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Multi-Membership Covariates

Source: R/formula-re.R
mmc.Rd

Specify covariates that vary over different levels of multi-membership grouping factors thus requiring special treatment. This function is almost solely useful, when called in combination with mm. Outside of multi-membership terms it will behave very much like cbind.

Usage

mmc(...)

Arguments

...

One or more terms containing covariates corresponding to the grouping levels specified in mm.

Value

A matrix with covariates as columns.

See also

mm

Examples

# \dontrun{
# simulate some data
dat <- data.frame(
  y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100),
  g1 = sample(1:10, 100, TRUE), g2 = sample(1:10, 100, TRUE)
)

# multi-membership model with level specific covariate values
dat$xc <- (dat$x1 + dat$x2) / 2
fit <- brm(y ~ xc + (1 + mmc(x1, x2) | mm(g1, g2)), data = dat)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.711 seconds (Warm-up)
#> Chain 1:                0.474 seconds (Sampling)
#> Chain 1:                1.185 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.75 seconds (Warm-up)
#> Chain 2:                0.479 seconds (Sampling)
#> Chain 2:                1.229 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 3.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.762 seconds (Warm-up)
#> Chain 3:                0.876 seconds (Sampling)
#> Chain 3:                1.638 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.724 seconds (Warm-up)
#> Chain 4:                0.582 seconds (Sampling)
#> Chain 4:                1.306 seconds (Total)
#> Chain 4: 
#> Warning: There were 1 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
summary(fit)
#> Warning: There were 1 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: y ~ xc + (1 + mmc(x1, x2) | mm(g1, g2)) 
#>    Data: dat (Number of observations: 100) 
#>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 4000
#> 
#> Multilevel Hyperparameters:
#> ~mmg1g2 (Number of levels: 10) 
#>                        Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
#> sd(Intercept)              0.19      0.15     0.01     0.55 1.00     1841
#> sd(mmcx1x2)                0.33      0.24     0.02     0.91 1.00     1306
#> cor(Intercept,mmcx1x2)     0.04      0.57    -0.93     0.94 1.00     2003
#>                        Tail_ESS
#> sd(Intercept)              1779
#> sd(mmcx1x2)                2045
#> cor(Intercept,mmcx1x2)     2465
#> 
#> Regression Coefficients:
#>           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept     0.20      0.13    -0.05     0.45 1.00     2919     1995
#> xc            0.00      0.20    -0.38     0.39 1.00     2902     2197
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     0.95      0.07     0.82     1.09 1.00     4748     2897
#> 
#> 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).
# }