Pointwise log-likelihood matrix — log

For models fit using MCMC only, the log_lik method 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, or in the case of the stanmvreg method (when called on stan_jm model objects) an $S$ by $Npat$ matrix where $Npat$ is the number of individuals.

# S3 method for stanreg
log_lik(object, newdata = NULL, offset = NULL, ...)

# S3 method for stanmvreg
log_lik(object, m = 1, newdata = NULL, ...)

# S3 method for stanjm
log_lik(object, newdataLong = NULL, newdataEvent = NULL, ...)

Arguments

object	A fitted model object returned by one of the rstanarm modeling functions. See `stanreg-objects`.
newdata	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`.
offset	A vector of offsets. Only required if `newdata` is specified and an `offset` was specified when fitting the model.
...	Currently ignored.
m	Integer specifying the number or name of the submodel
newdataLong, newdataEvent	Optional data frames containing new data (e.g. holdout data) to use when evaluating the log-likelihood for a model estimated using `stan_jm`. If the fitted model was a multivariate joint model (i.e. more than one longitudinal outcome), then `newdataLong` is allowed to be a list of data frames. If supplying new data, then `newdataEvent` should also include variables corresponding to the event time and event indicator as these are required for evaluating the log likelihood for the event submodel. For more details, see the description of `newdataLong` and `newdataEvent` for `posterior_survfit`.

Value

For the stanreg and stanmvreg methods an $S$ by $N$ matrix, where $S$ is the size of the posterior sample and $N$ is the number of data points. For the stanjm method an $S$ by $Npat$ matrix where $Npat$ is the number of individuals.

Examples

# \donttest{
 roaches$roach100 <- roaches$roach1 / 100
 fit <- stan_glm(
    y ~ roach100 + treatment + senior,
    offset = log(exposure2),
    data = roaches,
    family = poisson(link = "log"),
    prior = normal(0, 2.5),
    prior_intercept = normal(0, 10),
    iter = 500, # just to speed up example,
    refresh = 0
 )
 ll <- log_lik(fit)
 dim(ll)
#> [1] 1000  262
 all.equal(ncol(ll), nobs(fit))
#> [1] TRUE

 # using newdata argument
 nd <- roaches[1:2, ]
 nd$treatment[1:2] <- c(0, 1)
 ll2 <- log_lik(fit, newdata = nd, offset = c(0, 0))
 head(ll2)
#>              1         2
#> [1,] -7.183851 -3.419396
#> [2,] -6.728552 -3.369339
#> [3,] -6.913875 -3.497883
#> [4,] -6.433703 -3.516367
#> [5,] -7.500837 -3.342995
#> [6,] -7.810188 -4.052911
 dim(ll2)
#> [1] 1000    2
 all.equal(ncol(ll2), nrow(nd))
#> [1] TRUE
# }