search for: cadeb

...e records manually by doing the algebra and the predict.glm() function is working correctly incorporating the factor levels on my machine. Any thoughts??? Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: cadeb at usgs.gov <brian_cade at usgs.gov> tel: 970 226-9326 [[alternative HTML version deleted]]

I am trying to implement an ANOVA on a pair of quantile regression models in R. The anova.rq() function performs a basic check to see whether the models are nested, but I think this check is failing in my case. I think my models are nested despite the anova.rqlist() function saying otherwise. Here is an example where the GLM ANOVA regards the models as nested, but the quantile regression ANOVA

...2 machines. If so, ignore the above. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Dec 5, 2017 at 12:17 PM, Cade, Brian <cadeb at usgs.gov> wrote: > I am helping a student with some logistic regression analyses and we are > getting some strange inconsistencies regarding a warning about factor > levels being dropped when running predict.glm(, newdata = ournewdata) on > the logistic regression model object....

...ate the simplest model with just an intercept term, the $effects for the n observations differ from $y by a constant quantity except for one of the values. Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: cadeb at usgs.gov <brian_cade at usgs.gov> tel: 970 226-9326 [[alternative HTML version deleted]]

...d to believe that components="response" , type = "response" would do that but that does not seem to be the case. Thank you Brian Brian S. Cade, PhD U. S. Geological Survey (emeritus) Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: cadeb at usgs.gov<mailto:brian_cade at usgs.gov> tel: 970 404-0447 [[alternative HTML version deleted]]

Hi, I am currently trying to do a GLMM on a dataset with percent cover of seagrass (dep. var) and a suite of explanatory variables including algal (AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours. M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/fSi/fTr), family=binomial,data=data,nAGQ=1) As the dependent variable is percent cover, I used a binomial error structure. I also have a

Dear moderator, If possible I would like to send in the following question for R-help: I am analyzing a small data set using PGLS with phylogenetic uncertainty taken into account and thereby including 100 potential phylogenetic tree scenarios. I've managed to run models on all of the different trees and performed model averaging to get parameter estimates for the intercept and most of the

I have a recurring graphics issue that I've not been able to resolve with R. If I make a series of regression estimates and then plot the estimated function for the regression lines over a scatter plot of the data, e.g., using a sequence of plot( ) and lines ( ) similar to those below

Hi, A following function was kindly provided by GGally?s maintainer, Barret Schloerke. function(data, mapping, ...) { p <- ggplot(data = data, mapping = mapping) + geom_point(color = I("blue")) + geom_smooth(method = "lm", color = I("black"), ...) + theme_blank() + theme(panel.border=element_rect(fill=NA, linetype =

List: I'm helping a colleague with some Poisson regression modeling. He uses SAS proc GENMOD and I'm using glm() in R. Note on the SAS and R output below that our estimates, standard errors, and deviances are identical but what we get for likelihoods differs considerably. I'm assuming that these must differ just by some constant but it would be nice to have some confirmation

Hi Everyone, I am uncertain that I am writing the contrast statements correctly. Basically, I'm unsure when to use a -1 and a 1 when writing the contrasts. Specifically I am interested in comparing the slopes between different temperature regimes. Temperature is therefore a factor. Time and percent are numerical. Using the gmodels package I made the following model:

I originally sent this to Doug Bates but have received no reply yet so I thought I would expand to a wider source. I've been trying to estimate linear mixed effect models in lmer() from the lme4 package using the weights option. The help and code for lmer() suggest to me that this is implemented but I can't seem to get it to do anything with weights = , no error message reported it

Does anyone have code for computing the standard error of the predicted mean count from a zero-inflated Poisson regression model estimated by the zeroinfl() function from the pscl package (and yes, we've checked with A. Z. already)? Thank you Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email:

I recently lost the partitions on my hard drive (second time in 6 months) so I had to have our IT folks image all my files over to a new drive. I completely reinstalled R (now 2.15.2) and all my libraries to my computer (Dell Latitude running Windows 7). A few of my previous workspaces (created with R 2.14.1) can't be restored, reporting an error similar to the one I get when I try to

Just noticed that I get a similar error about object 'kronecker' in "Matrix" package when trying to load "lme4". So this is a more pervasive problem. Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: brian_cade@usgs.gov tel: 970 226-9326 ----- Forwarded by Brian S