I am trying to implement the Delta Method in R to calculate the MTTF variance of a Weibull survival curve. The shape parameter is alpha and scale parameter is delta. Variance = var; covariance = cov. The equation is: var(mttf) = var(alpha)*[d(mttf)/d
Because the estimation of the parameters of the Weibull function using R2OpenBUGS is so different from the amounts provided to generate the data set using rweibull? What's wrong with my fit? data<-rweibull(200, 2, 10) model<-function(){ v ~ dgamma(0
I'm trying to fit a curve to model responses from a direct mail campaign over time. Using R, I was a able to get a shape and scale factor using the fitdistr() function. Then I use the shape and scale as parameters in the weibull() function. However,
I want to turn a continuous random variable X with cdf F(x) into a continuous random variable Y with cdf F(y) and am wondering how to implement it in R. For example, perform a probability transformation on data following normal distribution (X) to ma
I want to use grid.table() in an existing plot in R. However I can't locate this table on the right side of my chart. So the thing is: First of all, I make an histogram of my data: hist(as.numeric(unlist((vels[counts]))),freq=F, col="gray",border="bl
I’m trying to fit and plot a Weibull model to a survival data. The data has just one covariate, cohort, which runs from 2006 to 2010. So, any ideas on what to add to the two lines of code that follows to plot the survival curve of the cohort of 2010?
I am trying to generate an inverse Weibull distribution using parameters estimated from survreg in R. By this I mean I would like to, for a given probability (which will be a random number in a small simulation model implemented in MS Excel), return
I am just wondering what is the best way(s) to compare the performance of different binary choice models? I run logit probit and weibull binary choice(bernoulli), and some semi-parametric estimation methods(i.e. Klein&Spady's method) on the same
I know that the Weibull distribution exhibits subexponential heavy-tailed behavior when the shape parameter is < 1. I need to demonstrate this using the limit definition of a heavy tailed distribution: for all How do I incorporate the cumulative d