`log_lik.stanreg.Rd`

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, ...)

object | A fitted model object returned by one of the
rstanarm modeling functions. See |
---|---|

newdata | An optional data frame of new data (e.g. holdout data) to use
when evaluating the log-likelihood. See the description of |

offset | A vector of offsets. Only required if |

... | 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 |

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.

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 )#> #> SAMPLING FOR MODEL 'count' NOW (CHAIN 1). #> #> Gradient evaluation took 0.000116 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 1.16 seconds. #> Adjust your expectations accordingly! #> #> #> Iteration: 1 / 500 [ 0%] (Warmup) #> Iteration: 50 / 500 [ 10%] (Warmup) #> Iteration: 100 / 500 [ 20%] (Warmup) #> Iteration: 150 / 500 [ 30%] (Warmup) #> Iteration: 200 / 500 [ 40%] (Warmup) #> Iteration: 250 / 500 [ 50%] (Warmup) #> Iteration: 251 / 500 [ 50%] (Sampling) #> Iteration: 300 / 500 [ 60%] (Sampling) #> Iteration: 350 / 500 [ 70%] (Sampling) #> Iteration: 400 / 500 [ 80%] (Sampling) #> Iteration: 450 / 500 [ 90%] (Sampling) #> Iteration: 500 / 500 [100%] (Sampling) #> #> Elapsed Time: 0.211255 seconds (Warm-up) #> 0.106999 seconds (Sampling) #> 0.318254 seconds (Total) #> #> #> SAMPLING FOR MODEL 'count' NOW (CHAIN 2). #> #> Gradient evaluation took 4.5e-05 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds. #> Adjust your expectations accordingly! #> #> #> Iteration: 1 / 500 [ 0%] (Warmup) #> Iteration: 50 / 500 [ 10%] (Warmup) #> Iteration: 100 / 500 [ 20%] (Warmup) #> Iteration: 150 / 500 [ 30%] (Warmup) #> Iteration: 200 / 500 [ 40%] (Warmup) #> Iteration: 250 / 500 [ 50%] (Warmup) #> Iteration: 251 / 500 [ 50%] (Sampling) #> Iteration: 300 / 500 [ 60%] (Sampling) #> Iteration: 350 / 500 [ 70%] (Sampling) #> Iteration: 400 / 500 [ 80%] (Sampling) #> Iteration: 450 / 500 [ 90%] (Sampling) #> Iteration: 500 / 500 [100%] (Sampling) #> #> Elapsed Time: 0.140815 seconds (Warm-up) #> 0.135274 seconds (Sampling) #> 0.276089 seconds (Total) #> #> #> SAMPLING FOR MODEL 'count' NOW (CHAIN 3). #> #> Gradient evaluation took 4.4e-05 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds. #> Adjust your expectations accordingly! #> #> #> Iteration: 1 / 500 [ 0%] (Warmup) #> Iteration: 50 / 500 [ 10%] (Warmup) #> Iteration: 100 / 500 [ 20%] (Warmup) #> Iteration: 150 / 500 [ 30%] (Warmup) #> Iteration: 200 / 500 [ 40%] (Warmup) #> Iteration: 250 / 500 [ 50%] (Warmup) #> Iteration: 251 / 500 [ 50%] (Sampling) #> Iteration: 300 / 500 [ 60%] (Sampling) #> Iteration: 350 / 500 [ 70%] (Sampling) #> Iteration: 400 / 500 [ 80%] (Sampling) #> Iteration: 450 / 500 [ 90%] (Sampling) #> Iteration: 500 / 500 [100%] (Sampling) #> #> Elapsed Time: 0.148195 seconds (Warm-up) #> 0.117762 seconds (Sampling) #> 0.265957 seconds (Total) #> #> #> SAMPLING FOR MODEL 'count' NOW (CHAIN 4). #> #> Gradient evaluation took 5e-05 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds. #> Adjust your expectations accordingly! #> #> #> Iteration: 1 / 500 [ 0%] (Warmup) #> Iteration: 50 / 500 [ 10%] (Warmup) #> Iteration: 100 / 500 [ 20%] (Warmup) #> Iteration: 150 / 500 [ 30%] (Warmup) #> Iteration: 200 / 500 [ 40%] (Warmup) #> Iteration: 250 / 500 [ 50%] (Warmup) #> Iteration: 251 / 500 [ 50%] (Sampling) #> Iteration: 300 / 500 [ 60%] (Sampling) #> Iteration: 350 / 500 [ 70%] (Sampling) #> Iteration: 400 / 500 [ 80%] (Sampling) #> Iteration: 450 / 500 [ 90%] (Sampling) #> Iteration: 500 / 500 [100%] (Sampling) #> #> Elapsed Time: 0.18563 seconds (Warm-up) #> 0.11219 seconds (Sampling) #> 0.29782 seconds (Total) #>ll <- log_lik(fit) dim(ll)#> [1] 1000 262all.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,] -7.208039 -3.379939 #> [2,] -7.538332 -3.645910 #> [3,] -6.475576 -3.415374 #> [4,] -6.957932 -3.350174 #> [5,] -5.320412 -3.461726 #> [6,] -6.218462 -3.344113dim(ll2)#> [1] 1000 2all.equal(ncol(ll2), nrow(nd))#> [1] TRUE