r - Scaling for estimating scale and shape parameter with fitdist function (fitdistrplus package) -

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as specified in title have scaling problem fitdist function in r (fitdistrplus package).

please have @ following code:

# initialize arrays storing result fit_store_scale <- rep(na, 3) fit_store_shape <- rep(na, 3)  # load data data1 <- c(7.616593e-05, 5.313253e-05, 1.604328e-04, 6.482365e-05,            4.217499e-05, 6.759114e-05, 3.531301e-05, 1.934228e-05,            6.263665e-05, 8.796205e-06) data2 <- c(7.616593e-06, 5.313253e-06, 1.604328e-05, 6.482365e-06,            4.217499e-06, 6.759114e-06, 3.531301e-06, 1.934228e-06,            6.263665e-06, 8.796205e-07) data3 <- c(7.616593e-07, 5.313253e-07, 1.604328e-06, 6.482365e-07,            4.217499e-07, 6.759114e-07, 3.531301e-07, 1.934228e-07,            6.263665e-07, 8.796205e-08) # form data frame data <- data.frame(data1, data2, data3)  # set scaling factor scaling <- 1        #works without warnings , errors at:                         #10000 (data1), 100000 (data2) or                     #1000000 (data3)  # store scale , shape parameter of data1, data2 , data3 in array for(i in 1:3) {     fit.w1 <- fitdist(data[[i]]*scaling,"weibull", method = "mle")     fit_store_scale[i] <- fit.w1$estimate[[2]]*1/scaling     #1/scaling needed correcting scale parameter     fit_store_shape[i] <- fit.w1$estimate[[1]] }

i have 3 vectors of data, stored in data frame. want use fitdist function estimating scale , shape parameter separately each column of data (data1, data2 , data3) , storing them in fit_store_scale , fit_store_shape respectively.

the problem here fitdist function doesn't work without appropriate scaling factor , data1, data2 , data3 need different factors. looking solution determine optimal scaling factor automatically each column of data , getting fitdist function work in end.

if you're not absolutely wedded fitdist, use little bit more robust -- following fits weibull parameters on log scale, , uses nelder-mead rather gradient-based approach. doesn't seem have problems fitting these data.

dd <- data.frame(data1,data2,data3) library("bbmle") fx <- function(x) {     m1 <- mle2(y~dweibull(shape=exp(logshape),scale=exp(logscale)),            data=data.frame(y=x),start=list(logshape=0,logscale=0),            method="nelder-mead")     exp(coef(m1)) } t(sapply(dd,fx))  ## not quite output format asked for,                   ##  easy enough convert. ##       logshape     logscale ## data1 1.565941 6.589057e-05 ## data2 1.565941 6.589054e-06 ## data3 1.565941 6.589055e-07

this approach should work reasonably distribution have standard distribution (d*()) function.


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