| Binom2.or | R Documentation |
Density and random generation for a bivariate binary regression model using an odds ratio as the measure of dependency.
rbinom2.or(n, mu1,
mu2 = if (exchangeable) mu1 else
stop("argument 'mu2' not specified"),
oratio = 1, exchangeable = FALSE, tol = 0.001,
twoCols = TRUE, colnames = if (twoCols) c("y1","y2") else
c("00", "01", "10", "11"),
ErrorCheck = TRUE)
dbinom2.or(mu1, mu2 = if (exchangeable) mu1 else
stop("'mu2' not specified"),
oratio = 1, exchangeable = FALSE, tol = 0.001,
colnames = c("00", "01", "10", "11"), ErrorCheck = TRUE)
n |
number of observations.
Same as in |
mu1, mu2 |
The marginal probabilities.
Only |
oratio |
Odds ratio. Must be numeric and positive. The default value of unity means the responses are statistically independent. |
exchangeable |
Logical. If |
twoCols |
Logical.
If |
colnames |
The |
tol |
Tolerance for testing independence. Should be some small positive numerical value. |
ErrorCheck |
Logical. Do some error checking of the input parameters? |
The function rbinom2.or generates data coming from a
bivariate binary response model.
The data might be fitted with
the VGAM family function binom2.or.
The function dbinom2.or does not really compute the
density (because that does not make sense here) but rather
returns the four joint probabilities.
The function rbinom2.or returns
either a 2 or 4 column matrix of 1s and 0s, depending on the
argument twoCols.
The function dbinom2.or returns
a 4 column matrix of joint probabilities; each row adds up
to unity.
T. W. Yee
binom2.or.
nn <- 1000 # Example 1
ymat <- rbinom2.or(nn, mu1 = logitlink(1, inv = TRUE),
oratio = exp(2), exch = TRUE)
(mytab <- table(ymat[, 1], ymat[, 2], dnn = c("Y1", "Y2")))
(myor <- mytab["0","0"] * mytab["1","1"] / (mytab["1","0"] *
mytab["0","1"]))
fit <- vglm(ymat ~ 1, binom2.or(exch = TRUE))
coef(fit, matrix = TRUE)
bdata <- data.frame(x2 = sort(runif(nn))) # Example 2
bdata <- transform(bdata,
mu1 = logitlink(-2 + 4 * x2, inverse = TRUE),
mu2 = logitlink(-1 + 3 * x2, inverse = TRUE))
dmat <- with(bdata, dbinom2.or(mu1 = mu1, mu2 = mu2,
oratio = exp(2)))
ymat <- with(bdata, rbinom2.or(n = nn, mu1 = mu1, mu2 = mu2,
oratio = exp(2)))
fit2 <- vglm(ymat ~ x2, binom2.or, data = bdata)
coef(fit2, matrix = TRUE)
## Not run:
matplot(with(bdata, x2), dmat, lty = 1:4, col = 1:4,
main = "Joint probabilities", ylim = 0:1, type = "l",
ylab = "Probabilities", xlab = "x2", las = 1)
legend("top", lty = 1:4, col = 1:4,
legend = c("1 = (y1=0, y2=0)", "2 = (y1=0, y2=1)",
"3 = (y1=1, y2=0)", "4 = (y1=1, y2=1)"))
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.