thanks a lot
sorry for the mistake that i do in exponential, i am "francophone"
and for the programme if we want to apply the "power rule " condition we use
log(vi).
it works thank yo
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Hello,
Vous êtes française?
It shows, in english it would be 'exponential', with an 'a'.
Worked with me, after reading the manual.
dataexp <- read.table(text="
vi Ti
1 265.79
2 26 1579.52
3 26 2323.70
4 28 68.85
[...]
73 380.39
74 381.13
75 380.09
76 382.38
", he
On Aug 4, 2012, at 10:45 AM, hafida wrote:
Dear R-community,
I have tried to estimate an EXPONENTIEL accelerated failure time(AFT)
power rule model with time-independent . For that purpose, I have
used
the eha package.
Please, consider this example:
vi Ti
1 265.79
2 26 1579.
Dear R-community,
>
> I have tried to estimate an EXPONENTIEL accelerated failure time(AFT)
> power rule model with time-independent . For that purpose, I have used
> the eha package.
> Please, consider this example:
vi Ti
1 265.79
2 26 1579.52
3 26 2323.70
4 28 68.85
5 28
thanks!
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Sent from the R help mailing list archive at Nabble.com.
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R-help@r-project.org mailing list
https://stat
On Sat, Aug 20, 2011 at 7:33 PM, JPF wrote:
>
> Göran Broström wrote:
>>
>>
>> Good. Do you still need answers to your other questions?
>>
>>
>
> Yes. Could answer the following two questions:
>
> 1- Can I use phreg function to estimate a model with time-dependent
> covariates? In case of a posit
question 2
*aftreg vs. survreg*
for aftreg = S0 *{t/exp(b-BXi)]^a} a= shape and b= log(scale)
for survreg and stata S0 *{t*exp(intercept+BXi)]^1/p} p=shape
/intercept, log(scale) and estimates are equivalent with reversed sign./
*PH and AFT*
/phreg.Bhat= aftreg.Bhat * shape /
Göran Broström wrote:
>
>
> Good. Do you still need answers to your other questions?
>
>
Yes. Could answer the following two questions:
1- Can I use phreg function to estimate a model with time-dependent
covariates? In case of a positive answer, how?
2- I could not find any example that
On Sat, Aug 20, 2011 at 4:19 AM, JPF wrote:
>
> JPF wrote:
>>
>>
>>
>> weibullaft<-aftreg(Surv(sta,time,S) ~ TDC1 + TIC1, dist="weibull",
>> data.frame=Data)
>>
>> ## aftreg gives an error when I add an ID argument... That should be used
>> for controlling for time-varying variables.
>>
>> Error i
JPF wrote:
>
>
>
> weibullaft<-aftreg(Surv(sta,time,S) ~ TDC1 + TIC1, dist="weibull",
> data.frame=Data)
>
> ## aftreg gives an error when I add an ID argument... That should be used
> for controlling for time-varying variables.
>
> Error in aftreg.fit(X, Y, dist, strats, offset, init, shape
Dear Prof. Broström,
I have searched in the reference manual inside the package eha, updated
recently. I did not find any description on how to enter id in the aftreg
function except the description of the argument. Can you refer to a specific
part of the manual? Do you mean another documentatio
On Fri, Aug 19, 2011 at 2:55 PM, javier palacios wrote:
> Dear R-community,
>
> I have tried to estimate an accelerated failure time(AFT) and proportional
> hazard (PH) parametric survival model with time-independent and
> time-dependent covariates. For that purpose, I have used the eha package.
Dear R-community,
I have tried to estimate an accelerated failure time(AFT) and proportional
hazard (PH) parametric survival model with time-independent and
time-dependent covariates. For that purpose, I have used the eha package.
Please, consider this example:
weibullph <- phreg(Surv(sta,ti
Thanks for your super-fast reply.
I realized you're totally right: My problem is not left truncation
but missing data of time-varying covariates.
In my special case, two conditions are given by study design:
(1) A lot of subjects are missing all of their (time-varying)
covariates for a certai
On Thu, Jan 28, 2010 at 2:32 PM, Philipp Rappold
wrote:
> Dear Prof. Broström,
> Dear R-mailinglist,
>
> first of all thanks a lot for your great effort to incorporate time-varying
> covariates into aftreg. It works like a charm so far and I'll update you
> with detailled benchmarks as soon as I h
Dear Prof. Broström,
Dear R-mailinglist,
first of all thanks a lot for your great effort to incorporate
time-varying covariates into aftreg. It works like a charm so far
and I'll update you with detailled benchmarks as soon as I have them.
I have one more questions regarding Accelerated Failu
n...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Göran Broström
Sent: Tuesday, October 20, 2009 10:07 AM
To: spencerg
Cc: r-help@r-project.org; Terry Therneau
Subject: Re: [R] AFT-model with time-dependent covariates
Sorry for being late in responding to this thread, but I
Sorry for being late in responding to this thread, but I was made
aware of it only two weeks ago. In my package 'eha' there is a
function 'aftreg', which performs what is asked for, given that the
time-varying covariates are step functions of time and that the
observation period for each individual
To see what's available in other packages, try the following:
library(RSiteSearch)
AFT <- RSiteSearch.function('AFT model')
summary(AFT) # 24 help files found in 8 different packages
HTML(AFT) # opens a table with 24 rows in a web browser.
There may be nothing here that will help you,
The coding for an AFT model with time-dependent covariates will be very hard,
and I don't know of anyone who has done it. (But I don't keep watch of other
survival packages, so something might be there).
In a Cox model, a subject's risk depends only on the current value of his/her
covariat
Dear R-community,
Dear Prof. Therneau,
I would like to fit an AFT-model with time-dependent covariates and
right-censored data.
Searching the mailing list for information on the subject, I found some old
posts which said it didn't work back then.
My questions:
(1) Has this kind of fitting al
Thank you, Dr. Lumley. I have implemented the following code for
the pareto distribution (see below). However, the estimates obtained
from survreg are very small & inaccurate. What I need help with is the
function for the deviance (the code below is wrong). I just don't
understand how to ob
On Tue, 17 Mar 2009, tsn4867 wrote:
Hi,
In the package survival, using the function survreg for AFT model, I only see
4 distributions for the response y: weibull, gaussian, logistic, lognormal and
log-logistic, which correspond to certain distributions for the error terms.
I'm wondering i
Hi,
In the package survival, using the function survreg for AFT model, I
only see 4 distributions for the response y: weibull, gaussian,
logistic, lognormal and log-logistic, which correspond to certain
distributions for the error terms. I'm wondering if there is a package
or how to obta
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