[R] glmmPQL(MASS), logistic kernel machine regression, Gaussian process

Hi, I am trying to fit a logistic kernel machine regression model (with a Gaussian kernel) in R using glmmPQL. Does anyone have experience with this and maybe can provide some example code? Thanks, Ina [[alternative HTML version deleted]] __ R-

[R] glmmPQL for logistic kernel machine regression model

Hi, I am trying to fit a logistic kernel machine regression model (with a Gaussian kernel) in R using glmmPQL. Does anyone have experience with this and maybe can provide some example code? Thanks, Ina [[alternative HTML version deleted]] __ R-

Re: [R] glmmPQL crashes on inclusion of corSpatial object

Patrick Johann Schratz gmail.com> writes: > > Link to data: > (1170 obs, 9 variables, .Rd file) [plain link in case sth goes wrong > with the hyperlink: https://www.dropbox.com/s/yi3vf0bmqvydr8h/data.Rd?dl=0] > By the way, this has b

[R] glmmPQL crashes on inclusion of corSpatial object

Link to data: (1170 obs, 9 variables, .Rd file) [plain link in case sth goes wrong with the hyperlink: https://www.dropbox.com/s/yi3vf0bmqvydr8h/data.Rd?dl=0] Simply read it in using `readRDS(file)`. I´m trying to setup a GLMM using the

[R] glmmPQL: how to get std error of fitted values?

Hello R-Users, I am using glmmPQL (library MASS) on time-series count data that are aggregated by zipcode. My model includes natural cubic splines for season and day-of-week as fixed effects and random intercept term for zipcode. I need to extract the fitted values AND the standard error of the

Re: [R] glmmPQL and spatial correlation

Luis Huckstadt ucsc.edu> writes: > I'm running into some computer issues when trying to run a binomial model > for spatially correlated data using glmmPQL and was wondering if anyone > could help me out. > My whole dataset consists of about 300,000 points for which I have a suite > of environment

[R] glmmPQL and spatial correlation

Hi all, I'm running into some computer issues when trying to run a binomial model for spatially correlated data using glmmPQL and was wondering if anyone could help me out. My whole dataset consists of about 300,000 points for which I have a suite of environmental variables (I'm trying to come up

[R] glmmPQL and spatial autocorrelation

Hi all, I am analyzing data on habitat utilization of seals in the Southern Ocean. My data show spatial autocorrelation, which I'm interested in incorporating into my model. I am trying to model the presence of dives (versus simulated pseudo-absences) using a binomial generalized binomial model (g

Re: [R] GLMMPQL spatial autocorrelation

r-project.org [mailto:r-help-boun...@r-project.org] Namens Alexroyan Verzonden: dinsdag 29 mei 2012 15:06 Aan: r-help@r-project.org Onderwerp: [R] GLMMPQL spatial autocorrelation Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to

[R] GLMMPQL spatial autocorrelation

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sit

Re: [R] glmmPQL and predict

The whole of idea of 'level' in mixed models is confusing to some. Professor Snijders (who teaches our students) and Professor Bates label from opposite ends. But, assuming this is my work in package MASS (Master Harwood: it is childish, to put it mildly, to fail to give due credit), it follow

Re: [R] glmmPQL and predict

Mike Harwood gmail.com> writes: > Is the labeling/naming of levels in the documentation for the > predict.glmmPQL function "backwards"? The documentation states "Level > values increase from outermost to innermost grouping, with level zero > corresponding to the population predictions". Taking

[R] glmmPQL and predict

Is the labeling/naming of levels in the documentation for the predict.glmmPQL function "backwards"? The documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- g

[R] glmmPQL random effects model

Dear R-helpers, I'd like to use glmmPQL to predict binary responses based on a data.frame data1 containing N entries (N<1000): target covariate1 covariate2 covariate3 ...covariateM cluster 1341311 -0.30031885 0 0-2.886870e-07 1 38370

[R] GLMMPQL and negbinomial: trouble with the X-axis in PREDICT

Ahh, yes. The level made all the difference. I originally thought the level only applied to binomial response variables, so I disregarded it. And I'll just add that >plot(e) below still produced an indexed x axis, so I just combined the vectors and then was able to plot the scaled variable properly

[R] GLMMPQL and negbinomial: trouble with the X-axis in PREDICT

I'm having some difficulty with graphing outputs of a GLM model I've been working. I have count data for both my predictor (only 1) and response variables, and I have pseudoreplication which I've modeled as a random effect. The odTest() from pscl:: indicated that the negative binomial distribution

Re: [R] glmmPQL model selection

Sorry for the late reply. Just use the first 90% of your data to fit and then predict the last 10% and see which one is better. If the random effects are not good it will become very obvious. If the concern is with fixed effects then just use gls which puts the random effects in the error and s

Re: [R] glmmPQL and variance structure

this is very late, but I saw this now as I am dealing with it now: I think varPower should not be needed here. The family should be one of the quasi families eg quasibinomial and that will automatically allow variance/dispersion to become a function of the fit. This is a feature of glm inherently

Re: [R] glmmPQL

Jean-Baptiste Ferdy univ-montp2.fr> writes: > > Dear all, > > I am experiencing problems with glmmmPQL. I am trying to analyze > binomial data with some spatial autocorrelation. Here is my code and > some of the outputs > > m.1 <- glmmPQL(fixed=cbind(nb_pc_02,I(nb_expr_02-nb_pc_02))~(PSDC99 > +

[R] glmmPQL

Dear all, I am experiencing problems with glmmmPQL. I am trying to analyze binomial data with some spatial autocorrelation. Here is my code and some of the outputs > colnames(d.glmm) [1] "BV" "Longitude" "Latitude""nb_pc_02" "nb_expr_02" [6] "pc_02" "nb_pc_07""nb_expr_

[R] 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 g