Peter Dalgaard wrote: > Sachin J <[EMAIL PROTECTED]> writes: > > >>Hi, >> >> I have a data set which is assumed to follow weibull distr'. How can I find of cdf for this data. For example, for normal data I used (package - lmomco) >> >> >cdfnor(15,parnor(lmom.ub(c(df$V1))))
If X is a Weibull random variable then -X has a generalized extreme-value distribution. So something like cdfgev(-15,pargev(lmom.ub(-c(df$V1)))) should do the trick. >> Also, lmomco package does not have functions for finding cdf for some of the distributions like lognormal. Is there any other package, which can handle these distributions? I recommend that you use the generalized normal distribution, a reparametrized and extended version of the lognormal that accommodates distributions with negative as well as positive skewness. See Hosking & Wallis, "Regional Frequency Analysis", Cambridge Univ. Press, 1997, p.198. The relevant routines in lmomco are cdfgno, lmomgno, pargno and quagno. > What's wrong with pweibull, plnorm, etc.? Or pnorm for that matter.... What's wrong, or at least what I often find somewhat incovenient, is that R's distribution functions require the distribution parameters to be supplied as separate arguments rather than as a single vector. This complicates operations that involve passing parameters from one function to another. For example, the OP's one-liner above would, if pnorm were used, have to become something like par <- parnor(lmom.ub(c(df$V1))) pnorm(15, par[1], par[2]) or, if we still want to do it in one line, do.call('pnorm', as.list(c(15, parnor(lmom.ub(c(df$V1)))))) ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html