Covariance and Correlation Matrix of Population-Level Effects

Get a point estimate of the covariance or correlation matrix of population-level parameters

Usage

# S3 method for class 'brmsfit'
vcov(object, correlation = FALSE, pars = NULL, ...)

Arguments

object: An object of class brmsfit.
correlation: Logical; if FALSE (the default), compute the covariance matrix, if TRUE, compute the correlation matrix.
pars: Optional names of coefficients to extract. By default, all coefficients are extracted.
...: Currently ignored.

Value

covariance or correlation matrix of population-level parameters

Details

Estimates are obtained by calculating the maximum likelihood covariances (correlations) of the posterior draws.

Examples

# \dontrun{
fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit),
           data = epilepsy, family = gaussian(), chains = 2)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.51 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.695 seconds (Warm-up)
#> Chain 1:                0.515 seconds (Sampling)
#> Chain 1:                1.21 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.743 seconds (Warm-up)
#> Chain 2:                0.404 seconds (Sampling)
#> Chain 2:                1.147 seconds (Total)
#> Chain 2: 
#> Warning: There were 14 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
vcov(fit)
#>              Intercept        zAge       zBase        Trt1  zBase:Trt1
#> Intercept   1.15032828 -0.04942021 -0.01158160 -0.80612017  0.01497913
#> zAge       -0.04942021  0.28527683 -0.01574603  0.12128221  0.11267743
#> zBase      -0.01158160 -0.01574603  0.54910225  0.05211377 -0.53902232
#> Trt1       -0.80612017  0.12128221  0.05211377  2.23626581  0.01782143
#> zBase:Trt1  0.01497913  0.11267743 -0.53902232  0.01782143  0.99071009
# }