Category Specific Predictors in brms Models
Details
For detailed documentation see help(brmsformula)
as well as vignette("brms_overview").
This function is almost solely useful when called in formulas passed to the brms package.
Examples
# \dontrun{
fit <- brm(rating ~ period + carry + cs(treat),
data = inhaler, family = sratio("cloglog"),
prior = set_prior("normal(0,5)"), chains = 2)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000266 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.66 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: 2.707 seconds (Warm-up)
#> Chain 1: 2.244 seconds (Sampling)
#> Chain 1: 4.951 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000231 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.31 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: 2.594 seconds (Warm-up)
#> Chain 2: 2.138 seconds (Sampling)
#> Chain 2: 4.732 seconds (Total)
#> Chain 2:
summary(fit)
#> Family: sratio
#> Links: mu = cloglog
#> Formula: rating ~ period + carry + cs(treat)
#> Data: inhaler (Number of observations: 572)
#> Draws: 2 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 2000
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept[1] -0.02 0.06 -0.14 0.09 1.00 3010 1530
#> Intercept[2] 0.86 0.10 0.68 1.05 1.00 2292 1632
#> Intercept[3] -0.18 0.41 -1.10 0.55 1.00 1263 1005
#> period 0.12 0.09 -0.08 0.30 1.00 2901 1265
#> carry -0.08 0.09 -0.26 0.09 1.00 1705 1546
#> treat[1] -0.54 0.15 -0.81 -0.25 1.00 1751 1631
#> treat[2] -0.38 0.22 -0.82 0.05 1.00 2071 1624
#> treat[3] 0.83 0.82 -0.63 2.65 1.00 1179 1057
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> disc 1.00 0.00 1.00 1.00 NA NA NA
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
plot(fit, ask = FALSE)
# }