Displaying 20 results from an estimated 3000 matches similar to: "package 'ltm' -- version: 0.3-0"
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
2008 Feb 28
0
problem with the ltm package - 3PL model
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
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
2007 May 08
0
package ltm -- version 0.8-0
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
2007 May 08
0
package ltm -- version 0.8-0
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
2005 Mar 15
0
New package for latent trait models
Dear R-users,
I'd like to announce the release of my new package "ltm" (available
from CRAN), for fitting Latent Trait Models (including the Rasch
model) under the Item Response Theory approach. The latent trait model
is the analogous of the factor analysis model for Bernoulli response
data. "ltm" fits the linear one- and two-factor models but also allows
for
2005 Mar 15
0
New package for latent trait models
Dear R-users,
I'd like to announce the release of my new package "ltm" (available
from CRAN), for fitting Latent Trait Models (including the Rasch
model) under the Item Response Theory approach. The latent trait model
is the analogous of the factor analysis model for Bernoulli response
data. "ltm" fits the linear one- and two-factor models but also allows
for
2008 Feb 20
0
New Package 'JM' for the Joint Modelling of Longitudinal and Survival Data
Dear R-users,
I'd like to announce the release of the new package JM (JM_0.1-0
available from CRAN) for the joint modelling of longitudinal and
time-to-event data.
The package has a single model-fitting function called jointModel(),
which accepts as main arguments a linear mixed effects object fit
returned by function lme() of package nlme, and a survival object fit
returned by either
2008 Feb 20
0
New Package 'JM' for the Joint Modelling of Longitudinal and Survival Data
Dear R-users,
I'd like to announce the release of the new package JM (JM_0.1-0
available from CRAN) for the joint modelling of longitudinal and
time-to-event data.
The package has a single model-fitting function called jointModel(),
which accepts as main arguments a linear mixed effects object fit
returned by function lme() of package nlme, and a survival object fit
returned by either
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
2005 Sep 05
0
New package for grouped data models
Dear R-users,
We'd like to announce the release of our new package "grouped"
(available from CRAN), for fitting models for grouped or coarse data,
under the Coarsened At Random assumption. This is useful in cases
where the true response variable is known only up to an interval in
which it lies. Features of the package include: power calculations for
two-group comparisons,
2005 Sep 05
0
New package for grouped data models
Dear R-users,
We'd like to announce the release of our new package "grouped"
(available from CRAN), for fitting models for grouped or coarse data,
under the Coarsened At Random assumption. This is useful in cases
where the true response variable is known only up to an interval in
which it lies. Features of the package include: power calculations for
two-group comparisons,
2006 Jun 28
1
lme - Random Effects Struture
Thanks for the help Dimitris,
However I still have a question, this time I'll be more specific,
the following is my SAS code
proc mixed data=Reg;
class ID;
model y=Time Time*x1 Time*x2 Time*x3 /S;
random intercept Time /S type=UN subject=ID G GCORR V;
repeated /subject = ID R RCORR;
run; **
(Type =UN for random effects)
The eqivalent lme statement I
2008 Apr 17
1
survreg() with frailty
Dear R-users,
I have noticed small discrepencies in the reported estimate of the
variance of the frailty by the print method for survreg() and the
'theta' component included in the object fit:
# Examples in R-2.6.2 for Windows
library(survival) # version 2.34-1 (2008-03-31)
# discrepancy
fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats)
fit1
fit1$history[[1]]$theta
2007 Feb 23
1
Bootstrapping stepAIC() with glm.nb()
Dear all,
I would like to Boostrap the stepAIC() procedure from package MASS for
variety of model objects, i.e.,
fn <- function(object, data, B = 2){
n <- nrow(data)
res <- vector(mode = "list", length = B)
index <- sample(n, n * B, replace = TRUE)
dim(index) <- c(n, B)
for (i in 1:B) {
up.obj <- update(object, data = data[index[, i], ])
2006 Mar 02
2
'...' passed to both plot() and legend()
Dear R-devels,
I'd like to create a plot method for a class of objects that passes
the '...' argument to both plot() and legend(), e.g.,
x <- list(data = rnorm(1000))
class(x) <- "foo"
plot.foo <- function(x, legend = FALSE, cx = "topright", cy = NULL,
...){
dx <- sort(x$data)
plot(dx, dnorm(dx), type = "l", ...)
if (legend)
2006 May 12
3
Maximum likelihood estimate of bivariate vonmises-weibulldistribution
Thanks Dimitris!!! That's much clearer now. Still have a lot of work to
do this weekend to understand every bit but your code will prove very
useful.
Cheers,
Aziz
-----Original Message-----
From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be]
Sent: May 12, 2006 4:35 PM
To: Chaouch, Aziz
Subject: RE: [R] Maximum likelihood estimate of bivariate