logF {VGAM} | R Documentation |
Natural Exponential Family Generalized Hyperbolic Secant Distribution Family Function
Description
Maximum likelihood estimation of the 2-parameter log F distribution.
Usage
logF(lshape1 = "loglink", lshape2 = "loglink",
ishape1 = NULL, ishape2 = 1, imethod = 1)
Arguments
lshape1 , lshape2 |
Parameter link functions for
the shape parameters.
Called |
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 |
Details
The density for this distribution is
f(y; \alpha, \beta) = \exp(\alpha y) / [B(\alpha,\beta)
(1 + e^y)^{\alpha + \beta}]
where y
is real,
\alpha > 0
,
\beta > 0
,
B(., .)
is the beta function
beta
.
Value
An object of class "vglmff"
(see
vglmff-class
). The object is used by modelling
functions such as vglm
and vgam
.
Author(s)
Thomas W. Yee
References
Jones, M. C. (2008). On a class of distributions with simple exponential tails. Statistica Sinica, 18(3), 1101–1110.
See Also
Examples
nn <- 1000
ldata <- data.frame(y1 = rnorm(nn, +1, sd = exp(2)), # Not proper data
x2 = rnorm(nn, -1, sd = exp(2)),
y2 = rnorm(nn, -1, sd = exp(2))) # Not proper data
fit1 <- vglm(y1 ~ 1 , logF, ldata, trace = TRUE)
fit2 <- vglm(y2 ~ x2, logF, 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))))