places where the data are not missing. So, the next step in the function is mat[empty] <- NA which manually places NAs in places where the...

...28, 45, 4, 12, 41, 30, 51, 7), x1 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), x2 = c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1), N = 21) test<-bugs(data,inits, model.file="/home/meyerjp/rasch/test.bug", parameters=c("alpha0","alpha1","alpha12","alpha2","sigma"), n.chains=2,n.iter=10000,n.burnin=1000, bugs.directory="/home/meyerjp/.wine/drive_c/Program Files/WinBUGS14/", working.directory="/home/meyerjp/rasch/workin...

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 with up to two latent variables (allowing also for nonlinear terms), and Samejima's Graded Response model. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ab...

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 with up to two latent variables (allowing also for nonlinear terms), and Samejima's Graded Response model. Furthermore, supporting functions for descriptive statistics, goodness-of-fit, ab...

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard errors. By correct, I mean they match another software program as well as the rasch() function in the ltm package. Now, when I...

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) there is the option to report the estimated parameters under th...

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 biased parameter estimates. So, a correction for jml bias is provided for as an optional argument in the function. Currently, the package includes the following: 1) jml() a function for joint maximum...