Extract Model Coefficients — coef.brmsfit • brms

Extract model coefficients, which are the sum of population-level effects and corresponding group-level effects

Usage

# S3 method for class 'brmsfit'
coef(object, summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)

Arguments

object: An object of class brmsfit.
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.
...: Further arguments passed to fixef.brmsfit and ranef.brmsfit.

Value

A list of 3D arrays (one per grouping factor). If summary is TRUE, the 1st dimension contains the factor levels, the 2nd dimension contains the summary statistics (see posterior_summary), and the 3rd dimension contains the group-level effects. If summary is FALSE, the 1st dimension contains the posterior draws, the 2nd dimension contains the factor levels, and the 3rd dimension contains the group-level effects.

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 3.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.38 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: 0.714 seconds (Warm-up)
#> Chain 1:                0.618 seconds (Sampling)
#> Chain 1:                1.332 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.3 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: 0.657 seconds (Warm-up)
#> Chain 2:                0.476 seconds (Sampling)
#> Chain 2:                1.133 seconds (Total)
#> Chain 2: 
#> Warning: There were 3 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
## extract population and group-level coefficients separately
fixef(fit)
#>              Estimate Est.Error       Q2.5     Q97.5
#> Intercept   8.4080560 0.9655297  6.3413222 10.130798
#> zAge        1.6382347 0.5237024  0.6278908  2.639194
#> zBase       7.0265912 0.7639634  5.4769891  8.482213
#> Trt1       -0.7099839 1.3194749 -3.3396086  1.723281
#> zBase:Trt1  4.7828378 1.0198244  2.8581711  6.749996
ranef(fit)
#> $visit
#> , , Intercept
#> 
#>      Estimate Est.Error      Q2.5    Q97.5
#> 1  0.34135610 0.9382166 -1.226836 2.772130
#> 2  0.09968401 0.8782179 -1.696261 2.183759
#> 3  0.14431020 0.9119715 -1.480372 2.288115
#> 4 -0.20093860 0.9044591 -2.163351 1.678969
#> 
#> , , Trt1
#> 
#>     Estimate Est.Error      Q2.5    Q97.5
#> 1  0.2068644  1.129104 -1.896670 2.884655
#> 2  0.2626932  1.181336 -2.087958 3.195214
#> 3  0.1311467  1.152926 -2.244617 2.814154
#> 4 -0.2176979  1.209247 -3.083865 2.291449
#> 
#> 
## extract combined coefficients
coef(fit)
#> $visit
#> , , Intercept
#> 
#>   Estimate Est.Error     Q2.5     Q97.5
#> 1 8.749412 0.9369108 7.026388 10.724609
#> 2 8.507740 0.9026341 6.773999 10.419782
#> 3 8.552366 0.9153059 6.673310 10.425008
#> 4 8.207117 0.9397999 6.178842  9.968766
#> 
#> , , Trt1
#> 
#>     Estimate Est.Error      Q2.5    Q97.5
#> 1 -0.5031195  1.241760 -2.803109 2.036540
#> 2 -0.4472907  1.290644 -2.880706 2.143415
#> 3 -0.5788371  1.244170 -2.903376 1.944472
#> 4 -0.9276818  1.319342 -3.659297 1.526786
#> 
#> , , zAge
#> 
#>   Estimate Est.Error      Q2.5    Q97.5
#> 1 1.638235 0.5237024 0.6278908 2.639194
#> 2 1.638235 0.5237024 0.6278908 2.639194
#> 3 1.638235 0.5237024 0.6278908 2.639194
#> 4 1.638235 0.5237024 0.6278908 2.639194
#> 
#> , , zBase
#> 
#>   Estimate Est.Error     Q2.5    Q97.5
#> 1 7.026591 0.7639634 5.476989 8.482213
#> 2 7.026591 0.7639634 5.476989 8.482213
#> 3 7.026591 0.7639634 5.476989 8.482213
#> 4 7.026591 0.7639634 5.476989 8.482213
#> 
#> , , zBase:Trt1
#> 
#>   Estimate Est.Error     Q2.5    Q97.5
#> 1 4.782838  1.019824 2.858171 6.749996
#> 2 4.782838  1.019824 2.858171 6.749996
#> 3 4.782838  1.019824 2.858171 6.749996
#> 4 4.782838  1.019824 2.858171 6.749996
#> 
#> 
# }