brm_cat <- brm(
RT ~ 1 + PhonLev + IsWord,
data = mald,
family = lognormal,
cores = 4,
seed = 9812,
file = "data/cache/brm_cat.rds"
)
brm_cat
Family: lognormal
Links: mu = identity; sigma = identity
Formula: RT ~ 1 + PhonLev + IsWord
Data: mald (Number of observations: 3000)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 6.66 0.03 6.61 6.72 1.00 6091 3254
PhonLev 0.03 0.00 0.02 0.03 1.00 6579 3225
IsWordFALSE 0.12 0.01 0.10 0.14 1.00 4237 2689
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 0.28 0.00 0.27 0.29 1.00 3265 2928
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).