similar to: quantreg installation and conflicts with R 2.15.2

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 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

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'm having some troubles with the Quantreg package. I am using R version 2.10.0, and have downloaded the most recent version of Quantreg (4.44) and SparseM (0.83 - required package). However, when I try to run an analysis (e.g. fit1<-rq(y~x, tau=0.5)) I get an error message saying that the function "rq" could not be found. I get the same message when I try to search

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

Dear all, I just installed the new version of R, 2.10.1, and I am currently using the package sparseM. (I also use a 64 bit windows version) I got a problem that I never had: when I try to multiply with a kronecker product (%x%) two sparse matrixes I get the following message: Error in dim(x) <- length(x) : invalid first argument I never had this problem with previous versions of R. May

I have recently started using R and want to use the quantreg (and sparseM) packages. I downloaded the .tar files for each, and placed the subsequent folders into the library folder in the frameworks/R.framework/ resources/library folder with all the other packages. When I try to load either package from the package manager window I get: Loading required package: SparseM Error in

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

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

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)

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'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

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 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

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

Hey, My dataset has 1 dependent variable(Logloss) and 7 independent dummy variables(AS,AM,CB,CF,RB,RBR,TS) , it's attached in this email. The problem is I cant finish Khmaladze test because there's an error "Error in switch(mode(x), "NULL" = structure(NULL, class = "formula"), : invalid formula" which I really dont know how to fix. My R version is 2.7.2.

R-ers: I was wondering if anyone had suggestions on how to implement a GAM in a quantile fashion? I'm trying to derive a model of a "hull" of points which are likely to require higher-order polynomial fitting (e.g. splines)-- would quantreg be sufficient, if the response and predictors are all continuous? Thanks! --j

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 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 have a set of values and their corresponding weights. I can use the function weighted.mean to calculate the weighted mean, I would like to be able to similarly calculate the weighted median and quantiles? Is there a function in R that can do this? thanks, Spencer [[alternative HTML version deleted]]