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)
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