Extract the population-level ('fixed') effects
from a brmsfit object.
Usage
# S3 method for class 'brmsfit'
fixef(
object,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
pars = NULL,
...
)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. IfTRUE, the median and the median absolute deviation (MAD) are applied instead. Only used ifsummaryisTRUE.- probs
The percentiles to be computed by the
quantilefunction. Only used ifsummaryisTRUE.- pars
Optional names of coefficients to extract. By default, all coefficients are extracted.
- ...
Currently ignored.
Value
If summary is TRUE, a matrix returned
by posterior_summary for the population-level effects.
If summary is FALSE, a matrix with one row per
posterior draw and one column per population-level effect.
Examples
# \dontrun{
fit <- brm(time | cens(censored) ~ age + sex + disease,
data = kidney, family = "exponential")
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.13 seconds (Warm-up)
#> Chain 1: 0.081 seconds (Sampling)
#> Chain 1: 0.211 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.124 seconds (Warm-up)
#> Chain 2: 0.078 seconds (Sampling)
#> Chain 2: 0.202 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.105 seconds (Warm-up)
#> Chain 3: 0.087 seconds (Sampling)
#> Chain 3: 0.192 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.16 seconds (Warm-up)
#> Chain 4: 0.076 seconds (Sampling)
#> Chain 4: 0.236 seconds (Total)
#> Chain 4:
fixef(fit)
#> Estimate Est.Error Q2.5 Q97.5
#> Intercept 3.789685121 0.49111114 2.85305983 4.75064024
#> age -0.002749282 0.01138276 -0.02471688 0.01936449
#> sexfemale 1.594715610 0.33030765 0.92681530 2.20296404
#> diseaseGN -0.031891797 0.40672651 -0.78904274 0.77906522
#> diseaseAN -0.508046556 0.39750593 -1.27730246 0.26940170
#> diseasePKD 1.372240120 0.58078558 0.24973130 2.52323199
# extract only some coefficients
fixef(fit, pars = c("age", "sex"))
#> Estimate Est.Error Q2.5 Q97.5
#> age -0.002749282 0.01138276 -0.02471688 0.01936449
# }