Nothing
`plot.MOStest` <-
function(x, which = c(1,2,3,6), ...)
{
show <- rep(FALSE, 8)
show[which] <- TRUE
if (show[1]) {
X <- x$mod$model$x
Y <- x$mod$y
xx <- seq(min(X), max(X), len=101)
pre <- predict(x$mod, newdata=list(x = xx), se=TRUE)
g <- x$mod$family$linkinv
fv <- g(pre$fit)
hi <- g(pre$fit + 2*pre$se)
lo <- g(pre$fit - 2*pre$se)
plot(X, Y, ...)
matlines(xx, cbind(fv, hi, lo), lty=c(1, 2, 2), lwd=c(2, 1, 1), col=1, ...)
}
if (show[2]) {
## Covariance ellipse for the coefficients
s <- summary(x$mod)
k <- coef(s)[2:3, 1:2]
## Fix level to 0.95 (should be changed to an argument?)
level = 0.95
if (family(x$mod)$family %in% c("poisson", "binomial"))
scale <- sqrt(qchisq(level, 2))
else
scale <- sqrt(2 * qf(level, 2, s$df[2]))
ci <- veganCovEllipse(s$cov.scaled[2:3, 2:3], k[,1], scale)
plot(ci, type="l", lwd=2, xlim=range(ci[,1],0), ylim=range(ci[,2],0), ...)
abline(h=0, lty=2, ...)
par <- x$hump[c("min", "max")]
par[par==0] <- sqrt(.Machine$double.eps)
abline(0, -1/2/par[1], ...)
abline(0, -1/2/par[2], ...)
mul <- qnorm(1 - (1 - level)/2)
segments(k[1,1] - k[1,2]*mul, k[2,1], k[1,1]+k[1,2]*mul, k[2,1], lty=3)
segments(k[1,1], k[2,1]-k[2,2]*mul, k[1,1], k[2,1]+k[2,2]*mul, lty=3)
}
if (any(show[-c(1,2)])) {
still <- which(show[-c(1,2)])
plot(x$mod, which = still, ...)
}
invisible()
}
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