A fitted bglmm object estimated on bglmm_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 bglmm as returned by bglmm.
See the Value section of bglmm for a full
description of the list components. Key settings:
n_burn = 10000, n_it = 50000, n_thin = 10,
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(bglmm_fit)
#>
#> Bayesian Multilevel Multivariate Logistic Regression
#> ======================================================
#>
#> Group mean y1 mean y2
#> placebo 0.515 0.408
#> drug 0.617 0.592
#> J = 20 clusters n(placebo) = 150 n(drug) = 150
#>
#> Posterior probability P(drug > placebo) [All rule]: 0.977
#> Marginalization: Empirical over [-Inf, Inf]
#> MPSRF: fixed = 1.0218 random = 1.0247 variance = 1.0037
#>
#> Use summary() for full coefficient tables, priors and MCMC diagnostics.
#>
summary(bglmm_fit)
#>
#> Bayesian Multilevel Multivariate Logistic Regression
#> ======================================================
#>
#> Multilevel Structure: J = 20 clusters n(placebo) = 150 n(drug) = 150
#>
#> Group Estimates:
#> Group mean y1 sd y1 95% CI y1 mean y2 sd y2 95% CI y2
#> placebo 0.515 0.041 [0.435, 0.595] 0.408 0.037 [0.335, 0.481]
#> drug 0.617 0.037 [0.540, 0.687] 0.592 0.038 [0.515, 0.666]
#>
#> Treatment Effect:
#> Delta mean (y1, y2): 0.103, 0.184
#> Delta SE (y1, y2): 0.001, 0.001
#> 95% CI delta: y1 [0.002, 0.209] y2 [0.081, 0.296]
#> Posterior probability P(drug > placebo): 0.977
#>
#> Fixed Effects (mean [SD]):
#> b11 b10 b01
#> group 1.904 [1.555] -0.031 [1.480] 1.569 [1.588]
#> age 0.025 [0.021] -0.014 [0.019] -0.017 [0.022]
#> group_age -0.012 [0.030] 0.011 [0.029] -0.016 [0.031]
#>
#> Random Effects (population mean [SD]):
#> g11 g10 g01
#> Intercept -1.686 [1.126] 0.791 [0.977] 0.478 [1.109]
#>
#> Variance Components (posterior mean):
#> y1=1, y2=1 (b11):
#> Intercept
#> Intercept 0.324
#> y1=1, y2=0 (b10):
#> Intercept
#> Intercept 0.153
#> y1=0, y2=1 (b01):
#> Intercept
#> Intercept 0.682
#>
#> Prior Specification:
#> Fixed effects -- Normal prior:
#> Mean: 0, 0, 0
#> Variance: 10, 10, 10
#> Random effects -- Normal prior:
#> Mean: 0
#> Variance: 10
#> Covariance -- Inverse-Wishart: df = 1
#>
#> Marginalization:
#> Method: Empirical (Sub)population: [-Inf, Inf]
#> Decision rule: All
#>
#> MCMC Convergence Diagnostics:
#> Fixed effects -- MPSRF: 1.0218
#> Parameter ESS Rhat
#> b11_group[1] 400.7 1.0047
#> b11_age[2] 122.7 1.0044
#> b11_group_age[3] 523.4 1.0033
#> b10_group[1] 475.6 1.0037
#> b10_age[2] 91.7 1.0168
#> b10_group_age[3] 561.2 1.0029
#> b01_group[1] 632.2 1.0038
#> b01_age[2] 167.5 1.0140
#> b01_group_age[3] 729.9 1.0017
#>
#> Random effects -- MPSRF: 1.0247
#> Parameter ESS Rhat
#> g11_Intercept[1] 109.4 1.0051
#> g10_Intercept[1] 83.8 1.0202
#> g01_Intercept[1] 157.4 1.0176
#>
#> Variance components -- MPSRF: 1.0037
#> Parameter ESS Rhat
#> g11_InterceptIntercept 2304.1 1.0005
#> g10_InterceptIntercept 2261.1 1.0056
#> g01_InterceptIntercept 2592.1 0.9996
#>