https://github.com/cran/bbmle
Tip revision: d34a66feff17afec42ea66e0515a827863b81577 authored by Ben Bolker on 19 December 2019, 16:30:02 UTC
version 1.0.22
version 1.0.22
Tip revision: d34a66f
parscale.Rout.save
R Under development (unstable) (2019-06-19 r76722) -- "Unsuffered Consequences"
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> library(bbmle)
Loading required package: stats4
> old_opt <- options(digits=3)
> tracelevel <- 0
>
> ## source("~/lib/R/pkgs/bbmle/pkg/R/mle.R
>
> set.seed(1002)
> X <- rexp(1000, rate = 0.0001)
> f <- function(X, rate) {
+ if (tracelevel>0 && rate<0) cat("rate<0: ",rate,"\n")
+ -sum(dexp(X, rate = rate, log = TRUE))
+ }
> if (FALSE) {
+ ## L-BFGS-B violates bounds, and gets stuck at lower bound
+ m <- mle2(minuslogl = f,
+ data = list(X = X),
+ start = list(rate = 0.01),
+ method = "L-BFGS-B",
+ control = list(trace = tracelevel,
+ parscale = 1e-4),
+ lower = c(rate = 1e-9))
+
+ profile(m, std.err=0.0001) ## finds new optimum
+
+ fsc <- function(X, rate) {
+ -sum(dexp(X, rate = rate*1e-4, log = TRUE))
+ }
+ msc <- mle2(minuslogl = fsc,
+ data = list(X = X),
+ start = list(rate = 100),
+ method = "L-BFGS-B",
+ control = list(trace = tracelevel),
+ lower = c(rate = 1e-5))
+
+ ## does it work if we scale by hand?
+ ## no, identical problem
+ }
>
> ## works fine with a better starting point
> m <- mle2(minuslogl = f,
+ data = list(X = X),
+ start = list(rate = 0.001),
+ method = "L-BFGS-B",
+ control = list(trace = tracelevel,
+ parscale=1e-4),
+ lower = c(rate = 1e-9))
> vcov(m)
rate
rate 1.05e-11
> confint(m)
2.5 % 97.5 %
9.61e-05 1.09e-04
>
>
> ## works OK despite warnings about 1-dimensional opt. with N-M
> (m0 <- mle2(minuslogl = f,
+ data = list(X = X),
+ start = list(rate = 0.01),
+ method = "Nelder-Mead",
+ control = list(trace = tracelevel, parscale = 1e-4)))
Call:
mle2(minuslogl = f, start = list(rate = 0.01), method = "Nelder-Mead",
data = list(X = X), control = list(trace = tracelevel, parscale = 1e-04))
Coefficients:
rate
0.000102
Log-likelihood: -10188
Warning message:
In optim(par = c(rate = 0.01), fn = function (p) :
one-dimensional optimization by Nelder-Mead is unreliable:
use "Brent" or optimize() directly
> vcov(m0)
rate
rate 1.05e-11
>
> confint(m0)
2.5 % 97.5 %
0.000096 0.000109
> confint(m0,method="quad")
2.5 % 97.5 %
0.000096 0.000109
> ## very similar (good quadratic surface, not surprising)
>
> m1 <- mle2(minuslogl = f,
+ data = list(X = X),
+ start = list(rate = 0.01),
+ method = "BFGS",
+ control = list(trace = tracelevel, parscale = 1e-4))
There were 11 warnings (use warnings() to see them)
>
>
> ## gets stuck? will have to investigate ...
> m2 <- mle2(minuslogl = f,
+ data = list(X = X),
+ start = list(rate = 0.01),
+ optimizer = "optimize",
+ lower=1e-9,upper=0.1)
>
> vcov(m2)
rate
rate 1.41e-11
> options(old_opt)
>
> proc.time()
user system elapsed
1.528 0.100 1.762