View source: R/family.bivariate.R
biamhcop | R Documentation |
Estimate the association parameter of Ali-Mikhail-Haq's bivariate distribution by maximum likelihood estimation.
biamhcop(lapar = "rhobitlink", iapar = NULL, imethod = 1,
nsimEIM = 250)
lapar |
Link function applied to the association parameter
|
iapar |
Numeric. Optional initial value for |
imethod |
An integer with value |
nsimEIM |
See |
The cumulative distribution function is
P(Y_1 \leq y_1, Y_2 \leq y_2) = y_1 y_2
/ ( 1 - \alpha (1 - y_1) (1 - y_2) )
for -1 < \alpha < 1
.
The support of the function is the unit square.
The marginal distributions are the standard uniform distributions.
When \alpha = 0
the random variables are
independent.
This is an Archimedean copula.
An object of class "vglmff"
(see vglmff-class
).
The object is used by modelling functions
such as vglm
and vgam
.
The response must be a two-column matrix. Currently, the fitted value is a matrix with two columns and values equal to 0.5. This is because each marginal distribution corresponds to a standard uniform distribution.
T. W. Yee and C. S. Chee
Balakrishnan, N. and Lai, C.-D. (2009). Continuous Bivariate Distributions, 2nd ed. New York: Springer.
rbiamhcop
,
bifgmcop
,
bigumbelIexp
,
rbilogis
,
simulate.vlm
.
ymat <- rbiamhcop(1000, apar = rhobitlink(2, inverse = TRUE))
fit <- vglm(ymat ~ 1, biamhcop, trace = TRUE)
coef(fit, matrix = TRUE)
Coef(fit)
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