Binom2.rho {VGAM} | R Documentation |
Density and random generation for a bivariate probit model. The correlation parameter rho is the measure of dependency.
rbinom2.rho(n, mu1, mu2=if(exchangeable) mu1 else stop("'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)
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 * 2 matrix of 1s
and 0s is returned.
If FALSE , then a n * 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?
|
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.
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.
T. W. Yee
# Example 1 nn = 2000 (myrho <- rhobit(2, inverse=TRUE)) ymat = rbinom2.rho(n=nn, mu1=0.8, rho=myrho, exch=TRUE) (mytab <- table(ymat[,1], ymat[,2])) fit = vglm(ymat ~ 1, binom2.rho(exch=TRUE)) coef(fit, matrix=TRUE) # Example 2 x = sort(runif(nn)) mu1 = probit(-2+4*x, inverse=TRUE) mu2 = probit(-1+3*x, inverse=TRUE) dmat = dbinom2.rho(mu1=mu1, mu2=mu2, rho=myrho) ymat = rbinom2.rho(n=nn, mu1=mu1, mu2=mu2, rho=myrho) fit2 = vglm(ymat ~ x, binom2.rho) coef(fit2, matrix=TRUE) ## Not run: matplot(x, 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)")) ## End(Not run)