Summary method for glmMixBayes models
Usage
# S3 method for class 'glmMixBayes'
summary(object, ...)Examples
# \donttest{
data(lifem)
# lifem data preprocessing
# For computational efficiency in the example, we work with a subset of the lifem data.
lifem <- lifem[order(-(lifem$commf + lifem$comml)), ]
lifem_small <- rbind(
head(subset(lifem, hndlnk == 1), 100),
head(subset(lifem, hndlnk == 0), 20)
)
x <- cbind(1, poly(lifem_small$unit_yob, 3, raw = TRUE))
y <- lifem_small$age_at_death
adj <- adjMixBayes(
linked.data = lifem_small,
priors = list(theta = "beta(2, 2)")
)
fit <- plglm(
age_at_death ~ poly(unit_yob, 3, raw = TRUE),
family = "gaussian",
adjustment = adj,
control = list(
iterations = 200,
burnin.iterations = 100,
seed = 123
)
)
#>
#> SAMPLING FOR MODEL 'glmMixBayes_gaussian' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 5.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1: three stages of adaptation as currently configured.
#> Chain 1: Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1: the given number of warmup iterations:
#> Chain 1: init_buffer = 15
#> Chain 1: adapt_window = 75
#> Chain 1: term_buffer = 10
#> Chain 1:
#> Chain 1: Iteration: 1 / 200 [ 0%] (Warmup)
#> Chain 1: Iteration: 20 / 200 [ 10%] (Warmup)
#> Chain 1: Iteration: 40 / 200 [ 20%] (Warmup)
#> Chain 1: Iteration: 60 / 200 [ 30%] (Warmup)
#> Chain 1: Iteration: 80 / 200 [ 40%] (Warmup)
#> Chain 1: Iteration: 100 / 200 [ 50%] (Warmup)
#> Chain 1: Iteration: 101 / 200 [ 50%] (Sampling)
#> Chain 1: Iteration: 120 / 200 [ 60%] (Sampling)
#> Chain 1: Iteration: 140 / 200 [ 70%] (Sampling)
#> Chain 1: Iteration: 160 / 200 [ 80%] (Sampling)
#> Chain 1: Iteration: 180 / 200 [ 90%] (Sampling)
#> Chain 1: Iteration: 200 / 200 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.768 seconds (Warm-up)
#> Chain 1: 0.92 seconds (Sampling)
#> Chain 1: 1.688 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#>
#> ......................................................................................
#> . Method Time (sec) Status .
#> ......................................................................................
#> . ECR-ITERATIVE-1 0.079 Converged (2 iterations) .
#> ......................................................................................
#>
#> Relabelling all methods according to method ECR-ITERATIVE-1 ... done!
#> Retrieve the 1 permutation arrays by typing:
#> [...]$permutations$"ECR-ITERATIVE-1"
#> Retrieve the 1 best clusterings: [...]$clusters
#> Retrieve the 1 CPU times: [...]$timings
#> Retrieve the 1 X 1 similarity matrix: [...]$similarity
#> Label switching finished. Total time: 0.1 seconds.
summary(fit)
#> Call:
#> plglm(formula = age_at_death ~ poly(unit_yob, 3, raw = TRUE),
#> family = "gaussian", adjustment = adj, control = list(iterations = 200,
#> burnin.iterations = 100, seed = 123))
#>
#> Family:
#> gaussian
#>
#> (Component 1 = Correct-match):
#> Outcome Model Coefficients:
#> Estimates Std. Error 2.5 % 97.5 %
#> (Intercept) 55.7967 3.6257 49.5028 62.42
#> poly(unit_yob, 3, raw = TRUE)1 7.1598 4.4356 -0.9492 16.40
#> poly(unit_yob, 3, raw = TRUE)2 8.1611 4.0579 -0.7745 15.26
#> poly(unit_yob, 3, raw = TRUE)3 9.1663 4.5267 1.1864 17.96
#>
#> Dispersion:
#> Estimate Std. Error
#> 199.9 70.5
#>
#> (Component 2 = Incorrect-match):
#> Outcome Model Coefficients:
#> Estimates Std. Error 2.5 % 97.5 %
#> (Intercept) 29.0588 14.3733 -1.3359 44.579
#> poly(unit_yob, 3, raw = TRUE)1 -0.4999 4.2417 -9.1588 7.196
#> poly(unit_yob, 3, raw = TRUE)2 -1.0870 5.1031 -10.9310 8.499
#> poly(unit_yob, 3, raw = TRUE)3 -2.7743 4.9410 -11.6794 7.240
#>
#> Dispersion:
#> Estimate Std. Error
#> 156.4 91.1
#>
# }