Nothing

```
`print.profile.brglm` <-
function (x, ...)
{
cat("'level' was set to", attr(x, "level"), "\n")
cat("Methods that apply:\n")
cat("'confint' 'plot' 'pairs'\n")
}
`profile.brglm` <-
function (fitted, gridsize = 10, stdn = 5, stepsize = 0.5, level = 0.95,
which = 1:length(coef(fitted)), verbose = TRUE, zero.bound = 1e-08,
scale = FALSE, ...)
{
notNA <- !is.na(fitted$coefficients)
if (level <= 0 | level >= 1)
stop("invalid 'level'.")
if (fitted$method == "glm.fit") {
if (verbose)
cat("Profiling the ordinary deviance for the supplied fit...\n")
res1 <- profileModel(fitted, gridsize = gridsize, stdn = stdn,
stepsize = stepsize, grid.bounds = NULL, quantile = qchisq(level,
1), objective = "ordinaryDeviance", agreement = TRUE,
verbose = FALSE, trace.prelim = FALSE, which = which,
profTraces = TRUE, zero.bound = zero.bound, scale = scale)
res2 <- NULL
}
else {
fitted1 <- update(fitted, method = "glm.fit")
Xmat <- model.matrix(fitted)[, notNA]
if (verbose)
cat("Profiling the ordinary deviance for the corresponding ML fit...\n")
res1 <- profileModel(fitted1, gridsize = gridsize, stdn = stdn,
stepsize = stepsize, grid.bounds = NULL, quantile = qchisq(level,
1), objective = "ordinaryDeviance", agreement = TRUE,
verbose = FALSE, trace.prelim = FALSE, which = which,
profTraces = TRUE, zero.bound = zero.bound, scale = scale)
if (fitted$pl | all(fitted$family$link == "logit")) {
if (verbose)
cat("Profiling the penalized deviance for the supplied fit...\n")
res2 <- profileModel(fitted, gridsize = gridsize,
stdn = stdn, stepsize = stepsize, grid.bounds = NULL,
quantile = qchisq(level, 1), objective = "penalizedDeviance",
agreement = TRUE, verbose = FALSE, trace.prelim = FALSE,
which = which, profTraces = TRUE, zero.bound = zero.bound,
scale = scale,
X = model.matrix(fitted)[,!is.na(fitted$coefficients)])
}
else {
if (verbose)
cat("Profiling the modified score statistic for the supplied fit...\n")
res2 <- profileModel(fitted, gridsize = gridsize,
stdn = stdn, stepsize = stepsize, grid.bounds = NULL,
quantile = qchisq(level, 1), objective = "modifiedScoreStatistic",
agreement = TRUE, verbose = FALSE, trace.prelim = FALSE,
which = which, profTraces = TRUE, zero.bound = zero.bound,
scale = scale,
X = model.matrix(fitted)[,!is.na(fitted$coefficients)])
}
}
res <- list(profilesML = res1, profilesBR = res2)
attr(res, "level") <- level
class(res) <- "profile.brglm"
res
}
```

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