similar to: Different behavior of model.matrix between R 3.2 and R3.1.1

Terry Therneau has been very helpful on r-help but we can't figure out what change in R in the past months made extra columns appear in model.matrix when the terms object is subsetted to remove stratification factors in a Cox model. Terry has changed his logic in the survival package to avoid this issue but he requires generating a larger design matrix then dropping columns. A simple

Martin, ? There are a couple of issues with [.terms that have bitten my survival code.? At the useR conference I promised you a detailed (readable) explanation, and have been lax in getting it to you. The error was first pointed out in a bugzilla note from 2016, by the way.? The current survival code works around these. Consider the following formula: <<testform>>=

OS X R 4.3.3 Colleagues I have created objects using the Surv function in the survival package: > FIT.1 Call: survfit(formula = FORMULA1) n events median 0.95LCL 0.95UCL SUBDATA$ARM=1, SUBDATA[, EXP.STRAT]=0 18 13 345 156 NA SUBDATA$ARM=2, SUBDATA[, EXP.STRAT]=1 13 5 NA 186 NA SUBDATA$ARM=2, SUBDATA[, EXP.STRAT]=2 5

Hi Dennis, look at the help page for summary.survfit, the Value n.event. G?ran On 2024-05-15 22:41, Dennis Fisher wrote: > OS X > R 4.3.3 > > Colleagues > > I have created objects using the Surv function in the survival package: >> FIT.1 > Call: survfit(formula = FORMULA1) > > n events median 0.95LCL 0.95UCL >

Dear Helpers   I have a sample frame and i have sampled from it using three methods and now i want to calculate the statistics but i only get the population parameters.   H <- matrix(rnorm(100, mean=50000, sd=5000)) sampleframe=data.frame(type=c(rep("H",100)),value=c(H)) sampleframe   str=strata(sampleframe,c("type"),size=c(20,), method="srswor")

I am a relative newbie to survival analysis and R in general, but would like to use the coxme package to analyse some data I currently have. The data is relative to survival times of drosophila melanogaster populations to infection with pathogens, and has the variables: Time, Status, Treatment (4 treatments + 2 controls) Population Replicate ?and I'm currently using the following call

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

Full_Name: Marc Mamin Version: 1.8, 2.0.0 OS: Windows & Linux Submission from: (NULL) (217.17.202.254) Using only the beginning of a column name will match it: >aaa<-1 >df<-as.data.frame(aaa) >names(df) [1] "aaa" >df$a [1] 1 !!!!!!!!! (I expect df$a to be undefind) >df$x >NULL Compare with: > df["aaa"] aaa 1 1 > df["a"]

On 06/11/2015 7:36 AM, Therneau, Terry M., Ph.D. wrote: > I am currently puzzled by a seach path behavior. I have a library of a dozen routines > getlabs(), getssn(), getecg(), ... that interface to local repositories and pull back > patient information. All have a the first 6 arguments in common, and immediately call a > second routine to do initial processing of these 6. The

Please forgive me if you already received this. I had an e-mail sending glitch this morning. http://cran.r-project.org/src/contrib/checkSummary.html reported an error in Design.trans.Rd * checking Rd files ... ERROR Rd files with syntax errors: /var/mnt/hda3/R.check/r-devel/PKGS/Design/man/Design.trans.Rd: unterminated section 'alias' The .Rd file is attached. It begins

Hello R Experts, I kindly request your assistance on figuring out how to get a stratified random sampling proportional to 100. Below is my r code showing what I did and the error I'm getting with sampling::strata # FIRST I summarized count of records by the two variables I want to use as strata Library(RODBC) library(sqldf) library(sampling) #After establishing connection I query the data

Hi all, I am aware that crr lacks the "friendly" command structure of functions such as cph. All is clear to me about including covariates until I want to include a stratification term in the competing risk framework (no nice strat command). I am still a bit of a novice in R - I am looking for an example to help me with this, but can't seem to find one. Any advice appreciated (no

Dear R-help, I am having a problem with the interpretation of result from validate.cph in the Design package. My purpose is to fit a cox model and validate the Somer's Dxy. I used the hypothetical data given in the help manual with modification to the cox model fit. My research problem is very similar to this example. This is the model without stratification: > library(Design) > f1

I am currently puzzled by a seach path behavior. I have a library of a dozen routines getlabs(), getssn(), getecg(), ... that interface to local repositories and pull back patient information. All have a the first 6 arguments in common, and immediately call a second routine to do initial processing of these 6. The functions "joe" and "fred" below capture the relevant

Good afternoon, I am currently trying to do some work on survival analysis. - I hope to seek your advice re: 2 questions (1 general and 1 specific) (1) I'm trying to do a stratified Cox analysis and subsequently plot(survfit(object)). It seems to work for some strata, but not for others. I have tumor grade, which is a range of 1 - 4. When I divide this range of 1:4 into 2 groups, it

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Hello, I'm using a complex survey dataset and my goal is to simply spit out a bunch of probability-weighted outcome variable means for the different levels of covariate. So I first define the structure of the study design (I'm using the CDC's NHANES data): dhanes <- svydesign(id=~PSU, strat=~STRATA, weight=~lab_weight, data=final, nest=TRUE) No problem there. Now I use the

Dear WizaRds, having met some of you in person in Vienna, I think even more fondly of this community and hope to continue on this route. It was great talking with you and learning from you. Thank you. I am trying to work through an artificial example in post stratification. This is my dataset: library(survey) age <- data.frame(id=1:8, stratum=rep(

Dear R contributors, I have a problem in selecting in an efficient way, rows of a data frame according to a condition, which is a part of a row name of the table. The data frame is made of 64 rows and 2 columns, but the row names are very long but I need to select them according to a small part of it and perform calculations on the subsets. This is the example: X Y "Unique to