Displaying 20 results from an estimated 2000 matches similar to: "Parametric Survival Models with Left Truncation, survreg"
2008 Apr 17
1
survreg() with frailty
Dear R-users,
I have noticed small discrepencies in the reported estimate of the
variance of the frailty by the print method for survreg() and the
'theta' component included in the object fit:
# Examples in R-2.6.2 for Windows
library(survival) # version 2.34-1 (2008-03-31)
# discrepancy
fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats)
fit1
fit1$history[[1]]$theta
2008 Apr 18
0
survreg with frailty
The combination of survreg + gamma frailty = invalid model, i.e., the example
that you quote.
I did not realize that this had been added to the survreg help file until very
recently. I will try to fix the oversight. Other, more detailed documentation
states that Gaussian frailty + AIC is the only valid random effects choice for
survreg.
Details: frailty(x) with no optional
2007 Jan 22
0
[UNCLASSIFIED] predict.survreg() with frailty term and newdata
Dear All,
I am attempting to make predictions based on a survreg() model with some censoring and a frailty term, as below: predict works fine on the original data, but not if I specify newdata.
# a model with groups as fixed effect
model1 <- survreg(Surv(y,cens)~ x1 + x2 + groups,
dist = "gaussian")
# and with groups as a random effect
fr <- frailty(groups,
2011 Apr 08
1
Variance of random effects: survreg()
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 =
2009 Jun 08
0
Using survreg for Tobit
I am using survreg from the survival package to run a left censored tobit
model on “non-survival” data. I have to four questions that I hope someone
can help me with:
1) Is there anything I should take into consideration when using frailty()
to estimate random intercepts?
2) Is there anyway of extracting the estimated intercepts produced by
survreg when using frailty()?
3) Can someone point
2011 Jan 28
1
survreg 3-way interaction
> I was wondering why survreg (in survival package) can not handle
> three-way interactions. I have an AFT .....
You have given us no data to diagnose your problem. What do you mean
by "cannot handle" -- does the package print a message "no 3 way
interactions", gives wrong answers, your laptop catches on fire when you
run it, ....?
Also, make sure you read
2008 Jan 23
2
Parametric survival models with left truncated, right censored data
Dear All,
I would like to fit some parametric survival models using left
truncated, right censored data in R. However I am having problems
finding a function to fit parametric survival models which can handle
left truncated data.
I have tested both the survreg function in package survival:
fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1)
and the psm function in package
2011 Sep 20
0
Using method = "aic" with pspline & survreg (survival library)
Hi everybody. I'm trying to fit a weibull survival model with a spline
basis for the predictor, using the survival library. I've noticed that it
doesn't seem to be possible to use the aic method to choose the degrees of
freedom for the spline basis in a parametric regression (although it's
fine with the cox model, or if the degrees of freedom are specified directly
by the user),
2003 May 07
0
frailty models in survreg() -- survival package (PR#2933)
I am confused on how the log-likelihood is calculated in a parametric
survival problem with frailty. I see a contradiction in the frailty() help
file vs. the source code of frailty.gamma(), frailty.gaussian() and
frailty.t().
The function frailty.gaussian() appears to calculate the penalty as the
negative log-density of independent Gaussian variables, as one would
expect:
>
2003 May 07
0
Re: frailty models in survreg() -- survival package (PR#2934)
On Tue, 6 May 2003, Jerome Asselin wrote:
>
> I am confused on how the log-likelihood is calculated in a parametric
> survival problem with frailty. I see a contradiction in the frailty() help
> file vs. the source code of frailty.gamma(), frailty.gaussian() and
> frailty.t().
>
> The function frailty.gaussian() appears to calculate the penalty as the
> negative
2006 Feb 28
1
ex-Gaussian survival distribution
Dear R-Helpers,
I am hoping to perform survival analyses using the "ex-Gaussian"
distribution.
I understand that the ex-Gaussian is a convolution of exponential and
Gaussian
distributions for survival data.
I checked the "survreg.distributions" help and saw that it is possible to
mix
pre-defined distributions. Am I correct to think that the following code
makes
the
2003 May 07
0
Re: frailty models in survreg() -- survival package (PR#2934)
SEE ALSO ORIGINAL POSTING IN PR#2933
On May 6, 2003 03:58 pm, Thomas Lumley wrote:
>
> Looking at a wider context in the code
>
> pfun <- function(coef, theta, ndeath) {
> if (theta == 0)
> list(recenter = 0, penalty = 0, flag = TRUE)
> else {
> recenter <- log(mean(exp(coef)))
> coef <- coef - recenter
2011 Jan 10
4
Meaning of pterms in survreg object?
I am trying to model survival data with a Weibull distribution
using survreg. Units are clustered two apiece, sometimes receiving
the same treatment and sometimes opposing treatment.
2010 Dec 10
1
survreg vs. aftreg (eha) - the relationship between fitted coefficients?
Dear R-users,
I need to use the aftreg function in package 'eha' to estimate failure times for left truncated survival data. Apparently, survreg still cannot fit such models. Both functions should be fitting the accelerated failure time (Weibull) model. However, as G?ran Brostr?m points out in the help file for aftreg, the parameterisation is different giving rise to different
2007 May 11
0
Tobit model and an error message
Dear R users:
I am using survreg for modeling left censored longitudinal data. When I am using the following code for fitting the tobit model I am getting some output with an warning message(highlighted with red color):
> survreg(Surv(y, y>=0, type='left')~x + frailty(id), cytokine.data, weight=w, dist='gaussian', scale=1)
Call:
survreg(formula = Surv(y, y >= 0, type
2006 Apr 26
0
left-truncation in survreg
Dear R-users,
I know that a few people have asked whether survreg handles left-truncation
data and the reply that i have seen so far is that it does.
However, when I try to use survreg on left-truncated data, I got the
following error message.
> survcs3<-survreg(Surv(start,end,status)~AG, data=DPONEcs3,
dist="exponential")
Error in survreg(Surv(start, end, status) ~ AG, data =
2005 May 03
2
comparing lm(), survreg( ... , dist="gaussian") and survreg( ... , dist="lognormal")
Dear R-Helpers:
I have tried everything I can think of and hope not to appear too foolish
when my error is pointed out to me.
I have some real data (18 points) that look linear on a log-log plot so I
used them for a comparison of lm() and survreg. There are no suspensions.
survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000,
979000, 17420000, 71065000, 46397000,
2006 Mar 24
0
Random covariate in survreg (Survival)
Dear R Listers-
I am attempting to analyse the survival of seeds in cages
(exclosures) that differ in their permeability to rainforest mammals.
Because I did not observe the moment of seed disappearance, my data
is interval censored. This limits my options for analysis (as I
understand it) to survreg, in the survival package. Because I
repeated the experiment in 8 sites, I have a random
2012 Jan 26
1
3-parametric Weibull regression
Hello,
I'm quite new to R and want to make a Weibull-regression with the survival package. I know how to build my "Surv"-object and how to make a standard-weibull regression with "survreg".
However, I want to fit a translated or 3-parametric weibull dist to account for a failure-free time.
I think I would need a new object in survreg.distributions, but I don't know how
2007 Nov 29
1
Survreg(), Surv() and interval-censored data
Can anybody give me a neat example of interval censored data analysis codes in R?
Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1)
works why does
survreg(Surv(data[,1],data[,2],type="interval2")~1)
not work where
data is :
T.1 T.2 Status
1 0.0000000 0.62873036 1
2 0.0000000 2.07039068 1
3 0.0000000