similar to: Negative Binomial Model

Hi All, I have a dataset, longitudinal in nature, each row is a 'visit' to a clinic, which has numerous data fields and a count variable for the number of 'events' that occurred since the previous visit. ~50k rows, ~2k unique subjects so ~25 rows/visits per subject, some have 50 some have 3 or 4. In STATA there is an adjustment for the fact that you have multiple rows per

Dear Lister, I am facing a strange problem fitting a GLM of the negative binomial family. Actually, I tried to estimate theta (the scale parameter) through glm.nb from MASS and could get convergence only relaxing the convergence tolerance to 1e-3. With warning messages: glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3)) There were 25 warnings (use warnings() to see

Hi, Can someone tell me if it is possible to set the dispersion parameter constant when fitting a negative binomial glm in R? I've looked at the documentation and can't find the appropriate argument to pass. In STATA I can type: nbreg depvar [indepvar...], offset(offset) dispersion(constant). Thank you [[alternative HTML version deleted]]

I looked for examples of count data that might interest the students and found this project about dropout rates in Los Angeles High Schools. It is discussed in the UCLA stats help pages for the Stata users: http://www.ats.ucla.edu/stat/stata/library/count.htm and See: http://www.ats.ucla.edu/stat/stata/library/longutil.htm To replicate those results, I used R's excellent foreign package to

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

Dear all, I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit variables. However, most models fail because “the global deviance is increasing” and I am not sure what causes this behaviour. The dataset consists of counts of birds (duck) and 5 habit variables measured in the field (n= 182). The dependent

Hi, I am relatively new to R; when creating functions, I run into problems with missing values. I would like my functions to ignore rows with missing values for arguments of my function) in the analysis (as for example is the case in STATA). Note that I don't want my function to drop rows if there are missing arguments elsewhere in a row, ie for variables that are not arguments of my

When using function heckit() from package ‘sampleSelection’, is there anyway to make t-tests for the coefficients using robust covariance matrix estimator? By “robust” I mean something like if a had an object ‘lm’ called “reg” and then used: > coeftest(reg, vcov = vcovHC(reg)). I’m asking this because in Stata we could use function heckman and then use vce option “robust”. We could do the

Hi all, I need a hint how to load *nbi images directly with gpxe /pxelinux etc. I have some networkboot images available only as *.nbi, loaded by a startrom.0 with "kernel startrom.0" and then a *.lst for the menue of the nbi images. The *nbi images and the startrom.0 is closed source and I have found no solution to unpack and patch them. I need to boot the images directly without the

I have a floppy boot image which loads the NBI from the linux server. I want to create a bootable CD and copy the NBI file on the cd and have the NBI called frmo the CD instead of the network server How can SYSLINUX / PXELNIUX help me. Pls advise. sandeep __________________________________________________ Do you Yahoo!? Yahoo! Web Hosting - Let the expert host your site

Hi, there Since glm cannot handle factors very well. I try to use bigglm like this: logit_model <- bigglm(responser~var1+var2+var3, data, chunksize=1000, family=binomial(), weights=~trial, sandwich=FALSE) fitted <- predict(logit_model, data) only var2 is factor, var1 and var3 are numeric. I expect fitted should be a vector of value falls in (0,1) However, I get something like this:

Hi all, I need some suggestions or hints which syslinux version I shall use. The main focus will be on PXE boot. I have to upgrade due to (expected) performance problems (tftp), so I will swich to HTTP boot ! existing system: - syslinux version 4.05 (tftp only) - used modules : pxelinux.0, memdisk, vesamenuc32, patched startrom.0 - I boot .nbi , .iso, .ima and sometimes files (clonezilla etc.) -

Hello Is there any function to calculate White's standard errors in R in an NLS regression. The sandwich and car package do it but they need an lm object to calculate the error's. Does anyone have idea how to do it for an NLS object ? Regards The woods are lovely, dark and deep But I have promises to keep And miles before I go to sleep And miles before I go to sleep ----- [[alternative

Dear all, how can I use vcovHC() to get robust/corrected standard errors from an arima() output? I ran an arima model with AR(1) and got the estimate, se, zvalue and p-value using coeftest(arima.output). However, I cannot use vcovHC(arima.output) to get corrected standard errors. It seems vcovHC works only with lm and plm objects? Is there another way I can get robust/corrected

I am looking for code that allows for a more flexible negative binomial model (similar to Stata's "gnbreg"). In particular, I am looking to be able to model the ancillary shape parameter in terms of a series of covariates. So if, y[i] ~ poisson(mu[i]) mu[i] = exp(x[i]beta + u[i]) exp(u[i]) ~ Gamma(1/alpha, alpha) I am looking to parameterize alpha as exp(z[i]gamma). If you

Hi, I am running a negative binomial model using Gamlss and when I try to include random effect, I get the following message: Warning messages: 1: In vcov.gamlss(object, "all") : addive terms exists in the mu formula standard errors for the linear terms maybe are not appropriate 2: In vcov.gamlss(object, "all") : addive terms exists in the sigma formula standard

Hi! I'm preparing what should be a general "network boot" solution for the ones that needs to do network boot on PC without BIOS support / ROM chips. It supports both PXE (like pxelinux.0) and NBI images. I've prepared a precompiled multi-driver image that can autodetect and handle nearly 30 PCI and ISA NICs. My "remote boot kit" contains the same image in 4

Hello; Does anybody knows that R have a function for Generelized Negative Binomial model, something like "gnbreg" in "STATA" where dispersion parameter itself is a function of covaraites ? Thanks; [[alternative HTML version deleted]]

Dear R-help, I was recently wanting to use GEE with the negative binomial "family". It seems that this is lacking in the otherwise excellent implementations of the GEE methodology ( packages: gee, yags, geepack). I would have thought it a simple step to allow the creation of a family, i.e providing the link function (log mu) and the variance function (mu + mu^2/theta) , assuming theta

Dear all, I used the glm.nb with the default values from the MASS package to run a negative binomial regression. Here is a simple example: set.seed(123) y <- c( rep(0, 30), rpois(70, lambda=2) ) smoke <- factor( sample( c("NO", "YES"), 100, replace=T ) ) height <- c( rnorm(30, mean=100, sd=20), rnorm(70, mean=150, sd=20) ) fit <- glm.nb( y