Compositional Substitution Multilevel Analysis
Source:vignettes/D-substitution.Rmd
D-substitution.Rmd
Intro
When examining the relationship between a composition and an
outconme, we are often are interested in how an outcome changes when a
fixed unit in the composition (e.g., minutes of behaviours during a day)
is reallocated from one component to another. The Compositional
Isotemporal Substitution Analysis can be used to estimate this change.
The multilevelcoda
package implements this method in a
multilevel framework and offers functions for both between- and
within-person levels of variability. We discuss 4 different substitution
models in this vignette.
We will begin by loading necessary packages,
multilevelcoda
, brms
(for models fitting),
doFuture (for parallelisation), and data sets mcompd
(simulated compositional sleep and wake variables), sbp
(sequential binary partition), and psub
(base possible
substitution).
Fitting main model
Let’s fit our main brms
model predicting
Stress
from both between and within-person sleep-wake
behaviours (represented by isometric log ratio coordinates), with sex as
a covariate, using the brmcoda()
function. We can compute
ILR coordinate predictors using complr()
function.
cilr <- complr(data = mcompd, sbp = sbp,
parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID", total = 1440)
m <- brmcoda(complr = cilr,
formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
wilr1 + wilr2 + wilr3 + wilr4 + Female + (1 | ID),
cores = 8, seed = 123, backend = "cmdstanr")
A summary()
of the model results.
summary(m)
#> Family: gaussian
#> Links: mu = identity; sigma = identity
#> Formula: Stress ~ bilr1 + bilr2 + bilr3 + bilr4 + wilr1 + wilr2 + wilr3 + wilr4 + Female + (1 | ID)
#> Data: tmp (Number of observations: 3540)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~ID (Number of levels: 266)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 0.99 0.06 0.87 1.11 1.00 1574 2367
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 2.65 0.48 1.69 3.56 1.00 1494 2202
#> bilr1 0.11 0.32 -0.53 0.74 1.00 967 1699
#> bilr2 0.50 0.34 -0.16 1.15 1.00 1081 2071
#> bilr3 0.11 0.21 -0.31 0.53 1.00 1059 2057
#> bilr4 0.04 0.28 -0.51 0.60 1.00 1266 2174
#> wilr1 -0.34 0.12 -0.58 -0.09 1.00 2797 2882
#> wilr2 0.05 0.13 -0.21 0.32 1.00 3134 2299
#> wilr3 -0.10 0.08 -0.26 0.06 1.00 2712 2653
#> wilr4 0.24 0.10 0.04 0.44 1.00 2963 2874
#> Female -0.41 0.17 -0.77 -0.07 1.00 1364 1823
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 2.38 0.03 2.33 2.44 1.00 4866 2981
#>
#> 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).
We can see that the first and forth within-person ILR coordinates were both associated with stress. Interpretation for multilevel ILR coordinates can often be less intuitive. For example, the significant coefficient for wilr1 shows that the within-person change in sleep behaviours (sleep duration and time awake in bed combined), relative to wake behaviours (moderate to vigorous physical activity, light physical activity, and sedentary behaviour) on a given day, is associated with stress. However, as there are several behaviours involved in this coordinate, we don’t know the within-person change in which of them drives the association. It could be the change in sleep, such that people sleep more than their own average on a given day, but it could also be the change in time awake. Further, we don’t know about the specific changes in time spent across behaviours. That is, if people sleep more, what behaviour do they spend less time in?
This is common issue when working with multilevel compositional data
as ILR coordinates often contains information about multiple
compositional components. To gain further insights into these
associations and help with interpretation, we can conduct post-hoc
analyses using the substitution models from our multilevel
package.
Substitution models
multilevelcoda
package provides 2
different
methods to compute substitution models, via the
substitution()
function.
Basic substitution models:
- Between-person substitution
- Within-person substitution
Average marginal substitution models:
- Average marginal between-person substitution
- Average marginal within-person substitution
Tips: Substitution models are often computationally demanding
tasks. You can speed up the models using parallel execution, for
example, using doFuture
package.
