wsubmargins: Within-person Average Substitution

In florale/multilevelcoda: Estimate Bayesian Multilevel Models for Compositional Data

Within-person Average Substitution

Description

This function is an alias of substitution to estimates the the difference in an outcome when compositional parts are substituted for specific unit(s) at within level using cluster mean (e.g., compositional mean at individual level) as reference composition. It is recommended that users run substitution model using the substitution function.

Usage

wsubmargins(
  object,
  delta,
  basesub,
  summary = TRUE,
  ref = "clustermean",
  level = "within",
  weight = "proportional",
  scale = c("response", "linear"),
  comparison,
  cores = NULL,
  ...
)

Arguments

object	A fitted brmcoda object.
delta	A integer, numeric value or vector indicating the amount of substituted change between compositional parts.
basesub	A base substitution. Can be a data.frame or data.table of the base possible substitution of compositional parts, which can be computed using function build.basesub. If "one-to-all", all possible one-to-remaining reallocations are estimated. If NULL, all possible one-to-one reallocations are estimated.
summary	A logical value to obtain summary statistics instead of the raw values. Default is TRUE. Currently only support outputing raw values for model using grandmean as reference composition.
ref	Either a character value or vector or a dataset. Can be "grandmean" and/or "clustermean", or a data.frame or data.table of user's specified reference grid consisting of combinations of covariates over which predictions are made. User's specified reference grid is only possible for simple substitution. Single level models are default to "grandmean".
level	A character string or vector. Should the estimate of multilevel models focus on the "between" and/or "within" or "aggregate" variance? Single-level models are default to "aggregate".
weight	A character value specifying the weight to use in calculation of the reference composition. If "equal", give equal weight to units (e.g., individuals). If "proportional", weights in proportion to the frequencies of units being averaged (e.g., observations across individuals). Default to "equal" for ref = "grandmean" and "proportional" for ref = "clustermean".
scale	Either "response" or "linear". If "response", results are returned on the scale of the response variable. If "linear", results are returned on the scale of the linear predictor term, that is without applying the inverse link function or other transformations.
comparison	internally used only.
cores	Number of cores to use when executing the chains in parallel, we recommend setting the mc.cores option to be as many processors as the hardware and RAM allow (up to the number of compositional parts). For non-Windows OS in non-interactive R sessions, forking is used instead of PSOCK clusters.
...	currently ignored.

Value

A list containing the results of multilevel compositional substitution model. The first six lists contain the results of the substitution estimation for a compositional part.

Mean	Posterior means.
CI_low and CI_high	95% credible intervals.
Delta	Amount substituted across compositional parts.
From	Compositional part that is substituted from.
To	Compositional parts that is substituted to.
Level	Level where changes in composition takes place.
Reference	Either grandmean, clustermean, or users.

See Also

substitution

Examples

if(requireNamespace("cmdstanr")){

cilr <- complr(data = mcompd, sbp = sbp, 
               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID", total = 1440)

# model with compositional predictor at between and within-person levels
m <- brmcoda(complr = cilr, 
             formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 + 
                                wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
                     
subm <- wsubmargins(object = m, basesub = psub, delta = 5)
}

florale/multilevelcoda documentation built on April 13, 2025, 6:16 a.m.