A fitted bglm object estimated on bglm_data.
Used in package examples and tests so that print, summary,
and plot examples run in well under 5 seconds without re-running
the MCMC sampler.
Format
An object of class bglm as returned by bglm.
See the Value section of bglm for a full
description of the list components. Key settings:
n_burn = 10000, n_it = 20000, n_chain = 2,
return_diagnostics = TRUE, return_samples = TRUE.
Details
Generated with set.seed(2024).
See data-raw/generate_examples.R for the full reproducible script.
Examples
print(bglm_fit)
#>
#> Bayesian Multivariate Logistic Regression
#> ==========================================
#>
#> Group mean y1 mean y2
#> placebo 0.517 0.501
#> drug 0.660 0.689
#> n(placebo) = 100 n(drug) = 100
#>
#> Posterior probability P(drug > placebo) [All rule]: 0.979
#> Marginalization: Empirical over [-Inf, Inf]
#>
#> Use summary() for regression coefficients, priors and MCMC diagnostics.
#>
summary(bglm_fit)
#>
#> Bayesian Multivariate Logistic Regression
#> ==========================================
#>
#> Group Estimates:
#> Group mean y1 sd y1 95% CI y1 mean y2 sd y2 95% CI y2
#> placebo 0.517 0.049 [0.421, 0.612] 0.501 0.049 [0.405, 0.598]
#> drug 0.660 0.047 [0.566, 0.749] 0.689 0.045 [0.597, 0.775]
#>
#> n(placebo) = 100 n(drug) = 100
#>
#> Treatment Effect:
#> Delta mean (y1, y2): 0.143, 0.187
#> Delta SE (y1, y2): 0, 0
#> 95% CI delta: y1 [0.008, 0.275] y2 [0.054, 0.316]
#> Posterior probability P(drug > placebo): 0.979
#>
#> Regression Coefficients (mean [SD]):
#> b11 b10 b01
#> Intercept 0.233 [1.247] 1.863 [1.184] 0.073 [1.248]
#> group 0.065 [1.734] -0.192 [1.758] 0.782 [1.768]
#> age -0.003 [0.025] -0.035 [0.024] 0.000 [0.025]
#> group:age 0.034 [0.035] 0.028 [0.036] 0.011 [0.036]
#>
#> Prior Specification (regression coefficients):
#> Mean:
#> Intercept group age group:age
#> 1 0 0 0 0
#> Variance (diagonal of inverse precision):
#> Intercept group age group:age
#> 1 10 10 10 10
#>
#> Marginalization:
#> Method: Empirical
#> (Sub)population: [-Inf, Inf]
#> Decision rule: All
#>
#> MCMC Diagnostics (regression coefficients):
#> Multivariate PSRF (MPSRF): 1.0028
#> Parameter ESS Rhat
#> b11_Intercept[1] 6662.3 1.0000
#> b11_group[2] 6063.1 1.0007
#> b11_age[3] 6839.7 1.0000
#> b11_group_age[4] 4799.1 1.0008
#> b10_Intercept[1] 6756.0 1.0001
#> b10_group[2] 5993.6 1.0010
#> b10_age[3] 6830.4 0.9999
#> b10_group_age[4] 4613.8 1.0010
#> b01_Intercept[1] 7002.8 1.0002
#> b01_group[2] 6039.8 1.0020
#> b01_age[3] 7194.7 1.0002
#> b01_group_age[4] 4709.6 1.0023
#>