Displaying 20 results from an estimated 1000 matches similar to: "extractAIC"

2017 Jun 08

1

stepAIC() that can use new extractAIC() function implementing AICc

I would like test AICc as a criteria for model selection for a glm using
stepAIC() from MASS package.
Based on various information available in WEB, stepAIC() use
extractAIC() to get the criteria used for model selection.
I have created a new extractAIC() function (and extractAIC.glm() and
extractAIC.lm() ones) that use a new parameter criteria that can be AIC,
BIC or AICc.
It works as

2011 May 10

0

Help documentation in extractAIC

Hello.
The sentence in extractAIC's help <http://www.stat.psu.edu/~dhunter/R/html/stats/html/extractAIC.html> which discusses AIC's estimate of -2logL from RSS reads: "AIC only handles unknown scale and uses the formula n log (RSS/n) - n + n log 2pi - sum(log w) where w are the weights. Further AIC counts the scale estimation as a parameter

2005 Jan 26

2

Source code for "extractAIC"?

Dear R users:
I am looking for the source code for the R function extractAIC. Type the
function name doesn't help:
> extractAIC
function (fit, scale, k = 2, ...)
UseMethod("extractAIC")
<environment: namespace:stats>
And when I search it in the R source code, the best I can find is in (R
source root)/library/stats/R/add.R:
extractAIC <-

2009 Jan 07

0

Frailty by strata interactions in coxph (or coxme)?

Hello,
I was hoping that someone could answer a few questions for me (the background is given below):
1) Can the coxph accept an interaction between a covariate and a frailty term
2) If so, is it possible to
a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties)
b)augment model 1) by stratifying on the variable

2011 Apr 05

0

frailty

Hi R-users
I spend a lot of time searching on the web but I didn?t found a clear
answer.
I have some doubts with 'frailty' function of 'survival' package.
The following model with the function R ?coxph? was fitted:
modx <- coxph(Surv(to_stroke, stroke) ~ age + sbp + dbp + sex +
frailty(center,distribution = "gamma", method='aic'), data=datax)
Then I get

2011 Jun 25

2

cluster() or frailty() in coxph

Dear List,
Can anyone please explain the difference between cluster() and
frailty() in a coxph? I am a bit puzzled about it. Would appreciate
any useful reference or direction.
cheers,
Ehsan
> marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats)
> frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats)
> marginal.model
Call:
coxph(formula =

2012 Feb 03

1

coxme with frailty--variance of random effect?

Dear all,
This probably stems from my lack of understanding of the model, but I
do not understand the variance of the random effect reported in coxme.
Consider the following toy example:
#------------------------------- BEGINNING OF CODE
------------------------------------------------
library(survival)
library(coxme)
#--- Generate toy data:
d <- data.frame(id = c(1:100), #

2006 Sep 22

0

$theta of frailty in coxph

Dear all,
Does the frailty.object$history[[1]]$theta returns the Variance of random
effect?
Why is the value different? Here is an example with kidney data:
> library(survival)
> data(kidney)
> frailty.object<-coxph(Surv(time, status)~ age + sex + disease +
frailty(id), kidney)
> frailty.object
Call:
coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id),
data

2005 Jan 06

0

Parametric Survival Models with Left Truncation, survreg

Hi,
I would like to fit parametric survival models to time-to-event data
that are left truncated. I have checked the help page for survreg and
looked in the R-help archive, and it appears that the R function survreg
from the survival library (version 2.16) should allow me to take account
of left truncation. However, when I try the command

2009 Jun 24

1

Coxph frailty model counting process error X matrix deemed singular

Hello,
I am currently trying to simulate data and analyze it using the frailty option in the coxph function. I am working with recurrent event data, using counting process notation. Occasionally, (about 1 in every 100 simulations) I get the following warning:
Error in coxph(Surv(start, end, censorind) ~ binary + uniform + frailty(subject, :
X matrix deemed to be singular; variable 2
My

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,

2007 Mar 14

0

Wald test and frailty models in coxph

Dear R members,
I am new in using frailty models in survival analyses and am getting
some contrasting results when I compare the Wald and likelihood ratio
tests provided by the r output.
I am testing the survivorship of different sunflower interspecific
crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen
as fixed effects, and sub-block as a random effect. I stratified

2018 Mar 28

0

coxme in R underestimates variance of random effect, when random effect is on observation level

Hello,
I have a question concerning fitting a cox model with a random intercept, also known as a frailty model. I am using both the coxme package, and the frailty statement in coxph. Often 'shared' frailty models are implemented in practice, to group people who are from a cluster to account for homogeneity in outcomes for people from the same cluster. I am more interested in the classic

2010 Apr 26

1

Interpreting output of coxph with frailty.gamma

Dear all,
this is probably a very silly question, but could anyone tell me what the
different parameters in a coxph model with a frailty.gamma term mean?
Specifically I have two questions:
(1) Compared to a "normal" coxph model, it seems that I obtain two standard
errors [se(coef) and se2].
What is the difference between those?
(2) Again compared to a "normal" coxph model,

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 =

2006 Nov 07

1

Extracting parameters for Gamma Distribution

I'm doing a cox regression with frailty:
model <- coxph(Surv(Start,Stop,Terminated)~ X + frailty(id),table)
I understand that model$frail returns the group level frailty
terms. Does this mean this is the average of the frailty
values for the respective groups? Also, if I'm fitting it to
a gamma frailty, how do I extract the rate and scale
parameters for the different gamma

2011 Dec 20

2

Extract BIC for coxph

Dear all,
is there a function similar to extractAIC based on which I can extract the
BIC (Bayesian Information Criterion) of a coxph model?
I found some functions that provide BIC in other packages, but none of them
seems to work with coxph.
Thanks,
Michael
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2006 Aug 02

0

expected survival from a frailty cox model using survfit

Hello R users
Would somebody know how to estimate survival from a frailty cox model,
using the function survfit
and the argument newdata ? (or from any other way that could provide
individual expected survival
with standard error); Is the problem related to how the random term is
included in newdata ?
kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease + frailty(id,

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

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