A collection and description of moment and maximum likelihood estimators to fit the parameters of a distribution.
The functions are:
nFit |
MLE parameter fit for a normal distribution, |
tFit |
MLE parameter fit for a Student t-distribution, |
nFit(x, doplot = TRUE, span = "auto", title = NULL, description = NULL, ...)
tFit(x, df = 4, doplot = TRUE, span = "auto", trace = FALSE, title = NULL, description = NULL, ...)
stableFit(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, type = c("q", "mle"), doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL)
"show"(object)
nlminb
.
alpha
, beta
, gamma
,
and delta
:
value of the index parameter alpha
with alpha = (0,2]
;
skewness parameter beta
, in the range [-1, 1];
scale parameter gamma
; and
shift parameter delta
.
df > 2
, maybe non-integer. By default a value of 4 is
assumed.
span=seq(min, max,
times = n)
, where, min
and max
are the
left and right endpoints of the range, and n
gives
the number of the intermediate points.
"mle"
, the maximum log likelihood
approach, or "qm"
, McCulloch's quantile method.
tFit
, hypFit
and nigFit
return
a list with the following components:alpha
and beta
without asymptotic bias. Unfortunately, the estimators provided by
McCulloch have restriction alpha>0.6
.
## nFit -
# Simulate random normal variates N(0.5, 2.0):
set.seed(1953)
s = rnorm(n = 1000, 0.5, 2)
## nigFit -
# Fit Parameters:
nFit(s, doplot = TRUE)
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