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)))) 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? Thanx in advance Sachin --------------------------------- [[alternative HTML version deleted]] ______________________________________________ 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

Sachin J <sachinj.2006 at yahoo.com> 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)))) > > 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?What''s wrong with pweibull, plnorm, etc.? Or pnorm for that matter.... -- O__ ---- Peter Dalgaard ?ster Farimagsgade 5, Entr.B c/ /''_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907

Peter Dalgaard wrote: > Sachin J <sachinj.2006 at yahoo.com> 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))))))