Displaying 20 results from an estimated 120 matches similar to: "R lmer & SAS glimmix"
2008 Jan 04
1
GLMMs fitted with lmer (R) & glimmix (SAS)
I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying
2005 Dec 29
1
Glimmix and glm
Hello.
Some months age an e-mail was posted in which a comparison between Glimmix
and glm was discussed. I have not been able to find that e-mail on the R
archive. Does anyone recall the date of the above e-mail?
Thank you very much.
*******************************************
Antonio Paredes
USDA- Center for Veterinary Biologics
Biometrics Unit
510 South 17th Street, Suite 104
Ames, IA 50010
2010 Mar 25
3
Returning Data Frame from Function for use Outside Function
I have a function (see below) that does some bootstrapping (I am happy to
expand offline why I could use existing functions.) I put my results into
and empty matrix and add a row of results with each iteration. My problem is
i am a new user to R and I don't understand data frames, matrices, elements,
and vectors well. What I would like is to have a data frame I can manipulate
outside of the
2012 Jun 19
1
Pseudolikelihood Estimation of spatial GLMM using R
Dear R users,
I've been trying to find an R package which does the PL estimation of
spatial GLMMs especially with the negative binomial model. so it would be
something similar to the "proc GLIMMIX" with the PL method in SAS. I've
looked up some possible packages related to GLMMs, but it doesn't seem to be
anyone using the PL estimation.
Thanks for your help!
Fei He
UCR
2008 Nov 07
1
AIC value in lmer
Dear R Users,
May be this message should be directy send to Douglas Bates ...
I just want to know if I can use the AIC value given in the output of an lmer model to classify my logistic models.
I heard that the AIC value given in GLIMMIX output (SAS) is false because it come from a calculation based on pseudo-likelyhood.
Is it the same for lmer ???
thanks,
Arnaud
Arnaud MOSNIER
Biologiste
2012 Feb 24
1
code for mixed model in R?
Dear
I am analysing my data wit a mixed model. I used SAS but I want to redo the
same analysis in R. Here the SAS code and what I wrote in R. It seems to
work but the results are not the same. I don't know how to specify the class
variable in R or specify the variance matrix. Can you please help me?
Thanks
Jurgen
## SAS:
proc glimmix data=trend method=RSPL;
class pid;
model mdrfinal
2010 Mar 25
0
Counting a number of "elements" in an object
I apologize if this has been answered. I have researched this to the best of
my ability, that's not to say the answer isn't in the archives just I am a
new user and I don't know the proper terms to search under.
I have an object:
f <- mpr100 ~ time + nhb + hispanic + other +
rural + hrural +
factor(age) + factor(gender) +
2008 Feb 14
1
Cholmod error `matrix not positive definite'
Dear R-users,
I'm new to R, so my apologies if this question doesn't make sense.
I've tried the following model in lmer, and it works perfectly:
model<-lmer(aphids~densroot+zone+(1|zone/site), family=quasipoisson)
But if I try the exact same model with a different variable, totmas, the model looks as follows:
model<-lmer(aphids~totmas+zone+(1|zone/site), family=quasipoisson)
2012 Dec 03
2
How to read SPSS file in R
Dear R-users,
I have som troubles with .sav file. How is it possible for us R-users to
read SPSS files. I know that is possible,
I tried the following:
> library(foreign)
> Corp<-read.spss("/Users/kama/Analysis/Corporation.sav", header=TRUE,
> sep=",")
Error in read.spss("/Users/kama/Analysis/Corporation.sav", header = TRUE, :
unused
2008 Feb 20
1
p-value for fixed effect in generalized linear mixed model
Dear R-users,
I am currently trying to switch from SAS to R, and am not very familiar with R yet, so forgive me if this question is irrelevant.
