Fit of Normal and Exponential-Normal Mixtures to Binned Data

Fit mixture distributions to binned data with a maximum likelihood method, inspired by Venables and Ripley (2002)

fit_unimix(
  breaks,
  counts,
  parms,
  type = c("en", "enn", "ennn", "ennnn", "ennnnn", "n", "nn", "nnn", "nnnn", "nnnnn"),
  sd_min = 0,
  ...
)

fit_n(breaks, counts, parms, sd_min = 0, ...)

fit_en(breaks, counts, parms, sd_min = 0, ...)

fit_nn(breaks, counts, parms, sd_min = 0, ...)

fit_enn(breaks, counts, parms, sd_min = 0, ...)

fit_nnn(breaks, counts, parms, sd_min = 0, ...)

fit_nnnn(breaks, counts, parms, sd_min = 0, ...)

fit_ennn(breaks, counts, parms, sd_min = 0, ...)

fit_ennnn(breaks, counts, parms, sd_min = 0, ...)

fit_nnnnn(breaks, counts, parms, sd_min = 0, ...)

fit_ennnnn(breaks, counts, parms, sd_min = 0, ...)

Arguments

breaks	upper class limits of the data
counts	frequency of observations
parms	list of initial parameters for the mixture
type	of the mixture distribution, e.g. 'enn' for exponential-normal-normal
sd_min	lower boundary value for standard deviation and rate parameter
...	additional arguments passed to mle2

Value

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0

Bolker, Ben and R Development Core Team (2017) bbmle: Tools for General Maximum Likelihood Estimation. R package version 1.0.20. https://CRAN.R-project.org/package=bbmle