Displaying 20 results from an estimated 200 matches similar to: "ltm: Simplified approach to bootstrapping 2PL-Models?"
2008 Mar 10
1
ltm package question
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]]
2006 Aug 11
1
- factanal scores correlated?
Hi,
I wonder why factor scores produced by factanal are correlated, and I'd
appreciate any hints from people that may help me to get a deeper
understanding why that's the case. By the way: I'm a psychologist used
to SPSS, so that question my sound a little silly to your ears.
Here's my minimal example:
***********************************************
v1 <-
2005 Dec 17
0
- McNemar with unequal sample sizes
Hi,
i need to test the equality of proportions in a paired case, like here:
after before
+ -
+ 10 14
- 5 53
So usually i use the mcnemar.test(stats).
Due to mortality, I now got the problem that my sample sizes in both
factors are not equal any more -- there are less cases in the "after"
condition. I already learned that Ekbohm (1982) and Marascuilo adressed
the problem
2011 Feb 10
1
factor.scores
The function factor.scores is used with package ltm and others to estimate IRT type scores for various models. It inherits objects of class grm, gpcm and a few others. What I would like to do is to use the factor.scores function, but feed it my own item parameters (from a bifactor model where the 2PL parameters are adjusted for the bifactor structure). Does anybody have an idea of how this might
2010 Mar 11
2
about IRT simulation
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(
2009 Mar 02
0
package ltm -- version 0.9-0
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
2009 Mar 02
0
package ltm -- version 0.9-0
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
2008 Mar 05
1
degrees of freedom extraction
Hello,
II used the logLik() function to get the log-likelihood estimate of an
object. The function also prints the degrees of freedom. How can I extract
the degrees of freedom and assign it to a variable.
Below is the output:
> logLik(fit2pl)
'log Lik.' -4842.912 (df=36)
Thanks,
Davood Tofighi
[[alternative HTML version deleted]]
2010 Mar 22
0
IRT - Item Information Function: ltm package
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
2007 Dec 12
0
IRT Likelihood problem
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
2010 Jul 21
0
DIF Analysis starting from a gpcm class object
Dear useRs,
does any of you have suggestions on how to conduct a proper DIF analysis
starting from a model of
class gpcm (from the wonderful package ltm by prof. Rizopoulos)?
difR will handle only dichotomous items, and I have a mix of dicho- and
polytomous ones (that's why I chose the partial credit model).
I also found the package lordif, but I'm not really sure if that's what I
2007 Apr 27
0
Protocol for data inclusion in new packages
In the near future I will release MiscPsycho, a package that contains
various functions useful for applied psychometricians. I would like to
include some data sets for distribution in the package, but have not
created any of these on my own, but have used data distributed in other
packages such as the LSAT data in the ltm package.
Is it appropriate for me to distribute a data set in the package I
2010 Sep 27
0
IRT ltm function plot for probabilities
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
2006 Mar 13
0
package ltm -- version 0.4-0
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
2006 Mar 13
0
package ltm -- version 0.4-0
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
2010 Mar 20
0
Getting a complete vector of Theta estimates from Package LTM
I am using package LTM to estimate a Rasch model:
irtestimates <- rasch(binRasch)
I want to get a single vector containing theta estimates for all the
rows (individuals) in my data matrix (hopefully in the same order as
my data matrix) such that the length of the theta vector = the number
of rows (participants) in my data matrix. I am using:
theta.est <-
2005 Sep 27
0
package 'ltm' -- version: 0.3-0
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 scaling purposes), (ii)
2005 Sep 27
0
package 'ltm' -- version: 0.3-0
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 scaling purposes), (ii)
2006 Sep 06
0
package ltm -- version 0.6-0
Dear R-users,
I'd like to announce the release of the new version of package 'ltm'
for analyzing multivariate dichotomous and polytomous data under the
Item Response Theory approach.
New features:
* function tpm() (along with supporting methods, i.e., anova, plot,
margins, factor.scores, etc.) has been added for fitting Birnbaum's
Three Parameter Model.
* grm() can now
2006 Sep 06
0
package ltm -- version 0.6-0
Dear R-users,
I'd like to announce the release of the new version of package 'ltm'
for analyzing multivariate dichotomous and polytomous data under the
Item Response Theory approach.
New features:
* function tpm() (along with supporting methods, i.e., anova, plot,
margins, factor.scores, etc.) has been added for fitting Birnbaum's
Three Parameter Model.
* grm() can now