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 2.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.26 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(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) 11.02 1.10 9.16 13.40 1.01 686 1650
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.22 2.11 4.10 12.36 1.01 400 864
#> Trt1 -0.80 3.12 -7.06 5.39 1.02 352 761
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.73 0.31 5.17 6.37 1.00 3124 3232
#>
#> 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 2.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.28 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(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) 11.03 1.12 9.08 13.48 1.01 818 1538
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.11 2.21 3.76 12.47 1.02 323 448
#> Trt1 -0.66 3.03 -6.33 5.38 1.01 284 539
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.73 0.30 5.18 6.38 1.00 3551 2922
#>
#> 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) 8.19 1.19 6.18 10.85 1.00 1037 1947
#> sd(Intercept:Trt1) 13.05 1.74 10.17 16.77 1.00 1084 1575
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.39 1.68 5.14 11.75 1.00 527 1038
#> Trt1 -1.22 2.84 -6.69 4.60 1.01 360 605
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.74 0.31 5.19 6.39 1.00 3100 2842
#>
#> 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 3.2e-05 seconds
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.443 seconds (Warm-up)
#> Chain 3: 0.336 seconds (Sampling)
#> Chain 3: 0.779 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 2.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.426 seconds (Warm-up)
#> Chain 4: 0.46 seconds (Sampling)
#> Chain 4: 0.886 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) 11.21 1.10 9.28 13.58 1.01 825 1643
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 8.10 2.29 3.63 12.81 1.04 138 440
#> Trt1 -0.66 2.95 -6.78 4.93 1.02 331 697
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 5.73 0.31 5.17 6.38 1.00 2523 2695
#>
#> 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).
# }