log_lik.stanreg: Pointwise log-likelihood matrix

In csetraynor/aeim: What the Package Does (One Line, Title Case)

Description

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.

Usage

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

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

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

Arguments

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.
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

 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 # to speed up example
 )
 ll <- log_lik(fit)
 dim(ll)
 all.equal(ncol(ll), nobs(fit))

 # 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)
 dim(ll2)
 all.equal(ncol(ll2), nrow(nd))

csetraynor/aeim documentation built on May 15, 2019, 6:25 p.m.