Natural Exponential Family Generalized Hyperbolic Secant Distribution Family Function
Maximum likelihood estimation of the 2-parameter log F distribution.
logF(lshape1 = "loglink", lshape2 = "loglink",
ishape1 = NULL, ishape2 = 1, imethod = 1)lshape1, lshape2 |
Parameter link functions for
the shape parameters.
Called alpha and beta respectively.
See |
ishape1, ishape2 |
Optional initial values for the shape parameters.
If given, it must be numeric and values are recycled to the
appropriate length.
The default is to choose the value internally.
See |
imethod |
Initialization method.
Either the value 1, 2, or ....
See |
The density for this distribution is
f(y; alpha, beta) = exp(α y) / [B(α,β) * (1 + exp(y))^(α + β)]
where y is real,
α > 0,
β > 0,
B(., .) is the beta function beta.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm
and vgam.
Thomas W. Yee
Jones, M. C. (2008). On a class of distributions with simple exponential tails. Statistica Sinica, 18(3), 1101–1110.
nn <- 1000
ldata <- data.frame(y1 = rnorm(nn, m = +1, sd = exp(2)), # Not proper data
x2 = rnorm(nn, m = -1, sd = exp(2)),
y2 = rnorm(nn, m = -1, sd = exp(2))) # Not proper data
fit1 <- vglm(y1 ~ 1 , logF, data = ldata, trace = TRUE)
fit2 <- vglm(y2 ~ x2, logF, data = ldata, trace = TRUE)
coef(fit2, matrix = TRUE)
summary(fit2)
vcov(fit2)
head(fitted(fit1))
with(ldata, mean(y1))
max(abs(head(fitted(fit1)) - with(ldata, mean(y1))))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.