similar to: gnlr shape parameter

Hi list, I'd like to fit generalized gamma and weibull distributions to a number of data sets. I've been searching around and found references to R and Jim Lindsey's GNLM package, which has the gnlr and gnlr3 procedures that can do this. Now, I'm completely new to R, and I'm working my way through the introduction... Nevertheless, I'd like to ask if someone could post

Hi all, I'm trying to generate a Weibull distribution including four covariates in the model. Here is the code I used: T = rweibull(200, shape=1.3, scale=0.004*exp(-(-2.5*b1+2.5*b2+0.9*x1-1.3*x2)/1.3)) C = rweibull(n, shape=1.5, scale=0.008) #censoring time time = pmin(T,C) #observed time is min of censored and true event = time==T # set to 1 if event is observed

Hi Are there any functions that de-convolute data into a given number of clusters, rather like the NPMLE GLIM macros from Murray Aitkin and Brian Francis? Basically I would like to code into R the same approach but include the possiblility of some data being censored. In principle the formulae are the same (just replace the likelihood function) but I haven't managed to get my head round the

Hello, The function garchFit in the package fGarch allows for choosing a conditional distribution, one of which is the t-distribution. The function allows specification of the shape parameter of the distribution (equal to the degrees of freedom for the t-distribution), for which the default is set to 4. The function also includes an option "include.shape", which is "a logical flag

Hello, ?can i implement this as 10% censored data where t gives me failure and x censored. Thank you p=2;b=120 n=50 set.seed(132); r<-sample(1:50,45) t<-rweibull(r,shape=p,scale=b) t set.seed(123);? cens <- sample(1:50, 5)? x<-runif(cens,shape=p,scale=b)? x Chris Guure Researcher, Institute for Mathematical Research UPM

The new code seems to have broken various things. Autoloading of libraries doesn't seem to work: > library(survival4) Autoloading required library: splines Error in pos.to.env(pos) : invalid "pos" argument > search() [1] ".GlobalEnv" "library:survival4" "library:date" [4] "library:base" The coxph function the

The new code seems to have broken various things. Autoloading of libraries doesn't seem to work: > library(survival4) Autoloading required library: splines Error in pos.to.env(pos) : invalid "pos" argument > search() [1] ".GlobalEnv" "library:survival4" "library:date" [4] "library:base" The coxph function the

sampleSize <- 20 shape.true <- 1.82 scale.true <- 987 sampWB <- rweibull(sampleSize, shape=shape.true, scale=scale.true) print(sampWB) censidx <- sample(1:length(sampWB), length(sampWB)*0.3) Censored.data <- sampWB[censidx] noncensidx <- defines the rest values of the vector which is not included at Censored.data? [[alternative HTML version deleted]]

Dear Sir/madam, I'm getting a problem with a R-code which calculate Fisher Information Matrix for Hybrid Censored Weibull Distribution. My problem is that: when I take weibull(scale=1,shape=2) { i.e shape>1} I got my desired result but when I take weibull(scale=1,shape=0.5) { i.e shape<1} it gives error : Error in integrate(int2, lower = 0, upper = t) : the integral is probably

Hi there. I am receiving an unexpected error message when creating a new distribution for the survreg() function in the survival package. I understand the survival.distributions() function and have been following the Cauchy example provided in the help file. My goal is to use survreg to fit a gamma distribution to interval censored data. Here is a simple example of what I'm trying to do.

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

Hi I am trying to use nlm with an additional call to nlm within the function but after the first pass, the parameters to the outer call are being passed to the inner call. The inner call is a very trivial problem. ie: test.outer<-function(param.outer){ slope<-nlm(test.inner,param.inner) ... loglikelihood<-sum(...) return(-loglikelihood) } and nlm(test.outer,param.outer) on the

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

OK this is bound to be something silly as I'm completely new to R - having started using it yesterday. However I am already warming to its lack of 'proper' GUI... I like being able to rerun a command by editing one parameter easily... try and do that in a Excel Chart Wizzard! I eventually want to use it to analyse some chemotherapy response / survival data. That data will not be

I have a question about constructing the likelihood function where there is censoring at level 1 in a two-level random effects sum. In a conventional solution, the likelihood function is constructed using the density for failures and the survivor function for (in this case, right) censored results. Within (for example) an R environment, this is easy to do and gives the same solution as survreg

Your function is giving NaN's during the optimization. The R-forge version of optimx() has functionality specifically intended to deal with this. NOTE: the CRAN version does not, and the R-forge version still has some glitches! However, I easily ran the code you supplied by changing optim to optimx in the penultimate line. Here's the final output. KKT condition testing Number of

Dear R people, This is a possibly silly question, but the rgamma function takes the shape and scale arguments and simulates gamma rvs corresponding to those values, right? But the shape and scale parameters have to be positive, right? However, rgamma quite happily returns to me values for negative values of shape and scale, and in some cases returns negative values eg. > rgamma(1, 1, -1) [1]

Dear all, I am fitting a parametric regression model to survival data using the flexsurvreg function from the flexsurv package. I am using a Gompertz distribution (a 2-parameter distribution) to describe the hazard function and I want to compare two groups. The model is very simple: flexsurvreg(formula = Surv(day, censored) ~ group, dist = "gompertz") and the output is also quite

Hello, I am interested in fitting a generalized nonlinear regression (gnlr) model with negative binomial errors. I have found Jim Lindsay's package that will do gnlr, but I have having trouble with the particular model I am interested in fitting. It is a threshhold model, where below a certain value of one of the parameters being fitted, the model changes. Here is a sample: Cones:

A few days ago I uploaded to CRAN a new package called 'eha', which stands for 'Event History Analysis'. Its main focus is on proportional hazards modeling in survival analysis, and in that respect eha can be regarded as a complement and an extension to the 'survival' package. In fact eha requires survival. Eha contains three functions for proportional hazards