similar to: Adjusted survival curves

For adjusted survival curves I took the sample code from here: https://rpubs.com/daspringate/survival and adapted for my date, but got error. I would like to understand what is my mistake. Thanks! #ADAPTATION FOR MY DATA library(survival) library(survminer) df<-read.csv("F:/R/data/base.csv", header = TRUE, sep = ";") head(df) ID start stop censor sex age stage treatment 1

Adjusted survival curves (Thanks to sample code: https://rpubs.com/daspringate/survival ) Thanks to Moderator/Admin's Great Work! For a successful solution I used advice that could be understood: 1. Peter Dalgaard: The code does not work, because the covariates are not factors. 2. Jeff Newmiller: "Change the columns into factors before you give them to the coxph function, e.g.

Adjusted survival curves. (Sample code here: https://rpubs.com/daspringate/survival ) Deep gratitude?to Moderator/Admin! At?David Winsemius prompt, more elegant working code:Thanks, Ted :) library(survival) library(survminer) df<-read.csv("F:/R/data/edgr-orig.csv", header = TRUE, sep = ";") df2 <- df df2[,c('treatment', 'age', 'sex',

I got advice here that I didn't understand! Can I ask to explain me the meaning of this procedure: first get the structure, and then assign it back. For what? Thanks!? (Great thanks to Moderator/Admin!) You should learn to post in plain text and use dput to present your data structures. At your console do this dput(treat) # and this will appear. Copy it to your plain-text message:

The combination of survfit, coxph, and factors is getting confused. It is not smart enough to match a new data frame that contains a numeric for sitenew to a fit that contained that variable as a factor. (Perhaps it should be smart enough to at least die gracefully -- but it's not). The simple solution is to not use factors. site1 <- 1*(coxsnps$sitenew==1) site2 <-

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

Hola a todos, A partir de los siguientes datos: d <- list(`1` = structure(list(ped = c(1L, 1L, 1L, 1L, 1L, 1L, 1L), id = 1:7, father = c(2L, 0L, 0L, 2L, 2L, 2L, 2L), mother = c(3L, 0L, 0L, 3L, 3L, 3L, 3L), sex = c(2L, 1L, 2L, 2L, 2L, 1L, 2L), affected = c(1L, 2L, 1L, 1L, 2L, 2L, 2L)), row.names = c("1", "2", "3", "4", "5",

Dear all, I am facing some problem with how to fit a "Baseline category Logit model" with R. Basically I am considering famous "Alligator" data as discussed by Agresti. This data can also be found here: https://onlinecourses.science.psu.edu/stat504/node/174 (there is also an accompanying R file, however the underlying R code could not load the data properly!!!) Below are

Dear R experts, I am trying to analyze data from an article, the data looks like this Patient Age Sex Aura preCSM preFreq preIntensity postFreq postIntensity postOutcome 1 47 F A 4 6 9 2 8 SD 2 40 F A/N 5 8 9 0 0 E 3 49 M N 5 8 9 2 6 SD 4 40 F A 5 3 10 0 0 E 5 42 F N 5 4 9 0 0 E 6 35 F N 5 8 9 12 7 NR 7 38 F A 5 NA 10 2 9 SD 8 44 M A 4 4 10 0 0 E 9 47 M A 4 5 8 2 7 SD 10 53 F A 5 3 10 0 0 E 11

Hello- I have a dataset which basically looks like this: Location Sex Date Time Verbal Self harm Violence_objects Violence A 1 1-4-2007 1800 3 0 1 3 A 1 1-4-2007 1230 2 1 2 4 D 2 2-4-2007 1100 0

Dear everyone I am a bit of a computer imbecile and are having problems with R. I am using R in my research project to do chi-square tests on data imported from excel . However I have som missing data in one of my variables (columns) and I need R to "exclude" these and make chi-square test on the data that I have. I use a formula to make 2x2 tables which is: data <-

Hello users, I would like to obtain a survival curve from a Cox model that is smooth and does not have zero differences due to no events for those particular days. I have: > sum((diff(surv))==0) [1] 18 So you can see 18 days where the survival curve did not drop due to no events. Is there a way to ask survfit to fit a nice spline for the survival?? Note: I tried survreg and it did not

Dear List I am trying to fit a multinomial model using the mlogit package. Attempting to load the data into mlogit presents the following error. MLOG<-mlogit.data(Mult3,shape="long",choice="CHOICE",alt.var="mode.ids",indivs = "set3",chid.var = "obs") Error in `row.names<-.data.frame`(`*tmp*`, value = c("1.1", "1.2",

Hi, Sorry, but how did you bring it out? Thanks On Fri, Dec 28, 2012 at 8:48 AM, arun kirshna [via R] < ml-node+s789695n4654093h10@n4.nabble.com> wrote: > Hi, > bp.sub<- structure(list(CODEA = c(1L, 3L, 4L, 7L, 8L, 9L, 10L, 11L, 12L, > 13L, 14L, 16L, 17L), C45 = c(NA, 2L, 2L, 2L, 2L, 1L, NA, 1L, > 1L, 2L, 1L, 2L, 1L), ragek = c(3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, > 3L, 3L,

Hello I'm trying to make survival curves for some longevity data - 100 males and 100 females, some of which are still living (not dead at the end of survey) I would like to make sex specific survival curves as time on the X axis, proportion alive on the Y, and a line for each sex (two lines) Data looks like this: Focal DOB DOD Longevity Sex 1 89-04-20 na

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 column in a data frame looking something like: $sex $language $count male english 0 male english 0 female english 32 male spanish 154 female english 11 female norweigan 7 and so on. What I want to do is to order these in to categories, for instance one category where count>=0 & count<10 and so on.. I want my data to turn out looking something like: male

Hello, I'm using plot.survfit to plot cumulative incidence of an event. Essentially, my code boils down to: cox <-coxph(Surv(EVINF,STATUS) ~ strata(TREAT) + covariates, data=dat) surv <- survfit(cox) plot(surv,mark.time=F,fun="event") Follow-up time extends to 54 weeks, but the last event occurs at week 30, and no more people are censored in between. Is there a

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

Dear all, I am having a (really) hard time getting pyears to work together with a ratetable to give me the number of expected events (deaths). I have the following data: dos, date of surgery, as.Date dof, date of last follow-up, as.Date dos, date of surgery, as.Date sex, gender, as.factor (female,male) ev, event(death), 0= censored at time point dof, 1=death at time point dof Could someone