Displaying 20 results from an estimated 6000 matches similar to: "New versions of Matrix and lme4 packages"
2006 May 05
0
Spline integration & Gaussian quadrature (was: gauss.quad.prob)
Spencer
Thanks for your thoughts on this. I did a bit of work and did end up
with a method (more a trick), but it did work. I am certain there are
better ways to do this, but here is how I resolved the issue.
The integral I need to evaluate is
\begin{equation}
\frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta}
{\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta}
\end{equation}
Where
2018 Jan 18
0
MCMC Estimation for Four Parametric Logistic (4PL) Item Response Model
I know of no existing functions for estimating the parameters of this model using MCMC or MML. Many years ago, I wrote code to estimate this model using marginal maximum likelihood. I wrote this based on the using nlminb and gauss-hermite quadrature points from statmod.
I could not find that code to share with you, but I do have code for estimating the 3PL in this way and you could modify the
2006 Aug 21
1
New version of glmmML
A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite
of the inner structures, so frequent updates (bug fixes) may be
expected for some time.
News:
* The Laplace and adaptive Gauss-Hermite approximations to the log
likelihood function are fully implemented. The Laplace method is made
the default. It should give results you can compare to the results
from 'lmer' (for the
2006 Aug 21
1
New version of glmmML
A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite
of the inner structures, so frequent updates (bug fixes) may be
expected for some time.
News:
* The Laplace and adaptive Gauss-Hermite approximations to the log
likelihood function are fully implemented. The Laplace method is made
the default. It should give results you can compare to the results
from 'lmer' (for the
2006 Jun 09
1
binomial lmer and fixed effects
Hi Folks,
I think I have searched exhaustively, including, of course R-help (D.
Bates, S. Graves, and others) and but I remain uncertain about
testing fixed effects with lmer(..., family=binomial).
I gather that mcmcsamp does not work with Do we rely exclusively on z
values of model parameters, or could we use anova() with likelihood
ratios, AIC and BIC, with (or without)
2010 Apr 14
2
Gaussian Quadrature Numerical Integration In R
Hi All,
I am trying to use A Gaussian quadrature over the interval (-infty,infty) with weighting function W(x)=exp(-(x-mu)^2/sigma) to estimate an integral.
Is there a way to do it in R? Is there a function already implemented which uses such weighting function.
I have been searching in the statmode package and I found the function "gauss.quad(100, kind="hermite")" which uses
2006 Jul 12
0
glmmML updated
I have uploaded a new version (0.30-2) of glmmML to CRAN today.
This is a rather extensive upgrade, mostly internal. Adaptive
Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the
integrals in the log likelihood function. The user can choose the number
of points (default is 16), I _think_ that choosing 1 point will result
in a Laplace approximation. The integrals in the score and
2006 Jul 12
0
glmmML updated
I have uploaded a new version (0.30-2) of glmmML to CRAN today.
This is a rather extensive upgrade, mostly internal. Adaptive
Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the
integrals in the log likelihood function. The user can choose the number
of points (default is 16), I _think_ that choosing 1 point will result
in a Laplace approximation. The integrals in the score and
2007 Mar 13
1
lme4 and mcmcamp
Dear R users
I am trying to obtain p-values for (quasi)poisson lmer models, using
Markov-chain Monte Carlo sampling and the command summary.
>
> My problems is that p values derived from both these methods are
totally different. My question is
(1) there a bug in my code and
>
(2) How can I proceed, left with these uncertainties in the estimations of
> the p-values?
>
> Below is
2006 Apr 28
1
gauss.quad.prob
I've written a series of functions that evaluates an integral from -inf to a or b to +inf using equally spaced quadrature points along a normal distribution from -10 to +10 moving in increments of .01. These functions are working and give very good approximations, but I think they are computationally wasteful as I am evaluating the function at *many* points.
Instead, I would prefer to use
2007 Mar 21
2
Gaussian Adaptive Quadrature
Hi all,
Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions?
Thanks in advance,
Regards,
Caio
__________________________________________________
[[alternative HTML version deleted]]
2010 Mar 26
1
Poisson Lognormal
Hi R Users,
I'm going to estimate via. ML the parameters in Poisson Lognormal
model. The model is:
x | lambda ~ Poisson(lambda)
lambda ~ Lognormal(a,b)
Unfortunately, I haven't found a useful package allowing for such
estimation. I tried to use "poilog" package, but there is no equations
and it's hard to understand what exactly this package really does.
Using it I get the
2005 Dec 15
1
generalized linear mixed model by ML
Dear All,
I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The
2007 Aug 21
1
small issue with densityplot
Hi folks,
This is really minor but to someone not familiar with the various tentacles of the lmer package it could be really annoying. I was trying to plot the posterior density of the fixed effect parameters of a lmer model,
> hr.mcmc = mcmcsamp(hr.lmer, n=50000)
> densityplot(hr.mcmc, plot.points=F)
There is this error,
"Error in densityplot(hr.mcmc, plot.points = F) :
no
2006 Aug 22
1
a generic Adaptive Gauss Quadrature function in R?
Hi there,
I am using SAS Proc NLMIXED to maximize a likelihood with
multivariate normal random effects. An example is the two part random
effects model for repeated measures semi-continous data with a
cluster at 0. I use the "model y ~ general(loglike)" statement in
Proc NLMIXED, so I can specify a general log likelihood function
constructed by SAS programming statements. Then the
2009 Nov 29
1
optim or nlminb for minimization, which to believe?
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
2010 Mar 16
0
New package: ordinal
This is to announce the new R-package ?ordinal? that implements
cumulative link (mixed) models for ordinal (ordered categorical) data
(http://www.cran.r-project.org/package=ordinal/).
The main features are:
- scale (multiplicative) as well as location (additive) effects
- nominal effects for a subset of the predictors (denoted partial
proportional odds when the link is the logistic)
- structured
2010 Mar 16
0
New package: ordinal
This is to announce the new R-package ?ordinal? that implements
cumulative link (mixed) models for ordinal (ordered categorical) data
(http://www.cran.r-project.org/package=ordinal/).
The main features are:
- scale (multiplicative) as well as location (additive) effects
- nominal effects for a subset of the predictors (denoted partial
proportional odds when the link is the logistic)
- structured
2004 Jan 08
0
New version of eha
A new version of 'eha' (0.92-1) is now on CRAN. From the ChangeLog:
0.92-1 (January 7, 2004)
* mlreg: Geometric distribution (i.e., constant baseline discrete
hazard) added. Not for frailty models, yet (on the TODO list).
* mlreg: New argument, 'n.points', added to 'control'. Controls
the number of points used in the Gauss-Hermite quadrature.
* mlreg: Stricter
2004 Jan 08
0
New version of eha
A new version of 'eha' (0.92-1) is now on CRAN. From the ChangeLog:
0.92-1 (January 7, 2004)
* mlreg: Geometric distribution (i.e., constant baseline discrete
hazard) added. Not for frailty models, yet (on the TODO list).
* mlreg: New argument, 'n.points', added to 'control'. Controls
the number of points used in the Gauss-Hermite quadrature.
* mlreg: Stricter