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VGAM (version 0.8-1)

plackett: Plackett's Bivariate Distribution Family Function

Description

Estimate the association parameter of Plackett's bivariate distribution by maximum likelihood estimation.

Usage

plackett(link="loge", earg=list(), ioratio=NULL, method.init=1, nsimEIM=200)

Arguments

link
Link function applied to the (positive) odds ratio $\psi$. See Links for more choices.
earg
List. Extra argument for the link. See earg in Links for general information.
ioratio
Numeric. Optional initial value for $\psi$. If a convergence failure occurs try assigning a value or a different value.
method.init
An integer with value 1 or 2 which specifies the initialization method for the parameter. If failure to converge occurs try another value and/or else specify a value for ioratio.

Value

  • An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm and vgam.

Details

The defining equation is $$\psi = H \times (1-y_1-y_2+H) / ((y_1-H) \times (y_2-H))$$ where $P(Y_1 \leq y_1, Y_2 \leq y_2) = H_{\psi}(y_1,y_2)$ is the cumulative distribution function. The density function is $h_{\psi}(y_1,y_2) =$ $$\psi [1 + (\psi-1)(y_1 + y_2 - 2 y_1 y_2) ] / \left( [1 + (\psi-1)(y_1 + y_2) ]^2 - 4 \psi (\psi-1) y_1 y_2 \right)^{3/2}$$ for $\psi > 0$. Some writers call $\psi$ the cross product ratio but it is called the odds ratio here. The support of the function is the unit square. The marginal distributions here are the standard uniform although it is commonly generalized to other distributions.

If $\psi = 1$ then $h_{\psi}(y_1,y_2) = y_1 y_2$, i.e., independence. As the odds ratio tends to infinity one has $y_1=y_2$. As the odds ratio tends to 0 one has $y_2=1-y_1$.

Fisher scoring is implemented using rplack. Convergence is often quite slow.

References

Plackett, R. L. (1965) A class of bivariate distributions. Journal of the American Statistical Association, 60, 516--522.

See Also

rplack, frank.

Examples

Run this code
ymat = rplack(n=2000, oratio=exp(2))
plot(ymat, col="blue")
fit = vglm(ymat ~ 1, fam=plackett, trace=TRUE)
coef(fit, matrix=TRUE)
Coef(fit)
vcov(fit)
head(fitted(fit))
summary(fit)

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