search for: examinee

...eem to be going around for 2 days in the wrong circles. Here's what I have in 2 dataframes: > dim(params.data); head(params.data, n=2) [1] 284 6 item a1 a2 b1 b2 c 1 1 0.6671587 0.9588642 -3.5 -3.437961 0.2 2 2 1.1296298 1.4899307 -3.5 -4.146118 0.2 > dim(examinees.data); head(examinees.data, n=2) [1] 1600 3 examinee theta1 theta2 1 1 -3 -3.133437 2 2 -3 -3.293341 I also have a function, p(), that takes on values for a1, a2, b1, b2, c, theta1, and theta2, then outputs a value. I would like to produce a 3-column array that is...

...incorrect number of dimensions". Could you please help and let me know where is the mistake? Here is my program: library(ltm) library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #IRT Data Simulation Routine# n.exams = 500 #Sets number of examinees to be generated# n.items = 20 #Sets number of items to be generated# #The following intialize empty (NA) vectors or matrices# beta.values = rep(NA,n.items) resp.prob = matrix(rep(NA,n.exams*n.items),nrow=n.exams,ncol=n.items) Observed_Scores = matrix(rep(NA,n.exams*n.items),nrow=n.exams,ncol=...

Dear all: I have simulated ability(500 examinees) and difficulty(40 items). as: N <- 500 theta <- rnorm( N ) I <- 40 b <- seq( -2 , 2 , len=I ) p1 <- plogis( outer( theta, b, "-" ) ) I want to simulate 10 responses matrix through this syntax: respR <- 1 * ( p1 > matrix( runif( N*I ) , nrow=N , ncol=I ) ) I tried...

...can still use Stack? Please see the program: #Importing psych & ltm library for all the simulation related functions library(ltm) library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #IRT Data Simulation Routine# n.exams = 500 #Sets number of examinees to be generated# n.items = 20 #Sets number of items to be generated# #The following intialize empty (NA) vectors or matrices# beta.values = rep(NA,n.items) resp.prob=matrix(rep(NA, n.exams*n.items), nrow=n.exams, ncol=n.items) Observed_Scores=matrix(rep(NA, n.exams*n.items), nrow=n.exams, ncol...

...how to make R do it. # The code below produces a matrix of 0's and 1's (which will be used by another program) # I would like to nest this in a "do loop" such that, say, 30 replicate datasets are produced using the # same parameters. N <- nrow(t1) # number of examinees n <- nrow(a1) # number of items d <- 1.7 theta <- t1 response <- matrix (0,N,n) uni <- matrix (runif(N*n),nrow = N) for (i in 1:N) { for (j in 1:n) { if ( c1[j]+(1-c1[j])/(1+exp(-d*a1[j]*(theta[i]-b1[j]))) > uni[i,j] ) respo...

I am trying to simulate a dataset using Parallel Latent CTT model and this is what i have done so far: (START) #Importing psych library for all the simulation related functions library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #Using the function to generate the data GenData <- congeneric.sim(N=500, loads =

Version 1.1 of the MiscPsycho package had been uploaded to CRAN. The package has been updated to include the following: 1) The irt.ability() function that estimates examinee ability given a set of item parameters. The function is very general and can be used to estimate ability when there are only dichotomous items (1-, 2-, or 3PL), only polytomous items (generalized partial credit model), or a mixture of dichotomous and polytomous items. The function can be used to es...

Version 1.1 of the MiscPsycho package had been uploaded to CRAN. The package has been updated to include the following: 1) The irt.ability() function that estimates examinee ability given a set of item parameters. The function is very general and can be used to estimate ability when there are only dichotomous items (1-, 2-, or 3PL), only polytomous items (generalized partial credit model), or a mixture of dichotomous and polytomous items. The function can be used to es...

...an still use Stack? Please see the program: #Importing psych & ltm library for all the simulation related functions library(ltm) library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #IRT Data Simulation Routine# n.exams = 500 #Sets number of examinees to be generated# n.items = 20 #Sets number of items to be generated# #The following intialize empty (NA) vectors or matrices# beta.values = rep(NA,n.items) resp.prob=matrix(rep(NA, n.exams*n.items), nrow=n.exams, ncol=n.items) Observed_Scores=matrix(rep(NA, n.exams*n.items), nrow=n.exams, ncol...

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 b<-rnorm(I,0,1) #difficulty c<-runif(I,0,0.25) #guess Only item 1 have missing data. If the response to i...

...let me know where is the mistake? Here is my > program: > > > library(ltm) > library(psych) > > # Settting the working directory path to C:/NCME > > path="C:/NCME" > setwd(path) > > #IRT Data Simulation Routine# > > n.exams = 500 #Sets number of examinees to be generated# > n.items = 20 #Sets number of items to be generated# > > #The following intialize empty (NA) vectors or matrices# > beta.values = rep(NA,n.items) > resp.prob = matrix(rep(NA,n.exams*n.items),nrow=n.exams,ncol=n.items) > Observed_Scores = matrix(rep(NA,n.exam...

Hi All, I've been having a little trouble with creating a loop that will run a a series of t.tests for inspection, Below is the code i've tried, and some checks i've looked at. I've used the get(paste()) idea as i was told previously that the use of the eval should try and be avoided. I've run a single syntax to check that my systax is correct and works without any problems