```
> mod
Call: glmmML(formula = y ~ x, data = dat, cluster = id)
coef se(coef) z Pr(>|z|)
(Intercept) -0.37000 0.4103 -0.9017 0.367
x 0.04571 0.2414 0.1893 0.850
Scale parameter in mixing distribution: 1.432 gaussian
Std. Error: 0.4267
LR p-value for H_0: sigma = 0: 0.0004959
Residual deviance: 126.3 on 97 degrees of freedom AIC: 132.3
> class(mod)
[1] "glmmML"
> print.glmmML
function (x, digits = max(3, getOption("digits") - 3), na.print = "",
...)
{
cat("\\nCall: ", deparse(x$call), "\\n\\n")
savedig <- options(digits = digits)
on.exit(options(savedig))
coef <- x$coefficients
se <- x$coef.sd
tmp <- cbind(coef, se, coef/se, signif(1 - pchisq((coef/se)^2,
1), digits - 1))
dimnames(tmp) <- list(names(coef), c("coef", "se(coef)",
"z", "Pr(>|z|)"))
cat("\\n")
prmatrix(tmp)
cat("\\nScale parameter in mixing distribution: ", x$sigma,
x$prior, "\\n")
cat("Std. Error: ", x$sigma.sd,
"\\n")
pv <- 0.5 * pchisq(x$cluster.null.deviance - x$deviance,
df = 1, lower.tail = FALSE)
cat("\\n LR p-value for H_0: sigma = 0: ", pv, "\\n")
if (x$boot) {
cat("\\n Bootstrap p-value for H_0: sigma = 0: ", x$bootP,
"(", x$boot, ")\\n")
}
cat("\\nResidual deviance:", format(signif(x$deviance, digits)),
"on", x$df.residual, "degrees of freedom", "\\tAIC:",
format(signif(x$aic, digits)), "\\n")
}
<bytecode: 0x0000000036be2a48>
<environment: namespace:glmmML>
> # Note the lines starting with pv <-
> pv <- 0.5 * pchisq(mod$cluster.null.deviance - mod$deviance, df = 1, lower.tail = FALSE)
> pv
[1] 0.0004959339
```