Thanks for this. I suspect you need to read up on -st- methods.
I don't think there's a wired-in Stata function for Weibull random deviates, but you don't need one. In the parameterisation used by -weibullfit-, with scale parameter b and shape parameter c, the recipe is something like
gen weibull = b * (-ln(runiform()))^(1 / c)
Nick
[email protected]
Nikolaos Pandis
Thank you for the posting, and I apologize for not being very clear.
I am a novice and I think simulate is the more appropriate since I would like to create data for testing under different scenarios.
This is a furher question and possibly a better explanation of the problem. I would like to test success/failure using count data and compare the results with those derived by using Weibull which will take in account the time-to-event information and create plot(s) that forecast failure.
So, I think, I need to first create the data and then proceed with analysing either using Binomial or the weibull approach.
Nick Cox <[email protected]> wrote:
I don't understand what you mean by "generate" here. Do you mean
simulate?
Likewise, it is not clear to me whether (a) you have considered -st-
methods or (b) your problem requires -st- methods.
If no to (b), -weibullfit- from SSC is one program that may help.
More generally, -findit weibull- points to official and user-written
support. It's an easy keyword to use.
Nikolaos Pandis
I would like to generate continuous data for two groups for which I
would like to evaluate whether there is a difference between their means
(I could use use a t-test). I would like to be able to set the mean and
sd of the two groups. The data would represent bond failure strength (in
MPa) of two different materials (the 2 groups).
Additionally, I would like to generate data for the same experiment that
follows the Weibull distribution and compare the two groups.
It is of interest to see the results of the analysis on the data under
the ttest analysis and under an analysis suitable for the Weibull
distribution. The objective is to show how different analyses of similar
data might point to different results, and the importance of selecting
the correct distribution and analysis.
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