Here is an approach using optim: tmpfunc <- function(param){ ml<-param[1] sl<-param[2] (qlnorm(.15,ml,sl)-10)^2 + (qlnorm(.5,ml,sl)-30)^2 }
res <- optim(c(1,2), tmpfunc) res <- optim(res$par, tmpfunc) res hope this helps, Greg Snow, Ph.D. Statistical Data Center, LDS Hospital Intermountain Health Care [EMAIL PROTECTED] (801) 408-8111 >>> Per Bak <[EMAIL PROTECTED]> 05/05/05 07:37AM >>> Hi, I have been returning to the same problem a number of times without success and now look for help with the following: How do I fit a distribution function with the same number of parameters as there are quantiles and values such that I get an exact solution as opposed to a minimum least squares type solution? Say, which lognormal distribution has a 15% quantile of 10 and a 50% quantile of 30? My hope is that the solution to this problem can be expanded, such that I can fit three quantiles with the Generalized Weibull distribution (which has three parameters). This is what I attempt without success: ======================= library(stats) Target <- data.frame( quantiles = c(0.15, 0.50), values = c(10, 30)) dist <- nls(values ~ qlnorm(quantiles, mu, sd), data = Target, start = list(mu = 30, sd = 5)) I can see that it works with one more value: ========================== Target <- data.frame( quantiles = c(0.15, 0.50, 0.85), values = c(10, 30, 60)) dist <- nls(values ~ qlnorm(quantiles, mu, sd), data = Target, start = list(mu = 30, sd = 5)) Kind regards, Per Bak Copenhagen, Denmark ______________________________________________ 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 ______________________________________________ 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