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I would like to estimate Maximum Likelihood parameters of the Weibull distribution by applying to the following data with a given censoring vector in R:

data= 9 2 11 49 7 5 3 36 30 6 62 5 3 29 29 1 13 1 24 11 9 4 7 15 11 15 1 1 1 1 1 2 6 12 12 28 14 14 57 17 4 2 3 6 21 6 16 19 28 18 19 9 59 12 3 27 8 26 19 47 68 17 15 25 25 6 54 1 2 11 4 1 36 2 5 5 3 38 3 1 10 69 1 8 3 17 21 19 11 1 6 1 1 18 2 51 6 12 11 13 3 19 16 18 28 10 26 32 6 25 1 44

cens= 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

I would be very thankful if anyone could help me.

user5254599
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  • Please take a look at how to make a good reproducible example [here](https://stackoverflow.com/a/5963610/7306168) – Bea Jun 10 '17 at 16:37

1 Answers1

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Use the Abrem package:

install.packages("abrem", repos="http://R-Forge.R-project.org")

You may need to manually install an older version of RccpArmadillo if you have issues like I did:

install.packages("https://cran.r-project.org/src/contrib/Archive/RcppArmadillo/RcppArmadillo_0.6.100.0.0.tar.gz", repos=NULL, type="source")

Then have at it:

library(abrem)

a = Abrem(fail = c(2, 11, 49, ...), susp = c(9, 44))
a = abrem.fit(a, dist = 'weibull', method.fit = 'mle')
a = abrem.conf(a) # add 90% confidence bands
plot.abrem(a) # plot the points and fit distribution
print.abrem(a) # print the results, which includes the fitted parameters

I may have confused your failures vs. suspensions data, but hopefully the example makes it clear where each goes.

brittohalloran
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