VGAM (version 0.9-2)

Binom2.rho: Bivariate Probit Model

Description

Density and random generation for a bivariate probit model. The correlation parameter rho is the measure of dependency.

Usage

rbinom2.rho(n, mu1,
           mu2 = if(exchangeable) mu1 else stop("argument 'mu2' not specified"),
           rho = 0, exchangeable = FALSE, twoCols = TRUE,
           colnames = if(twoCols) c("y1","y2") else c("00", "01", "10", "11"),
           ErrorCheck = TRUE)
dbinom2.rho(mu1,
           mu2 = if(exchangeable) mu1 else stop("'mu2' not specified"),
           rho = 0, exchangeable = FALSE,
           colnames = c("00", "01", "10", "11"), ErrorCheck = TRUE)

Arguments

n

number of observations. Must be a single positive integer. The arguments mu1, mu2, rho are recycled to length n.

mu1, mu2

The marginal probabilities. Only mu1 is needed if exchangeable = TRUE. Values should be between 0 and 1.

rho

The correlation parameter. Must be numeric and lie between $-1$ and $1$. The default value of zero means the responses are uncorrelated.

exchangeable

Logical. If TRUE, the two marginal probabilities are constrained to be equal.

twoCols

Logical. If TRUE, then a $n$ $\times$ $2$ matrix of 1s and 0s is returned. If FALSE, then a $n$ $\times$ $4$ matrix of 1s and 0s is returned.

colnames

The dimnames argument of matrix is assigned list(NULL, colnames).

ErrorCheck

Logical. Do some error checking of the input parameters?

Value

• The function rbinom2.rho returns either a 2 or 4 column matrix of 1s and 0s, depending on the argument twoCols.

  The function dbinom2.rho returns a 4 column matrix of joint probabilities; each row adds up to unity.

Details

The function rbinom2.rho generates data coming from a bivariate probit model. The data might be fitted with the VGAM family function binom2.rho.

The function dbinom2.rho does not really compute the density (because that does not make sense here) but rather returns the four joint probabilities.

See Also

binom2.rho.

Examples

(myrho <- rhobit(2, inverse = TRUE))  # Example 1
ymat <- rbinom2.rho(nn <- 2000, mu1 = 0.8, rho = myrho, exch = TRUE)
(mytab <- table(ymat[, 1], ymat[, 2], dnn = c("Y1", "Y2")))                                     
fit <- vglm(ymat ~ 1, binom2.rho(exch = TRUE))
coef(fit, matrix = TRUE)

bdata <- data.frame(x2 = sort(runif(nn)))  # Example 2
bdata <- transform(bdata, mu1 = probit(-2+4*x2, inverse = TRUE),
                          mu2 = probit(-1+3*x2, inverse = TRUE))
dmat <- with(bdata, dbinom2.rho(mu1, mu2, myrho))
ymat <- with(bdata, rbinom2.rho(nn, mu1, mu2, myrho))
fit2 <- vglm(ymat ~ x2, binom2.rho, bdata)
coef(fit2, matrix = TRUE)
matplot(with(bdata, x2), dmat, lty = 1:4, col = 1:4,
        type = "l", main = "Joint probabilities",
        ylim = 0:1, lwd = 2, ylab = "Probability")
legend(x = 0.25, y = 0.9, lty = 1:4, col = 1:4, lwd = 2,
       legend = c("1 = (y1=0, y2=0)", "2 = (y1=0, y2=1)",
                  "3 = (y1=1, y2=0)", "4 = (y1=1, y2=1)"))