similar to: surv2sample 0.1-2

Dear R users, The package proptest (http://www.davidkraus.net/proptest/) has been recently released to CRAN. The package provides functions for testing the proportional hazards assumption in the Cox model for right censored survival data. Two types of tests for identifying nonproportional covariates are implemented: * data-driven Neyman type smooth tests (using both nested and all-subsets

Hi, I have the binned data (observed and generated from model)  that I would like to test using the anderson-darling goodness of fit test.  But I'm not sure which package in R to use. I tried ad.test(...) but it does not recognise the test by Vito Ricci in FITTING DISTRIBUTIONS WITH R   > ad.test(hist_hume_beec[,1],hist_hume_beec[,2]) Error: could not find function "ad.test"

I am having trouble formatting some survival data to use in a time dependent cox model. My time dep. variable is habitat and I have it recorded for every day (with some NAs). I think it is working properly except for calculating the death.time. This column should be 1s or 0s and as I have it only produces 0s. Any help will be greatly appreciated.

Hello, I was wondering if anyone could please help me with some simple questions regarding ANCOVA and the assumption of homogeneity of slopes. The standard design of ANCOVA assumes the homogeneity of regression coefficients of the different groups. This assumption can be tested using the factor ?? covariate interaction, which should subsequently be removed. However if this assumption is not

Hola a todos, -Estoy estudiando el efecto de dos genotipos (~tratamientos) en la aparición de síndrome metabólico (MetS) con datos longitudinales recogidos a tiempo 0,7,10,15,20 y 25 años. -He hecho un dataframe con las siguientes variables MetS: Síndrome Metabólico (Si=1,No=0) bmi: Indice de masa corporal (IMC) cuando se produce la conversión a MetS+ . Para los que permancen MetS-, esta variable

Dear R users, My question is more methodology related rather than specific to R usage. Using time on study as time in a cox model, eg: library(Design) stanf.cph1=cph(Surv(time, status) ~ t5+id+age, data=stanford2, surv=T) #In this case the 1000-day survival probability would be: stanf.surv1=survest(stanf.cph1, times=1000) #Age in this case is a covariate. #I now want to compare the above

Dear all, I was wondering if anyone can help? I am an R user but recently I have resorted to SAS to calculate the probability of the event (and the associated confidence interval) for the Cox model with combinations of risk factors. For example, suppose I have a Cox model with two binary variables, one for gender and one for treatment, I wish to calculate the probability of survival for the

You asked about survival curves with age scale versus follow-up scale. > fit1 <- coxph(Surv(time/365.25, status) ~ t5 + id + age, data=stanford2) > surv1<- survfit(fit1) > surv1 n events median 0.95LCL 0.95UCL 157.000 102.000 1.999 0.898 3.608 > summary(surv1, times=3) time n.risk n.event survival std.err lower 95% CI upper 95% CI 3 46 85 0.451

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Thomas Petzoldt > Sent: 06 September 2005 06:34 > Cc: petzoldt at rcs.urz.tu-dresden.de; R-Help > Subject: Re: [R] model selection vs. H0 based testing > > > Hello, > > I wish to thank Douglas Bates very much for clarification and

Hi there, I want to generate a random point pattern using the Neyman-Scott cluster process using spatstat package in R. After running the following procedures, why i can not see any figures? > nclust <- function(x0, y0, radius, n) {return(runifdisc(n, radius, x0, y0))} > nclust function(x0, y0, radius, n) {return(runifdisc(n, radius, x0, y0))} > X <- rNeymanScott(10, 0.2, nclust,

Hello, I now receive an error message when obtaining a survival plot, which was not previously received with the same code. I recently updated all my packages. It does not seem to be a peculiarity of my data as I receive the error using data available in R. A plot is produced but I am uncertain regarding the error message. > library(surv2sample) > data(gastric) > fit <-

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Does anyone have any R code or suggestions on how to generate a population that follows a Neyman-Scott process? More specifically, I'd like to randomly generate both the parents and the children from Poisson distributions and the children locations from a bivariate normal onto an N by N grid. If the locations spill over outside the grid, then I'd like to reflect those locations back into

Hello, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables.

I haven't done any R memory debugging lately, but https://www.mail-archive.com/rcpp-devel at lists.r-forge.r-project.org/msg10289.html shows how I used to have gdb break where valgrind finds a problem so you could examine the details. Also, running your code after running gctorture(TRUE) can help track down memory problems. -Bill On Wed, Feb 7, 2024 at 12:03?PM Therneau, Terry M., Ph.D.

?I've hit a roadblock debugging a new update to the survival package.?? I do debugging in a developement envinment, i.e. I don't create and load a package but rather? source all the .R files and dyn.load an .so file, which makes things a bit easier. ? Running with R -d "valgrind --tool=memcheck --leak-check=full" one of my test files crashes in simple R code a dozen lines

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =

Dear list,I am quite new to the world of biostatistics and I encounter some issues in the precise understanding of the coxph function of the survival package.I have a set of survival data (patient who had (or died from) a breast cancer) I'd like to see which are the variables that might cause dead or not.When trying> summary(coxph(Surv(Time_to_distant_recurrence_yrs, !Distant_recurrence)~

It seems as though 'symbol.For' does not work correctly. > library("foo") > add(3,4) [1] 7 > sym.add(3,5) Error in .Fortran(symbol.For("add"), as.numeric(a), as.numeric(b), c = as.numeric(0)) : "Fortran" function name not in load table The function 'add' is a R wrapper to a simple fortran 77 subroutine that adds two numbers. In

Hello! I want to compare two Kaplan-Meier-Curves by using the Logrank-Test: logrank(Surv(time[b], status[b]) ~ group[b]) This way I only get the value of the test-statistic, but not the p-value. Does anybody know how I can get the p-value? Thanks in advance! Verena Hoffmann