Description Usage Arguments References
Fit mixture distributions to binned data with a maximum likelihood method, inspired by Venables and Ripley (2002)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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, ...)
|
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 |
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
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