Multilevel Model with Compositional Outcomes
Source:vignettes/C-composition-MMLM.Rmd
C-composition-MMLM.RmdIn this vignette, we discuss how to specify multilevel models with
compositional outcomes using multilevelcoda. In addition to
multilevelcoda, we will use brms package (to
fit models) and bayestestR package (to compute useful
indices and compare models). We will also attach built in datasets
mcompd (simulated compositional sleep and wake variables)
and sbp (sequential binary partition).
library(multilevelcoda)
library(brms)
library(bayestestR)
data("mcompd")
data("sbp")
options(digits = 3)Multilevel model with compositional outcomes.
Computing compositions and isometric log ratio coordinates.
The ILR coordinates outcomes can be calculated using the
compilr() functions.
cilr <- compilr(data = mcompd, sbp = sbp,
parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID", total = 1440)
head(cilr$TotalILR)
#> ilr1 ilr2 ilr3 ilr4
#> [1,] 0.287 1.20 0.6270 1.702
#> [2,] -0.472 1.57 -0.8336 0.984
#> [3,] -0.486 1.33 1.3344 2.659
#> [4,] -0.316 1.37 -0.0332 0.551
#> [5,] 0.205 1.43 -0.6893 0.733
#> [6,] -0.446 1.16 -0.0950 0.670
#> attr(,"class")
#> [1] "rmult"Fitting model
A model with multilevel compositional outcomes is multivariate, as it
has multiple ILR coordinate outcomes,each of which is predicted by a set
of predictors. Our brms model can be then fitted using the
brmcoda() function.
mv <- brmcoda(compilr = cilr,
formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + (1 | ID),
cores = 8, seed = 123, backend = "cmdstanr")
#> Warning: In the future, 'rescor' will be set to FALSE by default for all models. It is thus recommended to explicitely set 'rescor'
#> via 'set_rescor' instead of using the default.Here is a summary() of the model. We can see that stress
significantly predicted ilr1 and ilr2.
summary(mv)
#> Family: MV(gaussian, gaussian, gaussian, gaussian)
#> Links: mu = identity; sigma = identity
#> mu = identity; sigma = identity
#> mu = identity; sigma = identity
#> mu = identity; sigma = identity
#> Formula: ilr1 ~ Stress + (1 | ID)
#> ilr2 ~ Stress + (1 | ID)
#> ilr3 ~ Stress + (1 | ID)
#> ilr4 ~ Stress + (1 | ID)
#> Data: tmp (Number of observations: 3540)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Group-Level Effects:
#> ~ID (Number of levels: 266)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(ilr1_Intercept) 0.33 0.02 0.30 0.37 1.00 1203 1547
#> sd(ilr2_Intercept) 0.30 0.01 0.28 0.34 1.00 1097 2116
#> sd(ilr3_Intercept) 0.39 0.02 0.35 0.43 1.00 1594 2478
#> sd(ilr4_Intercept) 0.30 0.02 0.27 0.33 1.00 1687 2483
#>
#> Population-Level Effects:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> ilr1_Intercept -0.43 0.02 -0.48 -0.38 1.00 1089 1836
#> ilr2_Intercept 1.47 0.02 1.42 1.51 1.00 877 2029
#> ilr3_Intercept -0.87 0.03 -0.93 -0.82 1.00 1644 2583
#> ilr4_Intercept 0.65 0.02 0.60 0.70 1.00 1948 2670
#> ilr1_Stress -0.01 0.00 -0.02 -0.00 1.00 6441 3679
#> ilr2_Stress 0.01 0.00 0.00 0.01 1.00 5245 3596
#> ilr3_Stress 0.00 0.00 -0.01 0.01 1.00 6436 3519
#> ilr4_Stress 0.01 0.00 -0.00 0.01 1.00 6209 3303
#>
#> Family Specific Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma_ilr1 0.44 0.01 0.43 0.45 1.00 5231 3458
#> sigma_ilr2 0.38 0.00 0.37 0.39 1.00 5176 3232
#> sigma_ilr3 0.70 0.01 0.68 0.71 1.00 5339 3388
#> sigma_ilr4 0.53 0.01 0.51 0.54 1.00 5250 3363
#>
#> Residual Correlations:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> rescor(ilr1,ilr2) -0.54 0.01 -0.57 -0.52 1.00 5202 3296
#> rescor(ilr1,ilr3) -0.18 0.02 -0.21 -0.14 1.00 4833 3061
#> rescor(ilr2,ilr3) -0.05 0.02 -0.09 -0.02 1.00 5156 3254
#> rescor(ilr1,ilr4) 0.11 0.02 0.07 0.14 1.00 4910 3445
#> rescor(ilr2,ilr4) -0.05 0.02 -0.08 -0.01 1.00 5286 3449
#> rescor(ilr3,ilr4) 0.56 0.01 0.53 0.58 1.00 5255 3316
#>
#> Draws were sampled using sample(hmc). 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).Bayes Factor for compositional multilevel modelling
We are often interested in whether a predictor significantly predict the overall composition, in addition to the individual ILR coordinates. In Bayesian, this can be done by comparing the marginal likelihoods of two models. Bayes Factors (BFs) are indices of relative evidence of one model over another. In the context of compositional multilevel modelling, Bayes Factors provide two main useful functions:
- Testing single parameters within a model
- Comparing models
We can utilize Bayes factors to answer the following question: “Which model (i.e., set of composition predictors, expressed as ILRs) is more likely to have produced the observed data?”
Let’s examine whether stress predicts the overall sleep-wake composition.
Note: To use Bayes factors, brmsfit models must
be fitted with an additional non-default argument
save_pars = save_pars(all = TRUE).
# intercept only
mv0 <- brmcoda(compilr = cilr,
formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ 1 + (1 | ID),
iter = 6000, chains = 8, cores = 8, seed = 123, warmup = 1000,
backend = "cmdstanr", save_pars = save_pars(all = TRUE))
#> Warning: In the future, 'rescor' will be set to FALSE by default for all models. It is thus recommended to explicitely set 'rescor'
#> via 'set_rescor' instead of using the default.
# full model
mv <- brmcoda(compilr = cilr,
formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + (1 | ID),
iter = 6000, chains = 8, cores = 8, seed = 123, warmup = 1000,
backend = "cmdstanr", save_pars = save_pars(all = TRUE))
#> Warning: In the future, 'rescor' will be set to FALSE by default for all models. It is thus recommended to explicitely set 'rescor'
#> via 'set_rescor' instead of using the default.We can now compare these models with the
bayesfactor_models() function
bayes_factor(mv$Model, mv0$Model)
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#> Estimated Bayes factor in favor of mv$Model over mv0$Model: 0.00001With a \(BF\) < 1, our data favours the intercept only model, showing that there is insufficient evidence for stress predicting the overall sleep-wake composition.
Bayes factors provide a intuitive measure of the strength of evidence
of one model over the other or among different models. Check out the
bayestestR packages for several other useful functions
related to BFs.