similar to: aftreg vs survreg loglogistic aft model (different intercept term)

Dear all, does anyone know how I can extract specific p-values for covariates from an aftreg object? After fitting a model with aftreg I can find all different variables by using str(), but there's no place where p-values are kept. The odd thing is that print() displays them correctly. EXAMPLE: > testdata start stop censor groupvar var1 var2 1 0 1 0

Hi list! I'm getting an error message when trying to fit an accelerated failure time parametric model using the aftreg() function from package eha: > Error in optim(beta, Fmin, method = "BFGS", control = list(trace = > as.integer(printlevel)), : > non-finite finite-difference value [2] This only happens when adding four specific covariates at the same time in the

Hi Dear, Can someone please inform me on genreating random variables of Loglogistic, and PERT beta distributions? Thanks for your time and help, in advance. Regards, Murthy.

Dear all, What is the cumulative distribution (with parameterization) used within survreg with respect to the log-logistic distribution? That is, how are the parameters linked to the survivor function? Best regards, Mario [[alternative HTML version deleted]]

Hi, is there any similar function in R to S function qq.loglogistic, which produces a Q-Q plot? Thanks a lot Pete

I a trying to use survreg to fit a Weibull distribution. From the last few messages I understand how to interpret the parameters. However, how do I get the covariance of lambda and alpha? Is there a predict command for that? Thanks -- View this message in context: http://n4.nabble.com/Survreg-function-for-loglogistic-hazard-estimation-tp893503p1604918.html Sent from the R help mailing list

I am trying to use R to do loglogistic hazard estimation. My plan is to generate a loglogistic hazard sample data and then use survreg to estimate it. If everything is correct, survreg should return the parameters I have used to generate the sample data. I have written the following code to do a time invariant hazard estimation. The output of summary(modloglog) shows the factor loading of

Dear all, I have some trouble using the "id"-argument with aftreg (accelerated failure time regression analysis from the eha library). As far as I understand it, the id argument is used to group individuals together if there are time-varying covariates and the data is arranged in counting process style. Unfortunately, i cannot figure out how to use the "id"-argument. The

Hello, I am apparently confused about the use of an id parameter for an event history/survival model, and why the EHA documentation for aftreg does not specify one. All assistance and insights are appreciated. Attempting to specifiy an id variable with the documentation example generates an "overlapping intervals" error, so I sorted the original mort dataframe and set subsequent entry

Dear R-users, I have two samples with individuals that are in more than one of the samples and individuals that are only in one sample. I have been trying to do a pooled hazard model, stacking one sample below the other, with aftreg and time-dependent covariates. The idea behind is to see aggregate effects of covariates, but need to control for ther effects of same individuals in both samples

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

This kind of error seems to surprise R users. It surprises me that it doesn't happen much more frequently. The "BFGS" method of optim() from the 1990 Pascal version of my book was called the Variable Metric method as per Fletcher's 1970 paper it was drawn from. It really works much better with analytic gradients, and the Rvmmin package which is an all-R version that adds bounds

I am unsure what is being returned, and what is supposed to be returned, when using 'predict' with "type='expected'" for an aftreg survival model. The code below first generates a weibull model, then uses predict to create a vector of the linear predictors, then attempts to create the 'expected' vector, which is empty. The final two steps in the code generate a

G?ran, thanks for the update, I'm just about to install it! Just wanted to drop you a short line about performance (as you once requested): aftreg takes ages on my windows machine to calculate a small set of 7 observations which are not even grouped together by "id". To be a bit more precise, it takes 2:40 mins on my Intel T9300 Core2 Duo @ 2.5 GHz. Bigger samples with about 700

On Wed, Aug 1, 2012 at 3:08 PM, <fra.meucci@hotmail.it> wrote: > Dear Göran Broström, > I am trying to use AFTREG function for R to estimate a loglogistic > survival function, including time dependent covariates. > Actually, my Subset includes some partial events; the idea is to model > this kind of events using something similar to “weights” in the SURVREG > function.

Dear useRs, I am currently fitting an advanced failure time model using G?ran Brostr?m's excellent "eha" library with the "aftreg" command. My question: How do I interpret the "Overall p-value", that is reported at the very bottom of the output? I already figured out it must be a chi-square test, but I am wondering what a p-value < 0.01 means: Does it mean

Dear Prof. Brostr?m, 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 Failure Time models (with aftreg): You mention that left truncation in combination with time-varying

Hi, I split a data set into two partitions (80 and 42), use the first as the training set in glm and the second as testing set in glm predict. But when I call glm.predict, I get the warning message:  Warning message: 'newdata' had 42 rows but variable(s) found have 80 rows  ---------------------  s = sample(1:122)

Dear R-community, I have a short question: How do I interpret the result of a likelihood ratio test correctly? I am fitting a parametric survival model (with aftreg {eha}) and the output tells me the overall p-value of my model is < 0.001. My simple question is: Does the result mean my model fits the data well OR does it mean my model DOES NOT fit the data well? Some side information how the

Dear R-users I would like to fit weibull parameters using "Method of moments" in order to provide the inital values of the parameter to de function 'fitdistr' . I don`t have much experience with maths and I don't know how to do it. Can anyone please put me in the rigth direction? Borja [[alternative HTML version deleted]]