r - Fitting truncnorm using fitdistrplus -

i trying fit truncated normal distribution data. however, have been running following error:

<simpleerror in optim(par = vstart, fn = fnobj, fix.arg = fix.arg, obs = data,     gr = gradient, ddistnam = ddistname, hessian = true, method = meth,     lower = lower, upper = upper, ...): non-finite finite-difference value [1]> error in fitdist(testdata, "truncnorm", start = list(a = 0, mean = 0.8,  :    function mle failed estimate parameters,                  error code 100 

i'm not sure what's going wrong - i've read in cases there can problems fitting if initial guesses wrong or higher actual values, i've tried number of different start values , none seem work.

here small sample of data, , code used error:

library(fitdistrplus) library(truncnorm) testdata <- c(3.2725167726, 0.1501345235, 1.5784128343, 1.218953218, 1.1895520932,                2.659871271, 2.8200152609, 0.0497193249, 0.0430677458, 1.6035277181,                0.2003910167, 0.4982836845, 0.9867184303, 3.4082793339, 1.6083770189,                2.9140912221, 0.6486576911, 0.335227878, 0.5088426851, 2.0395797721,                1.5216239237, 2.6116576364, 0.1081283479, 0.4791143698, 0.6388625172,                0.261194346, 0.2300098384, 0.6421213993, 0.2671907741, 0.1388568942,                0.479645736, 0.0726750815, 0.2058983462, 1.0936704833, 0.2874115077,                0.1151566887, 0.0129750118, 0.152288794, 0.1508512023, 0.176000366,                0.2499423442, 0.8463027325, 0.0456045486, 0.7689214668, 0.9332181529,                0.0290242892, 0.0441181842, 0.0759601229, 0.0767983979, 0.1348839304 )  fitdist(testdata, "truncnorm", start = list(a = 0, mean = 0.8, sd = 0.9)) 

the problem mle estimator provides increasingly negative estimates parameter mean lower bound a tends 0 (note latter must not specified within start parameter, within fix.arg):

fitdist(testdata, "truncnorm", fix.arg=list(a=-.5),         start = list(mean = mean(testdata), sd = sd(testdata))) fitdist(testdata, "truncnorm", fix.arg=list(a=-.2),         start = list(mean = mean(testdata), sd = sd(testdata))) fitdist(testdata, "truncnorm", fix.arg=list(a=-.15),         start = list(mean = mean(testdata), sd = sd(testdata))) 

one possibility prevent large negative values mean use lower bound optimisation:

fitdist(testdata, "truncnorm", fix.arg=list(a=0),         start = list(mean = mean(testdata), sd = sd(testdata)),         optim.method="l-bfgs-b", lower=c(0, 0)) 

however, alters estimation procedure; in fact imposing additional constraints on parameters , might obtain different answers different lower bounds.