rstanarm: log_lik.stanreg – R documentation

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log_lik.stanreg

Pointwise log-likelihood matrix

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

`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

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)
 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))

rstanarm

Bayesian Applied Regression Modeling via Stan

v2.21.1

GPL (>= 3)

Authors

Jonah Gabry [aut], Imad Ali [ctb], Sam Brilleman [ctb], Jacqueline Buros Novik [ctb] (R/stan_jm.R), AstraZeneca [ctb] (R/stan_jm.R), Trustees of Columbia University [cph], Simon Wood [cph] (R/stan_gamm4.R), R Core Deveopment Team [cph] (R/stan_aov.R), Douglas Bates [cph] (R/pp_data.R), Martin Maechler [cph] (R/pp_data.R), Ben Bolker [cph] (R/pp_data.R), Steve Walker [cph] (R/pp_data.R), Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R), William Venables [cph] (R/stan_polr.R), Paul-Christian Burkner [cph] (R/misc.R), Ben Goodrich [cre, aut]

Initial release

2020-07-20