Displaying 20 results from an estimated 5000 matches similar to: "generalized linear mixed models with a beta distribution"
2008 Mar 17
1
generalized linear mixed models with a beta distribution [Sec=Unclassified]
Craig A Faulhaber wrote:
>I am interested in using a generalized linear mixed model with data
> that best fits a beta distribution (i.e., the data is bounded between
> 0 and 1 but is not binomial).
..
>For clarification, here's what I'm trying to model:
>I have a beta-distributed response variable (y). I have a fixed-effect
>explanatory variable (treatment),
2005 Apr 17
3
generalized linear mixed models - how to compare?
Dear all,
I want to evaluate several generalized linear mixed models, including the null
model, and select the best approximating one. I have tried glmmPQL (MASS
library) and GLMM (lme4) to fit the models. Both result in similar parameter
estimates but fairly different likelihood estimates.
My questions:
1- Is it correct to calculate AIC for comparing my models, given that they use
2008 Aug 19
1
R vs Stata on generalized linear mixed models: glmer and xtmelogit
Hello,
I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway).
Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not
2006 Apr 17
1
using betareg: problems with anova and predict
Dear R-helpers:
We have had fun using betareg to fit models with proportions as
dependent variables.
However, in the analysis of these models we found some wrinkles and
don't know where is the best place to start looking for a fix.
The problems we see (so far) are that
1. predict ignores newdata
2. anova does not work
Here is the small working example:
----------------------------
x
2011 Mar 17
1
generalized mixed linear models, glmmPQL and GLMER give very different results that both do not fit the data well...
Hi,
I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER).
I wanted to fit the following model:
2011 Sep 01
3
betareg question - keeping the mean fixed?
Hello,
I have a dataset with proportions that vary around a fixed mean, is it
possible to use betareg to look at variance in the dispersion parameter
while keeping the mean fixed?
I am very new to R but have tried the following:
svec<-c(qlogis(mean(data1$scaled)),0,0,0)
f<-betareg(scaled~-1 | expt_label + grouped_hpi, data=data1, link.phi="log",
2013 Feb 22
1
How to do generalized linear mixed effects models
I want to analyze binary, multinomial, and count outcomes (as well as
the occasional continuous one) for clustered data.
The more I search the less I know, and so I'm hoping the list can
provide me some guidance about which of the many alternatives to choose.
The nlme package seemed the obvious place to start. However, it seems
to be using specifications from nls, which does non-linear
2002 Apr 12
1
summary: Generalized linear mixed model software
Thanks to those who responded to my inquiry about generalized linear
mixed models on R and S-plus. Before I summarize the software, I note
that there are several ways of doing statistical inference for
generalized linear mixed models:
(1)Standard maximum likelihood estimation, computationally intensive
due to intractable likelihood function
(2) Penalized quasi likelihood or similar
2007 Apr 22
1
How to add e.g. lines to a zoo plot?
Hi all,
a problem I encounter again and again: I plot a zoo object using "plot"
and then want to add lines or points to this plot.
I usually circumvent this problem by adding artificial coloumns to the
zoo object before plotting, but I am sure there's a better solution.
To be specific: Assume I did
x <-
2009 Feb 13
1
need help with errors in betareg analysis
Hi
I'm trying to fit a model in betareg and I'm getting errors, but have no
idea what they mean or how to solve them. Does anyone have experience with
this?
> model <- betareg(ACT ~ ST*SoilT, data = actDL_F)
Warning messages:
1: In sqrt(W) : NaNs produced
2: In sqrt(W) : NaNs produced
3: In sqrt(1 + phihat) : NaNs produced
data summaries don't give any na's or problems I
2011 Jun 24
3
Error using betareg
Dear all,
I get an error using betrag on this data set
:http://dl.dropbox.com/u/1866110/dump.csv.
I run it like this
regression f2.1=betareg(Y~X1+X2,data=dump)
summary(f2.1)
I get :
Call:
betareg(formula = Y ~ X1 + X2, data = dump)
Standardized weighted residuals 2:
Error in quantile.default(x$residuals) :
missing values and NaN's not allowed if 'na.rm' is FALSE
In addition:
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
2001 Dec 13
3
Access denial
Hi,
My name is Tom Faulhaber. I am the network administrator at a small company
in Providence,RI.
I am running Samba 2.2.2 on a Redhat 6.1 machine on a Novell 4.21 network.
The workstations are Win98SE. The Linux box is being used for the company
intranet (Apache 1.3.22).
Everything runs fine, however I am completely stumped on one issue (so far).
That issue is: network connectivity. If I
2006 Jan 24
1
fitting generalized linear models using glmmPQL
Hi, I have tried to run the following (I know it's a huge data set but
I tried to perform it with a 1 GB RAM computer):
library(foreign)
library(MASS)
library(nlme)
datos<-read.spss(file="c:\\Documents and
Settings\\Administrador\\Escritorio\\datosfin.sav",to.data.frame=TRUE)
str(datos)
`data.frame': 1414 obs. of 5 variables:
$ POB : Factor w/ 6 levels
2007 Jan 18
2
The math underlying the `betareg' package?
Folks,
The betareg package appears to be polished and works well. But I would
like to look at the exact formulas for the underlying model being
estimated, the likelihood function, etc. E.g. if one has to compute
\frac{\partial E(y)}{\partial x_i}, this requires careful calculations
through these formulas. I read "Regression analysis of variates
observed on (0,1): percentages, proportions and
2013 Sep 18
1
dbeta may hang R session for very large values of the shape parameters
Dear all,
we received a bug report for betareg, that in some cases the optim call in betareg.fit would hang the R session and the command cannot be interrupted by Ctrl-C?
We narrowed down the problem to the dbeta function which is used for the log likelihood evaluation in betareg.fit.
Particularly, the following command hangs the R session to a 100% CPU usage in all systems we tried it (OS X
2006 Feb 06
3
power and sample size for a GLM with poisson response variable
Hi all,
I would like to estimate power and necessary sample size for a GLM with
a response variable that has a poisson distribution. Do you have any
suggestions for how I can do this in R? Thank you for your help.
Sincerely,
Craig
--
Craig A. Faulhaber
Department of Forest, Range, and Wildlife Sciences
Utah State University
5230 Old Main Hill
Logan, UT 84322
(435)797-3892
2004 Dec 07
3
Question about e1/digium
Hi all I am beginning in asterisk and am making tests with an ata-186.
For the time being the tests are going well, however have a doubt.
I am thinking about using a canal e1 with plate digium.
Assuming that the company of telecommunications supplies e1 with 30 canals
and numeration to me 4000-0001 4000-0029. she is possible to configure
asterisk
in way that somebody of is dials 4000-0025, to
2011 Mar 12
3
betareg help
Dear R users,
I'm trying to do betareg on my dataset.
Dependent variable is not normally distributed and is proportion (of condom
use (0,1)).
But I'm having problems:
gyl<-betareg(cond ~ alcoh + drug, data=results)
Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, :
initial value in 'vmmin' is not finite
Why is R returning me error in optim()?
What
2002 Apr 01
2
writing a package for generalized linear mixed modesl
Happy new month, everyone!
I am planning to write a NIH grant proposal to study ways to speed
Monte Carlo based maximum likelihood algorithm for hierarchical models
with a focus on generalized linear mixed models (GLM with random
effects). I thought it would be nice and also increase the chance of
funding if I could produce an R package in the process. I understand
that Prof. Pinheiro ang Bates