similar to: 1 continuous non-normal variable ~ 4 factors + 1 continuous covariate (with interactions)

Dear all, I'm running a coxph model of the form: coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3) Within this model, I would like to compare the influence of x1 and x2 on the hazard rate. Specifically I am interested in testing whether the estimated coefficient for x1 is equal (or not) to the estimated coefficient for x2. I was thinking of using a Chow-test for this but the Chow

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

Dear readers, I have a basic question about how to use lme for my design. I haven't been able to find an example in r-help that made it clear to me how to tackle this problem and unfortunately I also cannot get hold of Pinheiro & Bates 2000. I hope someone can help. Data for the response variable "foan" were collected in: · 60 plots · plots were re-sampled yearly

Dear list members, I am working with a Cox ph model for the duration of unemployment. The event of interest in my analysis is getting employed. I have various background variables explaining this event: age, sex, education etc. I have multiple unemployment spells per person. I use a model with person-specific frailty terms in order to take into account the correlation of spells by the same

Hello mailing list! I would like to know, how I can introduce a covariate in a glm, I've two factors and a covariate. Thank you very much! _______________________________________________________________________________________________ Michelangelo La Spina Equipo de Protección de cultivos - Control Biológico Departamento de Biotecnología y Protección de Cultivos Instituto Murciano de

Does anyone know how to make a plot like Fig. 4 of Paik, et al., New England Journal of Medicine, Dec. 30, 2004? Given survival data and a covariate, they plot a curve giving "Rate of Distant Recurrence at 10 Yr (% of patients)" on the y-axis versus the covariate on the x-axis. They also plot curves giving a 95% confidence interval. Thanks very much. -Ben The information in this

Dear All, I am a researcher doing research in plant growth and I have a statistical problem that seems to not be able to handle. Recently, I conducted an experiment about plant growing in three different nutrient-level sediments. I harvested these every three week (three harvests in all). Some growth traits of these plants were recorded (e.g. total biomass, leaf biomass and stem biomass). In

Hi All, I'm learning the R codes for Cox PH modeling. Could I ask you what the function of "factor" in modeling? Thank you! When dealing with the categorical covariates (for example 3 groups), it will come out different results if we add the command "factor" in front of the categorical covariate or not: if we don't add "factor", there is only one

Hi everyone, I am fitting a cox proportional hazard model with a continuous variable "x" as the covariate: fit<-coxph(Surv(time, status)~x) Now I wanted to make a plot of survival probability vs. the covariate, and the 95% confidence interval for the survival probability. It's just like a Kaplan-Meier Survival curve, except now the x axis represents the value of covariate, not

I need to remove certain interactions and keep only the one between the second level of the factor and the continuous var t2 bin4 <- glm(resp2~ t*t2+c5.vrm,data=dfa,family="quasibinomial") > summary(bin4) Call: glm(formula = resp2 ~ t * t2 + c5.vrm, family = "quasibinomial", data = dfa) Deviance Residuals: Min 1Q Median 3Q Max -6.5464

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that

Dear r-users, Does anyone know how to draw time-dependent survival curves? Example: Event outcome: CHD Time-dependent covariate: NSAID use, which changes over time for each subject I'm interested in survival curves stratified by NSAID use. I'd like to implement Simon & Makuch (1984) method. Is there a R package/function to draw this graph?

Using a p-value to make any kind of decision is questionable to begin with, and especially unreliable in choosing covariates in regression. Old studies, e.g. by Walls and Weeks and by Bendel and Afifi, have shown that if predictive ability is the criterion of interest and one wishes to use p-values for deciding whether to include a covariate, one should set the p-value bar very large, at 0.25 and

Hello, I have been trying to get an ANOVA table for a linear model containing a single nested factor, two fixed factors and a covariate: carbonmean<-lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto + Zoop*Diversity/Phyto) where, Mean.richness is a covariate, Zoop is a categorical variable (the species), Diversity is a categorical variable (Low or High), and Phyto (community

Hi I am trying to determine the mean of a Weibull function that has been fit to a data set, adjusted for a categorical covariate , gender (0=male,1=female). Here is my code: library(survival) survdata<-read.csv("data.csv") ##Fit Weibull model to data WeiModel<-survreg(Surv(survdata$Time,survdata$Status)~survdata$gender) summary(WeiModel) P<-pweibull(n,

Hi, I have a question about how to do covariate adjustment. I have two sets of 'gene expression' data. They are from two different tissue types, 'liver' and 'brain', respectively. The purpose of my analysis is to compare the pattern of the whole genome 'gene expression' between the two tissue types. I have 'age' and 'sex' as covariates. Since

Hello! I am using rhierMnlRwMixture from bayesm package. I would like to use it with a categorical covariate (Z). I have 2 clariciation questions: 1. If the covariate is categorical, do I have to represent it as dummy variable(s)? (e.g., 2 dummy variables for a 3-level categorical variable)? 2. Do those dummy variables have to be centered? Help file for rhierMnlRwMixture says: "Z should not

I have a question about TukeyHSD and the glht function because I'm getting different answers when a covariate is included in model for ANCOVA.? I'm using the cabbages dataset in the 'MASS' package for repeatability.? If I include HeadWt as a covariate, then I get different answers when performing multiple comparisons using TukeyHSD and the glht function. The difference appears

Hi all, I am trying to model (continuous) spatial variation in a response variable as a function of one or more of several explanatory variables. I am principally interested in obtaining some measure of the relative "importance" of the explanatory variables. I have found several R libraries that are tailored to this sort of problem (geoR, geoRglm, gstat, etc.); however, as near

dears, I like two write a program with R to estimate the coefficients of covariate,I like two know the original program for this programing for partial maximum likelihood for cox models with two co variate. I did it with coxph command, thanks [[alternative HTML version deleted]]