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 6.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.64 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.666 seconds (Warm-up)
#> Chain 1:                0.585 seconds (Sampling)
#> Chain 1:                1.251 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.654 seconds (Warm-up)
#> Chain 2:                0.396 seconds (Sampling)
#> Chain 2:                1.05 seconds (Total)
#> Chain 2: 
#> Warning: There were 7 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
#> 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
vcov(fit)
#>              Intercept         zAge       zBase         Trt1  zBase:Trt1
#> Intercept   0.93935805 -0.011653242 -0.02894459 -0.536356833  0.03132944
#> zAge       -0.01165324  0.259764792 -0.03222474  0.005324015  0.13069852
#> zBase      -0.02894459 -0.032224743  0.61246274 -0.039686536 -0.64777809
#> Trt1       -0.53635683  0.005324015 -0.03968654  1.914384150  0.04634288
#> zBase:Trt1  0.03132944  0.130698518 -0.64777809  0.046342881  1.18649881
# }