similar to: standard error for predicted mean count from ZIP

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

I know I must be missing something obvious, but checking help and googling a bit did not turn up a useable answer. When I've estimated a glm() model object (my example is with just identity link with gaussian family so I could have used lm() instead), one of the terms returned in the model object is listed as $effects. What are these quantities? I have not been able to relate them to the

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

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)

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'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 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 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

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

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 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

Ok so I see the problem that I'm having creating a new variable (LAG1DBC) in the example data transformation below is that tapply() is creating a list that is not dimensionally consistent with the data frame (data). So how do I go from the list output of tapply() to create a dimensionally consistent vector that can create the new variable in my original data frame? I've been trying

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

Dear all, A question related to the following has been asked on R-help before, but I could not find any answer to it. Input will be much appreciated. I got an unexpected sign of the "slope" parameter associated with a covariate (diam) using zeroinfl(). It led me to compare the estimates given by zeroinfl() and hurdle(): The (significant) negative estimate here is surprising, given

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:

Hi all, I have been trying to use zeroinfl() with the pscl package with R version 2.1.1. and with the newest versions of the contrib packages compatible with R 2.1.1. I have read the examples, the vignette and all the posts relating to zeroinfl() but I am still confused as to how to structure the model. Here is a small example; the error message is the same for big data sets

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

Dear R-Helpers, Can anyone please help me to interpret warning messages from zeroinfl (package pscl) while fitting a zero inflated negative binomial model? The console reports convergence and the parameters seam reasonable, but these <<Warning messages: 1: algorithm did not converge in: glm.fit(X, Y, family = poisson()) 2: fitted rates numerically 0 occurred in: glm.fit(X, Y, family =

Hi I have been using a Zero-Inflated negative binomial model fitted using the pscl zeroinfl command but I would like to extract a goodness of fit measure are there any suitable pseudo R^2 measures available for this type of analysis to try and assess the amount of variation in the data explained by the model? I have tried with the pR2 command in pscl (for computing various pseudo R2