amh {VGAM} | R Documentation |
Estimate the association parameter of Ali-Mikhail-Haq's bivariate distribution by maximum likelihood estimation.
amh(lalpha="rhobit", ealpha=list(), ialpha=NULL, method.init=1, nsimEIM=250)
lalpha |
Link function applied to the association parameter
alpha, which is real
and -1 < alpha < 1.
See Links for more choices.
|
ealpha |
List. Extra argument for the link.
See earg in Links for general information.
|
ialpha |
Numeric. Optional initial value for alpha.
By default, an initial value is chosen internally.
If a convergence failure occurs try assigning a different value.
Assigning a value will override the argument method.init .
|
method.init |
An integer with value 1 or 2 which
specifies the initialization method. If failure to converge occurs
try the other value, or else specify a value for ialpha .
|
nsimEIM |
See CommonVGAMffArguments for more information.
|
The cumulative distribution function is
P(Y1 <= y1, Y2 <= y2) = y1 * y2 / ( 1 - alpha * (1 - y1) * (1 - y2) )
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.
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
Hutchinson, T. P. and Lai, C. D. (1990) Continuous Bivariate Distributions, Emphasising Applications, Adelaide, South Australia: Rumsby Scientific Publishing.
ramh
,
fgm
,
gumbelIbiv
.
ymat = ramh(1000, alpha=rhobit(2, inverse=TRUE)) fit = vglm(ymat ~ 1, amh, trace = TRUE) coef(fit, mat=TRUE) Coef(fit)