VGAM (version 1.1-5)

bistudentt: Bivariate Student-t Family Function

Description

Estimate the degrees of freedom and correlation parameters of the (bivariate) Student-t distribution by maximum likelihood estimation.

Usage

bistudentt(ldf = "logloglink", lrho = "rhobitlink",
           idf = NULL, irho = NULL, imethod = 1,
           parallel = FALSE, zero = "rho")

Arguments

ldf, lrho, idf, irho, imethod

Details at CommonVGAMffArguments. See Links for more link function choices.

parallel, zero

Details at CommonVGAMffArguments.

Value

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

Warning

The working weight matrices have not been fully checked.

Details

The density function is $$f(y_1,y_2;\nu ,\rho )=\frac{1}{2\pi \sqrt{1-{\rho }^2}}(1+y_1^2+y_2^2-2\rho y_1{y}_2)/(\nu (1-{\rho }^2){)}^{(\nu +2)/2}$$ for $-1<\rho <1$, and real $y_1$ and $y_2$.

This VGAM family function can handle multiple responses, for example, a six-column matrix where the first 2 columns is the first out of three responses, the next 2 columns being the next response, etc.

References

Schepsmeier, U. and Stober, J. (2014). Derivatives and Fisher information of bivariate copulas. Statistical Papers 55, 525--542.

See Also

dbistudentt, binormal, pt.

Examples

# NOT RUN {
nn <- 1000
mydof <- logloglink(1, inverse = TRUE)
ymat <- cbind(rt(nn, df = mydof), rt(nn, df = mydof))
bdata <- data.frame(y1 = ymat[, 1], y2 = ymat[, 2],
                    y3 = ymat[, 1], y4 = ymat[, 2], x2 = runif(nn))
summary(bdata)
# }
# NOT RUN {
 plot(ymat, col = "blue") 
# }
# NOT RUN {
fit1 <- vglm(cbind(y1, y2, y3, y4) ~ 1,  # 2 responses, e.g., (y1,y2) is the 1st
             fam = bistudentt,  # crit = "coef",  # Sometimes a good idea
             data = bdata, trace = TRUE)

coef(fit1, matrix = TRUE)
Coef(fit1)
head(fitted(fit1))
summary(fit1)
# }