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 5.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.642 seconds (Warm-up)
#> Chain 1:                0.73 seconds (Sampling)
#> Chain 1:                1.372 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: 0.752 seconds (Warm-up)
#> Chain 2:                0.538 seconds (Sampling)
#> Chain 2:                1.29 seconds (Total)
#> Chain 2: 
#> Warning: There were 2 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   0.88644211 -0.017835660  0.013806951 -0.59013560 -0.03719786
#> zAge       -0.01783566  0.261236750 -0.008156782  0.07865400  0.09320789
#> zBase       0.01380695 -0.008156782  0.592407870 -0.05244389 -0.61251781
#> Trt1       -0.59013560  0.078654001 -0.052443885  1.84160706  0.07998178
#> zBase:Trt1 -0.03719786  0.093207885 -0.612517806  0.07998178  1.12548974
# }