"coef"(object, ...)
"confint"(object, parm, level = 0.95, ...)
"fitted"(object, ...)
"log_lik"(object, newdata = NULL, ...)
"nobs"(object, ...)
"residuals"(object, ...)
"se"(object, ...)
"update"(object, formula., ..., evaluate = TRUE)
"vcov"(object, correlation = FALSE, ...)
"fixef"(object, ...)
"ngrps"(object, ...)
"ranef"(object, ...)
"sigma"(object, ...)
"VarCorr"(x, sigma = 1, ...)stanreg-objects.update method. See
update.confint, an optional character vector of parameter
names.confint, a scalar between $0$ and $1$
indicating the confidence level to use.log_lik, an optional data frame of new data (e.g.
holdout data) to use when evaluating the log-likelihood. See the
description of newdata for posterior_predict.update.vcov, if FALSE (the default) the
covariance matrix is returned. If TRUE, the correlation matrix is
returned instead.VarCorr).confintconfint.default. If algorithm is
"sampling", "meanfield", or "fullrank", the
posterior_interval function should be used to compute Bayesian
uncertainty intervals.
log_liklog_lik
function returns the $S$ by $N$ pointwise log-likelihood matrix,
where $S$ is the size of the posterior sample and $N$ is the number
of data points. Note: we use log_lik rather than defining a
logLik method because (in addition to the conceptual
difference) the documentation for logLik states that the return value
will be a single number, whereas we return a matrix.
residuals"response" (not "deviance"
residuals or any other type). However, in the case of stan_polr
with more than two response categories, the residuals are the difference
between the latent utility and its linear predictor.
coefprint.stanreg for more details.
sese function returns standard errors based on
mad. See the Uncertainty estimates section in
print.stanreg for more details.
as.matrix.stanreg, plot.stanreg,
predict.stanreg, print.stanreg, and
summary.stanreg.posterior_interval and posterior_predict for
alternatives to confint and predict for models fit using MCMC
or variational approximation.