similar to: coxph data format

All, Is there an efficient way to apply say "mean" or "median" to a dataframe according to say all combinations of two variables in the dataframe? Below is a simple example and the outline of a "manual" solution that will work but is not very efficient (could also generalize this to a function). Searched the archives and docs but didn't see anything close to

Dear R users, I use the excelent Anova function of the library car because the easy way to get sphericity correction. Unless I use the scan function. I have not been able to access the values ​​of sum squares and degrees of freedom for each effect in the univariate summary table. Example of the car library for Anova function: library(car) phase <- factor(rep(c("pretest",

Hi, I am encountering a difficulty I don't understand. Be patient, I'm very new to analysis of variance. If I load this data: example12_7=read.table("http://msemac.redwoods.edu/~darnold/math15/data/chapter12/example12_7.dat",header=TRUE) The run the oneway.test: oneway.test(time~drug,data=example12_7,var.equal=TRUE) I get these results: data: time and drug F = 4.1881, num

Hi, I'm trying to do a simple survival analysis on some data, and I'm having the following problem (here's my code and the error message): out <- Surv(fup,event=status) Error in Surv(fup, event = status) : argument "time2" is missing, with no default >From reading the documentation, it seems that I should be able to simply write: Surv(time1, event) if my data is

Hi As I can't find an example of my data structure I'd like some advice on which is the most appropriate test for significant effects. If I should be using either lme or anova, is the relevant example below the best/correct way to do the test? The Data... 2 groups of patients (5 in GroupA, 7 in GroupB) 3 short acting drugs, (I'm not concerned with residual effects from the previous

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as

Hi all, thank you for your patience. I am dealing with a large dataset detailing patients and medications Medications are hard to code, as they are (usually) meaningless unless matched with doses. I have a dataframe with vectors (Drug1, Drug2..... Drug 16) and individual patients are represented by rows. The vectors are actually factors, with 100s of possible levels (all the drugs the patient

Hello dear R members. I have been learning the Anova syntax in order to perform an SS type III Anova with repeated measures designs (thank you Prof. John Fox!) And another question came up: where/what are the (between/within) residuals for my model? ############ Play code: phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),

I'm a journalist, wondering what questions to ask about a study that contrasted the impact on serum cholesterol of two drugs. This was a 40 dog study: 5 treatment blocks of 4 dogs each, randomized to: a control block, two blocks at different doses of drug1 and two at corresponding doses of drug2. Analysis was 2-factor repeated measures ANOVA on treatment group and sampling time. Linear

For some time I have been looking for a convenient way of performing post-hoc analysis to Repeated Measures ANOVA, that would be acceptable if sphericity is violated (i.e. leaving aside post-hoc to lme models). The best solution I found was John Fox's proposal to similar requests in R-help: http://tolstoy.newcastle.edu.au/R/e2/help/07/09/26518.html

Hey all, I'm trying to replicate some plots with ggplot2. The problem is that I need to specify the color for every attribute (drug). If I use the code below the colors get automatically assigned but I need to plot drug1 in black drug2 in blue etc. How do I do that? q = qplot(days,vol,data = cellLine7064, color = drug, geom = c("line", "point")) best regards, Immanuel

Please help with this error message drugbook is an 885 x 32 dataframe >names(drugbook) [1] "DRUG1" "DRUG2" "DRUG3" "DRUG4" "DRUG5" [6] "DRUG6" "DRUG7" "DRUG8" "DRUG9" "DRUG10" [11] "DRUG11" "DRUG12"

Hi! I'm trying to perform propensity score matching on survey data and so for each individual observation I have a statistical weight attached. My question is: is there a way within the package to consider these weights in the matching procedure? Thank you very much. -- Maria Cristina Maurizio [[alternative HTML version deleted]]

All, When specifying colors to xyplot w/ a groups argument, using auto.key no longer maintains the colors properly. I've searched the docs and help but haven't found exactly what I need ... I saw a few examples in the archives involving par.settings but that doesn't seem to do it. I also saw some people using key instead of auto.key, but that didn't seem consistent. Is there a

Dear all, Does the frailty.object$history[[1]]$theta returns the Variance of random effect? Why is the value different? Here is an example with kidney data: > library(survival) > data(kidney) > frailty.object<-coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney) > frailty.object Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id), data

Dear all, I have been doing some frailty calculations and been facing some difficulties. I can extract coefficients, value of theta and the following things library(survival) fit<-coxph(Surv(time,status)~covariate+frailty(group), data=simulated.data) fit$coef fit$history[[1]]$theta fit$history[[1]]$c.loglik fit$var fit$var2 from a frailty included coxph object: but how can i know what other

Dear List, In documents (Therneau, 2003 : On mixed-effect cox models, ...), as far as I came to know, coxme penalize the partial likelihood (Ripatti, Palmgren, 2000) where as frailtyPenal (in frailtypack package) uses the penalized the full likelihood approach (Rondeau et al, 2003). How, then, coxme and coxph(...frailty(id, dist='gauss')) differs? Just the coding algorithm, or in

Hi, I ran the example on pp. 799-800 from Machael Crawley's "The R Book" using package survival v. 2.36-5, R 2.13.0 and RStudio 0.94.83. The model is a Cox's Proportional Hazards model. The result was quite different compared to the R Book. I have compared my code to the code in the book but can not find any differences in the function call. My results are attached as well as a

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

Data set up as one observation/subject looks like (with a total of 10 subjects) Two treatments: shoe type with 3 categories and region with 8 categories ==> 24 "treatment" columns Subject PHallux PMidToes PLatToe PMTH1 PMidMTH PLatMTH PMidfoot PRearfoot LHallux LMidToes LLatToe LMTH1 LMidMTH LLatMTH LMidfoot LRearfoot DHallux DMidToes DLatToe DMTH1 DMidMTH DLatMTH