Computes the negative log likelihood of an underlying continuous distribution from binned data.
Source:R/mle.R
bin_log_likelihood.RdFrom: https://stats.stackexchange.com/a/68238/97417
Arguments
- theta
parameters for distribution
- cdf
the cdf function (pnorm, pgamma, punif)
- counts
the histogram bins
- bin_lower
the lower bounds for the histogram bins (a, b]
- bin_upper
the upper bounds for the histogram bins (a, b]
- ...
Additional parameters to pass into
cdf
Examples
set.seed(100);
x <- rnorm(1000, sd = 5)
bin.x <- observations_to_histogram(x)
bin_log_likelihood(
theta = c(0, 4),
cdf = pnorm,
counts = bin.x$histogram_data,
bin_lower = bin.x$interval_start,
bin_upper = bin.x$interval_end
)
#> [1] 3137.2
dist_optim <- DEoptim::DEoptim(
fn = bin_log_likelihood,
cdf = pnorm,
counts = bin.x$histogram_data,
bin_lower = bin.x$interval_start,
bin_upper = bin.x$interval_end,
lower = c(min(bin.x$interval_start), 0.01),
upper = c(max(bin.x$interval_end), 10),
control = list(
trace = TRUE,
itermax = 500
)
)
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print(dist_optim$optim$bestmem)
#> par1 par2
#> 0.08099958 5.15636586