similar to: survival analysis - predict function

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)

Thanks David. Could you show me how to do that in my example ? Thanks, Meghna > On Nov 6, 2017, at 12:58 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > You should stop trying to use matrices on the RHS and using separate vectors to Surv. Instead use a data argument and have the names in your formula refer to column names. > > ? > David > > Sent

You should stop trying to use matrices on the RHS and using separate vectors to Surv. Instead use a data argument and have the names in your formula refer to column names. ? David Sent from my iPhone > On Nov 5, 2017, at 7:21 PM, Meghna Govil via R-help <r-help at r-project.org> wrote: > > > Hi - Below is my code and then the error when I run the last line. > >

Hi - Below is my code and then the error when I run the last line. time_np <- train1_na$tte event_np <- train1_na$censored X_np <- cbind( train1_na$AMT, train1_na$DISCOUNT_AMT, train1_na$high_price_pcnt, train1_na$EM_RECEIVED, train1_na$DM_RECEIVED, train1_na$TXN_WITH_RINGCODE, train1_na$WEB, train1_na$clearance_pcnt, train1_na$bts_pcnt, train1_na$sales_pcnt,

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

Greetings, Version 0.1.4-0 of the ChainLadder package is available via CRAN and http://code.google.com/p/chainladder/ The ChainLadder package, which is focused on claims reserving methods typically carried out by property/casualty insurance actuaries, has recently been enhanced to implement the methods in David Clark's 2003 CAS (Casualty Actuarial Society) *Forum* paper "LDF

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.

Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a simple plain vanilla one (without time dependent covariates), I'm comparing the results that I obtain between aftreg (eha package) and survreg(surv package). If I don't use any covariate the results are identical , if I add covariates all the coefficients are the same until a precision of 10^4 or 10^-5 except

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

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

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work

Hola a tod@s, Hace poco en la lista se discutieron algunas aproximaciones para determinar la distribución de probabilidad que potencialmente podrían haber generado los datos (ver [1]) y una de ellas fué el AIC. Haciendo uso del programa enviado por Pablo Verde (ver [1]), estos son los resultados para mis datos: weibull , AIC = 69839.44 exponential , AIC = 79488.77 gaussian , AIC = 69413.03

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.

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

Dear All, I am doing a parametric survival analysis with: fit <- survreg(Surv(xyz$start, xyz$stop, xyz$event, type="interval") ~ 1, dist='loglogistic') At this point I do not want to look into covariates, hence the '~1' as model formulation. As event types I have exact, interval, and right censored lifetime data. Everything works fine. For reasons that are

R users, I am making model selection with an accelerated failure time model using the command survreg within the library survival. As I want to compare models with different probability distributions I need to have the full likelihood. How can I find out what survreg generates: the full likelihood or a likelihood with "unnecessary" constants dropped? Example I want to

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

Dear all, I am looking for routines which allow multi-variate sampling from non-normal distributions (loglogistic) given correlations among the variables. Unfortunately, I could not find a suitable package for R. Does anybody know one? Many thanks and best regards, Stefan Albrecht [[alternative HTML version deleted]]

Dear R-help, I am using R 2.14.1 on Windows 7 with the 'gfcure' package (cure rate model). I have included the treatment variable in the cure part of the model as shown below: Ø ref_treat <- gfcure(Surv(rem.Remtime,rem.Rcens)~1,~1+strata(drpa)+factor(treat(delcure)),data=delcure,dist="loglogistic") >From that I can obtain the coefficients, standard errors etc as per

Hey everyone, I've been reading an old scientific paper (well, not that old, about 15 years) and I want to verify the authors' statistical results. The paper is fairly unclear about what exactly they did, and none of the relatively simple commands I'm familiar with are producing results similar to theirs. The data is dose-response, recorded as binomial data: structure(list(X1 =