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Extract Variance and Correlation Components

Source: R/brmsfit-methods.R
VarCorr.brmsfit.Rd

This function calculates the estimated standard deviations, correlations and covariances of the group-level terms in a multilevel model of class brmsfit. For linear models, the residual standard deviations, correlations and covariances are also returned.

Usage

# S3 method for class 'brmsfit'
VarCorr(
  x,
  sigma = 1,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

Arguments

x

An object of class brmsfit.

sigma

Ignored (included for compatibility with VarCorr).

summary

Should summary statistics be returned instead of the raw values? Default is TRUE.

robust

If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.

probs

The percentiles to be computed by the quantile function. Only used if summary is TRUE.

...

Currently ignored.

Value

A list of lists (one per grouping factor), each with three elements: a matrix containing the standard deviations, an array containing the correlation matrix, and an array containing the covariance matrix with variances on the diagonal.

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 5.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.73 seconds (Warm-up)
#> Chain 1:                0.448 seconds (Sampling)
#> Chain 1:                1.178 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: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.764 seconds (Warm-up)
#> Chain 2:                0.444 seconds (Sampling)
#> Chain 2:                1.208 seconds (Total)
#> Chain 2: 
#> Warning: There were 44 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
VarCorr(fit)
#> $visit
#> $visit$sd
#>           Estimate Est.Error       Q2.5    Q97.5
#> Intercept 1.140915  1.094191 0.03764509 4.261134
#> Trt1      1.345855  1.266183 0.04438976 4.851442
#> 
#> $visit$cor
#> , , Intercept
#> 
#>             Estimate Est.Error       Q2.5     Q97.5
#> Intercept  1.0000000 0.0000000  1.0000000 1.0000000
#> Trt1      -0.1217731 0.6009979 -0.9743096 0.9333658
#> 
#> , , Trt1
#> 
#>             Estimate Est.Error       Q2.5     Q97.5
#> Intercept -0.1217731 0.6009979 -0.9743096 0.9333658
#> Trt1       1.0000000 0.0000000  1.0000000 1.0000000
#> 
#> 
#> $visit$cov
#> , , Intercept
#> 
#>             Estimate Est.Error          Q2.5     Q97.5
#> Intercept  2.4983440  4.938207   0.001417192 18.157265
#> Trt1      -0.7692375  3.509571 -11.125638255  2.243856
#> 
#> , , Trt1
#> 
#>             Estimate Est.Error          Q2.5     Q97.5
#> Intercept -0.7692375  3.509571 -11.125638255  2.243856
#> Trt1       3.4137435  7.023089   0.001970522 23.536494
#> 
#> 
#> 
#> $residual__
#> $residual__$sd
#>  Estimate Est.Error     Q2.5    Q97.5
#>  7.637655 0.3460858 7.007586 8.346337
#> 
#> 
# }