similar to: bug in extractAIC.survreg (PR#6910)

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 <- function(fit, scale, k = 2,

I would like to figure out the equations for calculating "AIC" in both "step() function" and "AIC () function". They are different. Then I just type "step" in the R console, and found the "AIC" used in "step() function" is "extractAIC". I went to the R help, and found: "The criterion used is AIC = - 2*log L + k *

Although the 'spatial' documentation doesn't mention that extractAIC works, it does seem to give an output. I may have misunderstood, but shouldn't the following give at least the same d.f.? > library(spatial) > data(topo, package="MASS") > extractAIC(surf.ls(2, topo)) [1] 46.0000 437.5059 > extractAIC(lm(z ~ x+I(x^2)+y+I(y^2)+x:y, topo)) [1]

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

Dear R users, Would it be difficult to change the code of stepAIC (from the MASS library) to use AICc instead of AIC? It would be great to know of someone has tried this already. Best wishes Christoph.

By changing three lines in drop1 from access based on $ to access based on standard accessor methods (terms() and residuals()), it becomes *much* easier to extend drop1 to work with other model types. The use of $ rather than accessors in this context seems to be an oversight rather than a design decision, but maybe someone knows better ... In particular, if one makes these changes (which I am

Hola, ¿qué tal? Tengo una pregunta de esta

Hi I'm trying to predict the values for a survreg object called loglogistic_na. Here is the definition of loglogistic_na and following that the syntax used for the predict function. But upon execution I don't get any output. Not sure what I'm doing wrong: loglogistic_na <- survreg(Surv(time_na,event_na) ~ t_na, dist="loglogistic") summary(loglogistic_na)

I am trying to perform a backwards stepwise variable selection with an mlogit model. The usual functions, step(), drop1(), and dropterm() do not work for mlogit models. Update() works but I am only able to use it manually, i.e. I have to type in each variable I wish to remove by hand on a separate line. My goal is to write some code that will systematically remove a certain set of variables

Hello, I am using a simple linear model and I would like to get an AIC value. I came across both AIC() and extractAIC() and I am not sure which is best to use. I assumed that I should use AIC for a glm and extractAIC() for lm, but if I run my model in glm the AIC value is the same if I use AIC() on an lm object. What might be going on? Did I interpret these functions incorrectly? Thanks,

I recently got a request to add head() and tail() methods for Surv objects, which is quite reasonable, but not unlike other requests for logLik,? vcov, extractAIC, ...?? What they all have in common is that are methods added since creation of the survival package, and that I didn't know they existed. To try and get ahead of the curve, is there a way to list names of all of the default

I want to do a linear regression where the coefficients obey two linear constraints, and also are all non-negative. What is the best way to do this? Computational speed is a consideration as I must do it many times. When this question was asked previously on the list, quadprog was suggested - is this the best solution? (I may have missed something obvious in the documentation, but I have

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,

Hey all, I am trying to use the survreg function in R to estimate the mean and standard deviation to come up with the MLE of alpha and lambda for the weibull distribution. I am doing the following: times<-c(10,13,18,19,23,30,36,38,54,56,59,75,93,97,104,107,107,107) censor<-c(1,0,0,1,0,1,1,0,0,0,1,1,1,1,0,1,0,0) survreg(Surv(times,censor),dist='weibull') and I get the following

Hi all, Sorry if this has been answered already, but I couldn't find it in the archives or general internet. Is it possible to implement the gompertz distribution as survreg.distribution to use with survreg of the survival library? I haven't found anything and recent attempts from my side weren't succefull so far. I know that other packages like 'eha' and

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 in the edf and extractAIC does

Hi, It is not critical but in case of coxph.null model (~1) extractAIC function generates Warning message: In is.na(fit$coefficients) : is.na() applied to non-(list or vector) of type 'NULL' As I understand it happens because of absent coefficients attribute. Function stats:::extractAIC.coxph Line edf <- sum(!is.na(fit$coefficients)) I think extra null-checking

Dear R-user: I am using survreg(Surv()) for fitting a Tobit model of left-censored longitudinal data. For logarithmic transformation of y data, I am trying use survreg.distributions in the following way: tfit=survreg(Surv(y, y>=-5, type="left")~x + cluster(id), dist="gaussian", data=y.data, scale=0, weights=w) my.gaussian<-survreg.distributions$gaussian

Hi,Just a quick one, does anyone know the command for accessing the standard errors from a survreg object? I can access the coefficients by model$coefficients, but I cant seem to find a command to access the errors. Any help would be greatly appreciated.Regards,Andre [[alternative HTML version deleted]]

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.