similar to: glm$effects

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. We have checked multiple times that the factor levels have been defined similarly on both data sets (one used to estimate model and the newdata)

A guess (treat accordingly): Different BLAS versions are in use on the two different machines/versions. In one, near singularities are handled, and in the other they are not, percolating up to warnings at the R level. You can check this by seeing whether the estimated fit is the same on the 2 machines. If so, ignore the above. -- Bert Bert Gunter "The trouble with having an open mind

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

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

I think I must be missing something obvious, but I'm having trouble getting a data transformation to work on groupings of data within a data frame (csss3) as defined by 2 factors (population, locid). The data are sorted by year within locid within population and I want to lag another variable (dbc), i.e, shift them down by 1 row replacing the first row with NA, within groups defined by

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

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

I've looked for this information in all the R help sources I could find and found nothing. Is it possible to use some function key to stop the execution of some R command without ending the R session (Windows, R 1.91)? I've several times started functions that for various reasons are not executing properly and it would be nice to stop them without killing the R session. I've been

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

Having finally updated from R 1.91 to R 2.2.0 with my installation of a new computer, I discovered that something has changed drastically about the way code for contributed packages is stored when installed in a local version of R. In the 1.* versions it was easy for me to go in and modify some of the code for a contributed package by using a text editor to change the script files (these

I am estimating fairly simple zero-inflated negative binomial models in glmmTMB. The models have just two factors and their interaction, a total of 4 levels. I was trying to use emmeans() to obtain estimates and 95% CI for these four levels. However, when I use emmeans() with the arguments component="response", type ="response", I do not get estimates that look like the

Hi I am using the quantreg package for median regression for a large series of subsets of data. It works fabulously for all but one subset. When it reaches this subset, R takes the command and never responds. I end up having to kill R and restart it. It appears to be something with the particular data subset, but I can't pinpoint the problem. Here are some details Operating system:

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

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

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 =

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

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:

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 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

Dear colleagues, has anyone an idea how to carry out a nonparametric manova for comparing K groups? has anyone a good package of non-parametric stats? Thank you for your help. -- José David Gómez Químico Farmacéutico Universidad Nacional [[alternative HTML version deleted]]