search for: covarait

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The code below is an example from the its help document I found on http://www.locfit.info...

Hello ; I am doing a regression of count data (number of award and there are some covariates) I have estiamted the parameters of negative binomial distribuion (lambda is a function of covaraites, GLM model) by glm.nb function and training dataset. Now I want to predict the number of award (for example y=0, y=1, y=2,) or testing dataset. I dont know how to calculate this numbers? I would be very grateful if anybody could help me. thanks [[alternative HTML version deleted]]

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]]

...ring is an issue, at least not for the reason that you discuss. The basic censoring assumption in the Cox model is that subjects who are censored have the same future risk as those who were a. not censored and b. have the same covariates. The real problem with informative censoring are the covaraites that are not in the model; ones that I likely don't even know exist. Assume for instance that some unknown exposure X, Perth sunlight say, makes people much more likely to get both of the outcomes. Assume further that it matters, i.e., the study includes a reasonable number of people wi...

Hi all, I'm very confused! I've been using the same code for many weeks without any bother for various covariates. I'm now looking at another covaraite and whenever I run the code you can see below I get an error message: "Error in rep(0, nrow(data)) : invalid 'times' argument" This code works: # remove 'missing' cases from data # snearma <- function(data) { for(i in 1:nrow(data)){ if(is.na(data$all.sp)[i...