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#' @templateVar class rlm
#' @template title_desc_glance
#'
#' @param x An `rlm` object returned by [MASS::rlm()].
#' @template param_unused_dots
#'
#' @evalRd return_glance(
#' "sigma",
#' "converged",
#' "logLik",
#' "AIC",
#' "BIC",
#' "deviance",
#' "nobs"
#' )
#'
#' @examplesIf rlang::is_installed("MASS")
#'
#' # load libraries for models and data
#' library(MASS)
#'
#' # fit model
#' r <- rlm(stack.loss ~ ., stackloss)
#'
#' # summarize model fit with tidiers
#' tidy(r)
#' augment(r)
#' glance(r)
#'
#' @export
#' @aliases rlm_tidiers
#' @family rlm tidiers
#' @seealso [glance()], [MASS::rlm()]
glance.rlm <- function(x, ...) {
s <- summary(x)
tibble(
sigma = s$sigma,
converged = x$converged,
logLik = stats::logLik(x),
AIC = stats::AIC(x),
BIC = stats::BIC(x),
deviance = stats::deviance(x),
nobs = stats::nobs(x)
)
}
# confint.lm gets called on rlm objects. should use the default instead.
#' @export
confint.rlm <- confint.default
#' @templateVar class rlm
#' @template title_desc_tidy
#'
#' @param x An `rlm` object returned by [MASS::rlm()].
#' @template param_confint
#' @template param_unused_dots
#'
#' @family rlm tidiers
#' @seealso [MASS::rlm()]
#' @export
#' @include stats-lm-tidiers.R
tidy.rlm <- function(x, conf.int = FALSE, conf.level = .95, ...) {
check_ellipses("exponentiate", "tidy", "rlm", ...)
ret <- as_tibble(summary(x)$coefficients, rownames = "term")
colnames(ret) <- c("term", "estimate", "std.error", "statistic")
if (conf.int) {
ci <- broom_confint_terms(x, level = conf.level)
ret <- dplyr::left_join(ret, ci, by = "term")
}
ret
}
#' @templateVar class rlm
#' @template title_desc_augment
#'
#' @param x An `rlm` object returned by [MASS::rlm()].
#' @template param_data
#' @template param_newdata
#' @template param_se_fit
#' @template param_unused_dots
#'
#' @evalRd return_augment(".se.fit", ".hat", ".sigma")
#' @inherit glance.rlm examples
#'
#' @family rlm tidiers
#' @seealso [MASS::rlm()]
#' @export
augment.rlm <- function(x, data = model.frame(x), newdata = NULL,
se_fit = FALSE, ...) {
df <- augment_newdata(x, data, newdata, se_fit)
if (is.null(newdata)) {
tryCatch(
{
infl <- influence(x, do.coef = FALSE)
df <- add_hat_sigma_cols(df, x, infl)
},
error = data_error
)
}
df
}
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