binormal {VGAM} | R Documentation

## Bivariate Normal Distribution Family Function

### Description

Maximum likelihood estimation of the five parameters of a bivariate normal distribution.

### Usage

    binormal(lmean1 = "identitylink", lmean2 = "identitylink",
             lsd1   = "loglink",     lsd2   = "loglink",
             lrho   = "rhobitlink",
             imean1 = NULL,       imean2 = NULL,
             isd1   = NULL,       isd2   = NULL,
             irho   = NULL,       imethod = 1,
             eq.mean = FALSE,     eq.sd = FALSE,
             zero = c("sd", "rho"))

### Arguments

`lmean1, lmean2, lsd1, lsd2, lrho` | Link functions applied to the means, standard deviations and `rho` parameters. See `Links` for more choices. Being positive quantities, a log link is the default for the standard deviations.
`imean1, imean2, isd1, isd2, irho, imethod, zero` | See `CommonVGAMffArguments` for more information.
`eq.mean, eq.sd` | Logical or formula. Constrains the means or the standard deviations to be equal.

### Details

For the bivariate normal distribution, this fits a linear model (LM) to the means, and by default, the other parameters are intercept-only. The response should be a two-column matrix. The correlation parameter is `rho`, which lies between -1 and 1 (thus the `rhobitlink` link is a reasonable choice). The fitted means are returned as the fitted values, which is in the form of a two-column matrix. Fisher scoring is implemented.

### Value

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

### Warning

This function may be renamed to `normal2()` or something like that at a later date.

### Note

If both equal means and equal standard deviations are desired then use something like `constraints = list("(Intercept)" = matrix(c(1,1,0,0,0, 0,0,1,1,0 ,0,0,0,0,1), 5, 3))` and maybe `zero = NULL` etc.

### Author(s)

T. W. Yee

### See Also

`uninormal`, `trinormal`, `pbinorm`, `bistudentt`.

### Examples

    set.seed(123); nn <- 1000
    bdata <- data.frame(x2 = runif(nn), x3 = runif(nn))
    bdata <- transform(bdata, y1 = rnorm(nn, 1 + 2 * x2),
                              y2 = rnorm(nn, 3 + 4 * x2))
    fit1 <- vglm(cbind(y1, y2) ~ x2,
                 binormal(eq.sd = TRUE), data = bdata, trace = TRUE)
    coef(fit1, matrix = TRUE)
    constraints(fit1)
    summary(fit1)

    # Estimated P(Y1 <= y1, Y2 <= y2) under the fitted model
    var1  <- loglink(2 * predict(fit1)[, "loglink(sd1)"], inverse = TRUE)
    var2  <- loglink(2 * predict(fit1)[, "loglink(sd2)"], inverse = TRUE)
    cov12 <- rhobitlink(predict(fit1)[, "rhobitlink(rho)"], inverse = TRUE)
    head(with(bdata, pbinorm(y1, y2,
                             mean1 = predict(fit1)[, "mean1"],
                             mean2 = predict(fit1)[, "mean2"],
                             var1 = var1, var2 = var2, cov12 = cov12)))

[Package VGAM version 1.1-1 Index]