similar to: extractAIC

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

2017 Jun 08
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
Help documentation in extractAIC
Hello. The sentence in extractAIC's help <> 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
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
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
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
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
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
$theta of frailty in coxph
Dear all, Does the frailty.object$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
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
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
[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
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
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
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
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
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
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 [[alternative HTML version deleted]]
2006 Aug 02
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
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),, weight=w, dist='gaussian', scale=1) Call: survreg(formula = Surv(y, y >= 0, type
2008 Apr 18
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