Function used to set up a basic grouping term in brms.
The function does not evaluate its arguments –
it exists purely to help set up a model with grouping terms.
gr is called implicitly inside the package
and there is usually no need to call it directly.
Arguments
- ...
One or more terms containing grouping factors.
- by
An optional factor variable, specifying sub-populations of the groups. For each level of the
byvariable, a separate variance-covariance matrix will be fitted. Levels of the grouping factor must be nested in levels of thebyvariable.- cor
Logical. If
TRUE(the default), group-level terms will be modelled as correlated.- id
Optional character string. All group-level terms across the model with the same
idwill be modeled as correlated (ifcorisTRUE). Seebrmsformulafor more details.- pw
Optional numeric variable specifying prior weights. They weight the contribution of each group to the log-prior of the group-level coefficients. Should have one distinct value for each level of the grouping variable.
- cov
An optional matrix which is proportional to the within-group covariance matrix of the group-level effects. All levels of the grouping factor should appear as rownames of the corresponding matrix. This argument can be used, among others, to model pedigrees and phylogenetic effects. See
vignette("brms_phylogenetics")for more details. By default, levels of the same grouping factor are modeled as independent of each other.- dist
Name of the distribution of the group-level effects. Currently
"gaussian"is the only option.
Examples
# \dontrun{
# model using basic lme4-style formula
fit1 <- brm(count ~ Trt + (1|patient), data = epilepsy)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 2:
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#> Chain 4:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
summary(fit1)
#> Family: gaussian
#> Links: mu = identity
#> Formula: count ~ Trt + (1 | patient)
#> Data: epilepsy (Number of observations: 236)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 59)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 10.98 1.14 8.96 13.34 1.00 937 1995
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.07 2.08 3.95 12.15 1.01 404 892
#> Trt1 -0.19 3.00 -6.10 5.61 1.01 328 650
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.73 0.31 5.14 6.36 1.00 3559 2966
#>
#> 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).
# equivalent model using 'gr' which is called anyway internally
fit2 <- brm(count ~ Trt + (1|gr(patient)), data = epilepsy)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
summary(fit2)
#> Family: gaussian
#> Links: mu = identity
#> Formula: count ~ Trt + (1 | gr(patient))
#> Data: epilepsy (Number of observations: 236)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 59)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 11.00 1.08 9.09 13.26 1.00 824 1023
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.11 2.10 4.06 12.24 1.00 370 800
#> Trt1 -0.48 3.07 -6.37 5.59 1.01 326 549
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.73 0.31 5.15 6.36 1.00 2861 2949
#>
#> 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).
# include Trt as a by variable
fit3 <- brm(count ~ Trt + (1|gr(patient, by = Trt)), data = epilepsy)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
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#> Chain 4:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
summary(fit3)
#> Family: gaussian
#> Links: mu = identity
#> Formula: count ~ Trt + (1 | gr(patient, by = Trt))
#> Data: epilepsy (Number of observations: 236)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 59)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept:Trt0) 8.24 1.26 6.16 10.98 1.00 786 1624
#> sd(Intercept:Trt1) 12.96 1.77 10.03 16.98 1.00 816 1561
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.31 1.68 4.94 11.58 1.02 436 956
#> Trt1 -1.00 2.91 -6.65 4.78 1.01 305 434
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.74 0.31 5.17 6.39 1.00 3357 2863
#>
#> 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).
# include a group-level weight variable
epilepsy[['patient_samp_wgt']] <- c(1, rep(c(0.9, 1.1), each = 29))
fit4 <- brm(count ~ Trt + (1|gr(patient, pw = patient_samp_wgt)),
data = epilepsy)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 3.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
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#> Chain 3: Elapsed Time: 0.405 seconds (Warm-up)
#> Chain 3: 0.285 seconds (Sampling)
#> Chain 3: 0.69 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.364 seconds (Warm-up)
#> Chain 4: 0.288 seconds (Sampling)
#> Chain 4: 0.652 seconds (Total)
#> Chain 4:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
summary(fit4)
#> Family: gaussian
#> Links: mu = identity
#> Formula: count ~ Trt + (1 | gr(patient, pw = patient_samp_wgt))
#> Data: epilepsy (Number of observations: 236)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 59)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 11.31 1.11 9.36 13.73 1.00 961 1404
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 7.91 2.30 3.39 12.40 1.01 297 738
#> Trt1 -0.53 3.19 -6.99 5.90 1.01 282 552
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.73 0.31 5.16 6.41 1.00 3153 2762
#>
#> 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).
# }