similar to: predicting cumulative hazard for coxph using predict

Dear all, I coded a function called u.glm u.glm <- function (x,ahi,age,bmiz,gender) { library(nortest) lil.rslt <- lillie.test(x) if (lil.rslt$p.value >0.05) { cat("Logtrans=0, lillie=",lil.rslt$p.value,"\n") xmodel<-glm(x~ahi+age+bmiz+as.factor(gender)) summary(xmodel) confint(xmodel) } else { cat("Logtrans=1,

Hello, I'm using optim to program a set of mle regression procedures for non-normal disturbances. This is for teaching and expository purposes only. I've successfully programmed the normal, generalized gamma, gamma, weibull, exponential, and lognormal regression functions. And optim returns reasonable answers for all of these compared with the identical optimization problems in STATA and

Dear R-developers: I'm trying to build a 64-bit R-patched_2006-07-24 on SunFire V40z with on Solaris OS 10 64-bit kernel and using Sun Studio 11 compilers. Everything runs OK until it gets to building package tools (all.R) where it fails. Bellow is how I tried it (I can provide any other additional info if needed). Any help please? Thank you very much Latchezar Dimitrov Wake Forest Univ.

Dear Friends, My problem is related to how to measure probabilities from a probit model by changing one independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <-

Good Afternoon, I am relatively new to R and have been trying to figure out how to estimate regression coefficients using the MLE method. Some background: I am trying to examine scenarios in which certain estimators might be preferred to others, starting with MLE. I understand that MLE will (should) produce the same results as Ordinary Least Squares if the assumption of normality holds. That

Dear All, I am trying to fit a simple linear mixed model (see below this paragraph) arising from a crossed factorial design with 2 factors and ubalanced number of replicates (from two to five) in each cell, but I keep getting an error message (see bottom of message). The model is: yijk = intercept + ai + bj + abij + ejik, where: "intercept" is fixed, and the crosss factors, ai, i =

I have a dataset at a hospital level (as opposed to the patient level) that contains number of patients experiencing events (call this number y), and the number of patients eligible for such events (call this number n). I am trying to model logit(y/n) = XBeta. In SAS this can be done in PROC LOGISTIC or GENMOD with a model statement such as: model y/n = <predictors>;. Can this be done

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Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

Hi thanks for your posts so far. Since what I had assumed was a 32 bit kernel which I compiled didn''t work, I religiously set out on compiling a 64 bit version as my system is currently booted in 64 bit mode. I installed SUNStudio12 on the system and set the SPRO_VROOT to point towards it and when I do a make all in usr/src directory, I get the following error when make cwds and runs in

On Feb 12, 2010, at 11:22 AM, Peter Collingbourne wrote: > Some doxygen annotations are attached to the wrong entry, which can be > misleading. This patch fixes the mistake everywhere I could find it. Nice, please send this patch as a non-inline attachment and I'll be happy to apply it, -Chris

Hi, I''m a bit stuck with mocking a unit test of a join model with regards to a validates_uniqueness_of clause. I have class Book < AR:B has_many :clippings various validates_presence_of etc end class Clip < AR:B has_many :clippings various validates_presence_of etc end class Clipping < AR:B belongs_to :book belongs_to :clip validates_uniqueness_of :clip_id,