A model for use in rstanarm examples.
example("example_model") will run the model in the
Examples section, below, and the resulting stanreg object will then be
available in the global environment. The
arguments are specified to make this example be small in size. In practice,
we recommend that they be left unspecified in order to use the default
values (4 and 2000 respectively) or increased if there are convergence
cores argument is optional and on a multicore system,
the user may well want to set that equal to the number of chains being
cbpp for a description of the data.
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Loading required package: Rcpp rstanarm (Version 2.15.3, packaged: 2017-04-29 06:18:44 UTC) - Do not expect the default priors to remain the same in future rstanarm versions. Thus, R scripts should specify priors explicitly, even if they are just the defaults. - For execution on a local, multicore CPU with excess RAM we recommend calling options(mc.cores = parallel::detectCores()) trying deprecated constructor; please alert package maintainer Gradient evaluation took 0.000107 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.07 seconds. Adjust your expectations accordingly! Elapsed Time: 0.485015 seconds (Warm-up) 0.198294 seconds (Sampling) 0.683309 seconds (Total) Gradient evaluation took 3.9e-05 seconds 1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds. Adjust your expectations accordingly! Elapsed Time: 0.547961 seconds (Warm-up) 0.227128 seconds (Sampling) 0.775089 seconds (Total) stan_glmer family: binomial [logit] formula: cbind(incidence, size - incidence) ~ size + period + (1 | herd) ------ Estimates: Median MAD_SD (Intercept) -1.6 0.6 size 0.0 0.0 period2 -1.0 0.3 period3 -1.1 0.3 period4 -1.6 0.4 Error terms: Groups Name Std.Dev. herd (Intercept) 0.78 Num. levels: herd 15 Sample avg. posterior predictive distribution of y (X = xbar): Median MAD_SD mean_PPD 1.8 0.2 ------ For info on the priors used see help('prior_summary.stanreg').
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