Basic Substitution Analysis
The below example examines the changes in stress for different
pairwise substitution of sleep-wake behaviours for a period of 1 to 5
minutes, at between-person level. We specify
level = between
to indicate substitutional change would be
at the between-person level, and ref = "grandmean"
to
indicate substitution model using the grand compositional mean as
reference composition. If your model contains covariates,
substitution()
will average predictions across levels of
covariates as the default.
subm1 <- substitution(object = m, delta = 1:10,
ref = "grandmean", level = c("between", "within"))
Output from substitution()
contains multiple data set of
results for all available compositional component. Here are the results
for changes in stress when sleep (TST) is substituted for 10
minutes.
Mean | CI_low | CI_high | Delta | From | To | Level | Reference |
---|---|---|---|---|---|---|---|
0.06 | -0.01 | 0.13 | 10 | WAKE | TST | between | grandmean |
0.01 | -0.03 | 0.04 | 10 | MVPA | TST | between | grandmean |
0.01 | -0.01 | 0.03 | 10 | LPA | TST | between | grandmean |
0.01 | -0.01 | 0.04 | 10 | SB | TST | between | grandmean |
None of them are significant, given that the credible intervals did
not cross 0, showing that increasing sleep (TST) at the expense of any
other behaviours was not associated in changes in stress at
between-person level. These results can be plotted to see the patterns
more easily using the plot()
function.
plot(subm1, to = "TST", level = "between", ref = "grandmean")
Here are the results for within-person level.
Mean | CI_low | CI_high | Delta | From | To | Level | Reference |
---|---|---|---|---|---|---|---|
0.04 | 0.01 | 0.07 | 10 | WAKE | TST | within | grandmean |
-0.01 | -0.02 | 0.01 | 10 | MVPA | TST | within | grandmean |
-0.01 | -0.02 | 0.00 | 10 | LPA | TST | within | grandmean |
0.00 | -0.01 | 0.01 | 10 | SB | TST | within | grandmean |
At within-person level, we got some significant results for substitution of sleep (TST) and time awake in bed (WAKE) for 5 minutes, but not other behaviours. Increasing 5 minutes in sleep at the expense of time spent awake in bed predicted 0.04 higher stress [95% CI 0.01, 0.7], on a given day. Let’s also plot theses results.
plot(subm1, to = "TST", level = "within", ref = "grandmean")
Average Marginal Substitution Effects
The average marginal models use the unit compositional mean as the
reference composition to obtain the average of the predicted group-level
changes in the outcome when every unit (e.g., individual) in the sample
reallocates a specific unit from one compositional part to another. This
is difference from the basic substitution model which yields prediction
conditioned on an “average” person in the data set (e.g., by using the
grand compositional mean as the reference composition). Average
substitution models models are generally more computationally expensive
than basic subsitution models. All models can be run faster in shorter
walltime using parallel execution. In this example, we set
cores = 5
. substitution()
will run 5
substitution models for 5 sleep-wake behaviours by parallel excuting
them across 5 workers.
subm2 <- substitution(object = m, delta = 1:10,
ref = "clustermean", level = c("between", "within"),
cores = 5)
Below are the results.
Mean | CI_low | CI_high | Delta | To | From | Level | Reference |
---|---|---|---|---|---|---|---|
0.07 | -0.01 | 0.15 | 10 | TST | WAKE | between | clustermean |
0.01 | -0.03 | 0.04 | 10 | TST | MVPA | between | clustermean |
0.01 | -0.01 | 0.04 | 10 | TST | LPA | between | clustermean |
0.01 | -0.02 | 0.05 | 10 | TST | SB | between | clustermean |
0.05 | 0.01 | 0.08 | 10 | TST | WAKE | within | clustermean |
-0.01 | -0.02 | 0.01 | 10 | TST | MVPA | within | clustermean |
-0.01 | -0.02 | 0.00 | 10 | TST | LPA | within | clustermean |
0.00 | -0.01 | 0.01 | 10 | TST | SB | within | clustermean |
A comparison between between- and within-person substitution analyses
of sleep on stress, plot using plot()
and
ggpubr::ggarrange()
functions.
library(ggpubr)
p1 <- plot(subm2, to = "TST", level = "between", ref = "clustermean")
p2 <- plot(subm2, to = "TST", level = "within", ref = "clustermean")
ggarrange(p1, p2,
ncol = 1, nrow = 2)