similar to: Package of EM and MI for IRT in R

Dear All, I am Ph.D student at Chulalongkorn University in Thailand, I want to use Package 'sampleSection' to estimate missing data which generate under IRT model(3-PL); n<-500 ## number of examinee I<-20 ## number of items num.imp<-5 ##number of imputations p.missing<-c(0.09, 0.01) #prob of missing theta<-sort(rnorm(n,0,1)) #ability a<-rnorm(I,0.5,0.1) #discrimination

Dear all, I generate data under IRT mdel. I conducted 1000 replications. When I run, some replication was not fit with my model. So, replications were fit model were less than 1000 replication. If I want its run until 1000 replicaions. How shouldI write functin. Thank you, Kamontip [[alternative HTML version deleted]]

I'm relatively new to R, and not particularly adept yet, but I was wondering if there was a simply way to simulate missing data that are MAR, MNAR and MCAR. I've got a good work-around for the MCAR data, but it's sort of hard to work with. Josh [[alternative HTML version deleted]]

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Hi. Can anyone point me to any module in R which implements "Little's Chi Square test" for MCAR. The problem is that i have around 60 behavioural variables on a 6 point categorical scale which i need to test for MCAR and MAR. What i can make out from preliminary analysis is that moderate (0.30 to 0.60) correlations may be present in several variable pairs leading me to suspect

We're using FreeBSD 10.1 Thanks On Thu, May 21, 2015 at 4:56 PM, Xin Li <delphij at delphij.net> wrote: > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA512 > > On 05/21/15 15:47, Cassidy Larson wrote: > > I've been seeing this same error as well now: > > > > Fatal: master: service(indexer-worker): child 20979 killed with > > signal 6 (core not

I have a really long functions, and at the end of the function, I am using a if statement to tag certain keywords based on whether they have certain values contained in them. However, the if statement doesn't seem to work. When I had split up the commands into various functions, it worked fine, but I'm not sure what going on now that it's combined into a single function. myfunc

Hello!   I would like to sample 30 % of cases (with at least 1 value lower than 3) and among them I want to set all values lower than 3 (within selected cases) as NA (NMAR- Not missing at random). I managed to sample cases, but I don’t know how to set values (lower than 3) as NA.   R code:   x <-

On 16 May 2015, at 09:39, Xin Li <delphij at delphij.net> wrote: > > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA512 > > Hi, > > On 5/15/15 10:58, Xin Li wrote: >> Hi, >> >> I have seen the following assertion fails on my own mail server >> (indexer-worker): >> >> Panic: file charset-iconv.c: line 132 (charset_to_utf8):

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

Hello forum, I have developed an IRT model for a set of items on a large reference sample. I now want to use the IRT parameters that were estimated in the reference sample to score new individuals, one at a time. What R package(s) would allow me to do this? Also, several covariates were associated with item thresholds in the reference sample, so I was wondering if any programs can take

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

Hi: I need to apply the IRT model from Ramsay (1991), He apply the Smoothing Kernel to multiple choice test. Is possible in R?. Thank you, Xavier G. Ordonez Doctoral Student Universidad Complutense de Madrid

I have been struggling with the same problem for the past few hours and am desperately in need of some help. The code I am running is as follows: ## opening a data set on the desktop setwd("C:/Users/haillie/Desktop") UN2010<- read.csv("UN2010.csv",header=TRUE) ##calling libraries library(reshape) library(car) ## these are the variables I want to recode colname <-

Hi all, Could anyone tell me if there is some package that fits any Item Response Model (further the ltm package)? Regards, Caio --------------------------------- [[alternative HTML version deleted]]

hello R, This is about simulation in psychomtrics in IRT in R. I want to simulate b parameters(item difficulty) with moments of fixed values of mean, st.d, skewness and kurtosis. Is there any specific IRT package in R with those functions to control those moments? I have seen other programs that can control mean and st.d but not skewness and kurtosis. Thank you, helen L [[alternative HTML

Hello R: i work on an IRT simulation research. I've written a code to generate a single dataset.As i will repeat simulating the data 100 times under every condition, how can i write the R code to make it run the single simulation code 100 times and save the generate results each time? Thanks so much~

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 R: we have a two-parameter IRT simulation code. The goal is to generate a response matrix.But the "for" part doesn't run. we don't know what is wrong with it. Thanks so much~~~ I <- 10 J <- 5 response <- matrix(0, 10, 5) pij <- function(a,b,theta) { a <- rnorm(J, 0.8, 0.04) a b <- rnorm(J, 0, 1) b theta <- rnorm(I, 0,1) theta for( i in 1:I ) { for(

I have the following item response theory (IRT) likelihood that I want to maximize w.r.t. to theta (student ability). L(\theta) = \prod(p(x)) Where p(x) is the 3-parameter logistic model when items are scored dichotomously (x_{ij} = 0 or 1) and p(x) is Muraki's generalized partial credit model when items are scored polytomously (x_{ij} = 0 \ldots J). Now, I wrote the following two functions