# biamhcopUC: Ali-Mikhail-Haq Bivariate Distribution In VGAM: Vector Generalized Linear and Additive Models

## Description

Density, distribution function, and random generation for the (one parameter) bivariate Ali-Mikhail-Haq distribution.

## Usage

 ```1 2 3``` ```dbiamhcop(x1, x2, apar, log = FALSE) pbiamhcop(q1, q2, apar) rbiamhcop(n, apar) ```

## Arguments

 `x1, x2, q1, q2` vector of quantiles. `n` number of observations. Same as `runif` `apar` the association parameter. `log` Logical. If `TRUE` then the logarithm is returned.

## Details

See `biamhcop`, the VGAM family functions for estimating the parameter by maximum likelihood estimation, for the formula of the cumulative distribution function and other details.

## Value

`dbiamhcop` gives the density, `pbiamhcop` gives the distribution function, and `rbiamhcop` generates random deviates (a two-column matrix).

## Author(s)

T. W. Yee and C. S. Chee

`biamhcop`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ``` x <- seq(0, 1, len = (N <- 101)); apar <- 0.7 ox <- expand.grid(x, x) zedd <- dbiamhcop(ox[, 1], ox[, 2], apar = apar) ## Not run: contour(x, x, matrix(zedd, N, N), col = "blue") zedd <- pbiamhcop(ox[, 1], ox[, 2], apar = apar) contour(x, x, matrix(zedd, N, N), col = "blue") plot(r <- rbiamhcop(n = 1000, apar = apar), col = "blue") par(mfrow = c(1, 2)) hist(r[, 1]) # Should be uniform hist(r[, 2]) # Should be uniform ## End(Not run) ```