If I try to find the significance of the fixed factor "spikes" in a generalized linear mixed model, with "site" nested within "zone" as a random factor, I compare following two models with the anova function:
2005 Dec 01
3
Strange Estimates from lmer and glmmPQL
I'm trying to fit a generalized mixed effects model to a data set where
each subject has paired categorical responses y (so I'm trying to use a
binomial logit link). There are about 183 observations and one
explanatory factor x. I'm trying to fit something like:
(lmer(y~x+(1|subject)))
I also tried fitting the same type of model using glmmPQL from MASS. In
both cases, I get a
2008 Nov 20
1
syntax and package for generalized linear mixed models
Hi All,
I am making the switch to R and uncertain which of the several packages for
mixed models is appropriate for my analysis. I am waiting for Pinheiro and
Bates' book to arrive via inter-library loan, but it will be a week or more
before it arrives.
I am trying to fit a generalized linear mixed model of survival data
(successes/trials) as a function of several categorical fixed and
2008 Oct 03
0
glmmPQL & Wald-type F-tests
Hello,
Might anyone know how to conduct Wald-type F-tests of the fixed
effects estimated by glmmPQL? I see this implemented in SAS (GLIMMIX),
and have seen it recommended in user group discussions, but haven't come
across any code to accomplish it. I understand the anova function treats
a glmmPQL fit as an lme fit, with the test assumptions based on maximum
likelihood, which is inappropriate
2008 Nov 26
0
Needs suggestions for choosing appropriate R packages
Dear all,
I am thinking to fit a multilevel dataset with R. I have found several
possible packages for my task, such as
glmmPQL(MASS),glmm(repeated),glmer(lme4), et al. I am a little confused by
these functions.
Could anybody tell me which function/package is the correct one to analyse
my dataset?
My dataset is as follows:
the response variable P is binary variable (the subject is a patient
2005 Jul 13
3
nlme, MASS and geoRglm for spatial autocorrelation?
Hi.
I'm trying to perform what should be a reasonably basic analysis of some
spatial presence/absence data but am somewhat overwhelmed by the options
available and could do with a helpful pointer. My researches so far
indicate that if my data were normal, I would simply use gls() (in nlme)
and one of the various corSpatial functions (eg. corSpher() to be
analagous to similar analysis in SAS)
2006 May 16
1
lm summary
Dear all,
Is there anybody who can help me to avoid scientific number in the summary of an lm model?
Here there is an example of a usual output of the lm model.
Thank you!
Guillermo
Example
summary(lm(promiscuity.Index~allK))
Call:
lm(formula = promiscuity.Index ~ allK)
Residuals:
Min 1Q Median 3Q Max
-1.67094 -0.13126 0.06703 0.19913 0.40673
Coefficients:
2006 Jun 29
1
lmer - Is this reasonable output?
I'm estimating two models for data with n = 179 with four clusters (21,
70, 36, and 52) named siteid. I'm estimating a logistic regression model
with random intercept and another version with random intercept and
random slope for one of the independent variables.
fit.1 <- lmer(glaucoma~(1|siteid)+x1
+x2,family=binomial,data=set1,method="ML",
2010 May 13
1
tune svm
Hello, I hope you can help me!
I`m trying to tune svm parameters: cost and gamma for a landsat image
classification, but I get an error and I can't understand it.
I write this:
> tune(svm, Class~., data = mdt01bis, ranges = list(gamma = 2^(-15:3), cost
> = 2^(-5:15)))
and R gives:
Error en predict.svm(model, if (!is.null(validation.x)) validation.x else if
(useFormula)
2010 Apr 14
1
what is the intercept of a two-way anova model without interaction term?
Dear list,
I have a question regarding the meaning of intercept term in a two-way anova model without interaction term.
for example (let's assume there is no interaction between factor1 and factor2) :
> df
val factor1 factor2
1 48.61533 A t1
2 171.13535 B t1
3 65.96884 C t1
4 63.71222 A t2
5 80.22049 B t2
6
2007 May 08
3
ordered logistic regression with random effects. Howto?
I'd like to estimate an ordinal logistic regression with a random
effect for a grouping variable. I do not find a pre-packaged
algorithm for this. I've found methods glmmML (package: glmmML) and
lmer (package: lme4) both work fine with dichotomous dependent
variables. I'd like a model similar to polr (package: MASS) or lrm
(package: Design) that allows random effects.
I was