Displaying 20 results from an estimated 3000 matches similar to: "Problems with coxph and survfit in a stratified model with interactions"
2012 Oct 14
1
Problems with coxph and survfit in a stratified model, with interactions
First, here is your message as it appears on R-help.
On 10/14/2012 05:00 AM, r-help-request@r-project.org wrote:
> I?m trying to set up proportional hazard model that is stratified with
> respect to covariate 1 and has an interaction between covariate 1 and
> another variable, covariate 2. Both variables are categorical. In the
> following, I try to illustrate the two problems that
2009 Sep 08
1
Obtaining value of median survival for survfit function to use in calculation
Hi,
I'm sure this should be simple but I can't figure it out! I want to get the median survival calculated by the survfit function and use the value rather than just be able to print it. Something like this:
library(survival)
data(lung)
lung.byPS = survfit(Surv (time, status) ~ ph.ecog, data=lung)
# lung.byPS
Call: survfit(formula = Surv(time, status) ~ ph.ecog, data = lung)
1
2009 Feb 25
3
survival::survfit,plot.survfit
I am confused when trying the function survfit.
my question is: what does the survival curve given by plot.survfit mean?
is it the survival curve with different covariates at different points?
or just the baseline survival curve?
for example, I run the following code and get the survival curve
####
library(survival)
fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)
2014 Jul 05
1
Predictions from "coxph" or "cph" objects
Dear R users,
My apologies for the simple question, as I'm starting to learn the concepts
behind the Cox PH model. I was just experimenting with the survival and rms
packages for this.
I'm simply trying to obtain the expected survival time (as opposed to the
probability of survival at a given time t). I can't seem to find an option
from the "type" argument in the predict
2013 Jan 17
3
coxph with smooth survival
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
2003 Apr 28
2
stepAIC/lme problem (1.7.0 only)
I can use stepAIC on an lme object in 1.6.2, but
I get the following error if I try to do the same
in 1.7.0:
Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( :
unused argument(s) (formula ...)
Does anybody know why?
Here's an example:
library(nlme)
library(MASS)
a <- data.frame( resp=rnorm(250), cov1=rnorm(250),
cov2=rnorm(250),
2007 Dec 09
2
Getting estimates from survfit.coxph
Dear all,
I'm having difficulty getting access to data generated by survfit and
print.survfit when they are using with a Cox model (survfit.coxph).
I would like to programmatically access the median survival time for
each strata together with the 95% confidence interval. I can get it on
screen, but can't get to it algorithmically. I found myself examining
the source of print.survfit to
2012 Jul 06
2
Anova Type II and Contrasts
the study design of the data I have to analyse is simple. There is 1 control group (CTRL) and 2 different treatment groups (TREAT_1 and TREAT_2).
The data also includes 2 covariates COV1 and COV2. I have been asked to check if there is a linear or quadratic treatment effect in the data.
I created a dummy data set to explain my situation:
df1 <- data.frame(
Observation =
2006 Dec 21
1
: newbie estimating survival curve w/ survfit for coxph
I am wondering how to estimate the survival curve for a particular case(s)
given a coxph model
using this example code:
#fit a cox proportional hazards model and plot the
#predicted survival curve
fit <- coxph(
Surv(futime,fustat)~resid.ds+strata(rx)+ecog.ps+age,data=ovarian[1:23,])
z <- survfit(fit,newdata=ovarian[24:26,],individual=F)
zs <- z$surv
zt <-
2012 Nov 06
3
Survplot, Y-axis in percent
Hi
I am a new fan of R after getting mad with the graphical functional in SPSS.
I have been able to create a nice looking Kaplan Meyer graph using Survplot
function.
However I have difficulties in turning the y axis to percent instead of the
default 0-1 scale.
Further I have tried the function yaxt="n" without any results. Any help in
this matter will be appreciated.
The code is
2011 Oct 01
4
Is the output of survfit.coxph survival or baseline survival?
Dear all,
I am confused with the output of survfit.coxph.
Someone said that the survival given by summary(survfit.coxph) is the
baseline survival S_0, but some said that is the survival S=S_0^exp{beta*x}.
Which one is correct?
By the way, if I use "newdata=" in the survfit, does that mean the survival
is estimated by the value of covariates in the new data frame?
Thank you very much!
2010 Feb 16
1
survival - ratio likelihood for ridge coxph()
It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected?
In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.
2008 Aug 18
1
lmer syntax, matrix of (grouped) covariates?
I have a fairly large model:
> length(Y)
[1] 3051
> dim(covariates)
[1] 3051 211
All of these 211 covariates need to be nested hierarchically within a
grouping "class", of which there are 8. I have an accessory vector, "
cov2class" that specifies the mapping between covariates and the 8 classes.
Now, I understand I can break all this information up into individual
2009 Feb 17
3
Survival-Analysis: How to get numerical values from survfit (and not just a plot)?
Hi!
I came across R just a few days ago since I was looking for a toolbox
for cox-regression.
I?ve read
"Cox Proportional-Hazards Regression for Survival Data
Appendix to An R and S-PLUS Companion to Applied Regression" from John Fox.
As described therein plotting survival-functions works well
(plot(survfit(model))). But I?d like to do some manipulation with the
survival-functions
2010 Nov 12
3
predict.coxph
Since I read the list in digest form (and was out ill yesterday) I'm
late to the discussion.
There are 3 steps for predicting survival, using a Cox model:
1. Fit the data
fit <- coxph(Surv(time, status) ~ age + ph.ecog, data=lung)
The biggest question to answer here is what covariates you wish to base
the prediction on. There is the usual tradeoff between too few (leave
out something
2010 Jan 30
2
Questions on Mahalanobis Distance
Hello,
I am a new R user and trying to learn how to implement the mahalanobis
function to measure the distance between to 2 population centroids. I
have used STATISTICA to calculate these differences, but was hoping to learn
to do the analysis in R. I have implemented the code as below, but my
results are very different from that of STATISTICA, and I believe I may not
have interpreted the help
2006 Jul 11
2
Multiple tests on 2 way-ANOVA
Dear r-helpers,
I have a question about multiple testing.
Here an example that puzzles me:
All matrixes and contrast vectors are presented in treatment contrasts.
1. example:
library(multcomp)
n<-60; sigma<-20
# n = sample size per group
# sigma standard deviation of the residuals
cov1 <- matrix(c(3/4,-1/2,-1/2,-1/2,1,0,-1/2,0,1), nrow = 3, ncol=3, byrow=TRUE,
dimnames =
2012 Jun 05
1
model.frame and predvars
I was looking at how the model.frame method for lm works and comparing
it to my own for coxph.
The big difference is that I try to retain xlevels and predvars
information for a new model frame, and lm does not.
I use a call to model.frame in predict.coxph, which is why I went that
route, but never noted the difference till now (preparing for my course
in Nashville).
Could someone shed light
2013 Oct 18
1
crr question in library(cmprsk)
Hi all
I do not understand why I am getting the following error message. Can
anybody help me with this? Thanks in advance.
install.packages("cmprsk")
library(cmprsk)
result1 <-crr(ftime, fstatus, cov1, failcode=1, cencode=0 )
one.pout1 = predict(result1,cov1,X=cbind(1,one.z1,one.z2))
predict.crr(result1,cov1,X=cbind(1,one.z1,one.z2))
Error: could not find function
2009 Aug 02
1
Competing Risks Regression with qualitative predictor with more than 2 categories
Hello,
I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor !
Thank you for your help
Jan
> # simulated data to test
> set.seed(10)