Package 'multilevelcoda' reference manual

Title:	Estimate Bayesian Multilevel Models for Compositional Data
Description:	Implement Bayesian Multilevel Modelling for compositional data in a multilevel framework. Compute multilevel compositional data and Isometric log ratio (ILR) at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models.
Authors:	Flora Le [aut, cre] , Joshua F. Wiley [aut]
Maintainer:	Flora Le <[email protected]>
License:	GPL (>= 3)
Version:	1.3.0.2
Built:	2024-09-08 06:47:03 UTC
Source:	CRAN

Extract Compositional Data from `complr` object.

Description

Extract amounts and compositions in conventional formats as data.frames, matrices, or arrays.

Usage

## S3 method for class 'complr'
as.data.frame(x, row.names = NULL, optional = TRUE, ...)

## S3 method for class 'complr'
as.matrix(x, ...)
## S3 method for class 'complr'
as.data.frame(x, row.names = NULL, optional = TRUE, ...)

## S3 method for class 'complr'
as.matrix(x, ...)

Arguments

`x`	An object of class `complr`.
`row.names`, `optional`	Unused and only added for consistency with the `as.data.frame` generic.
`...`	generic argument, not in use.

Base Pairwise Substitution

Description

Make a data set of all possible pairwise substitution of a composition which can be used as the base for substitution models.

Usage

basesub(parts)
basesub(parts)

Arguments

parts

A character vector specifying the names of compositional variables to be used.

Value

A data table of all possible pairwise substitution.

Examples

ps1 <- basesub(parts = c("TST", "WAKE", "MVPA", "LPA", "SB"))
print(ps1)

ps2 <- basesub(c("WAKE", "MVPA", "LPA", "SB"))
print(ps2)
ps1 <- basesub(parts = c("TST", "WAKE", "MVPA", "LPA", "SB"))
print(ps1)

ps2 <- basesub(c("WAKE", "MVPA", "LPA", "SB"))
print(ps2)

Bayes Factors from Marginal Likelihoods

Description

Compute Bayes factors from marginal likelihoods

Usage

## S3 method for class 'brmcoda'
bayes_factor(x1, x2, ...)
## S3 method for class 'brmcoda'
bayes_factor(x1, x2, ...)

Arguments

`x1`	A `brmcoda` object.
`x2`	Another `brmcoda` object based on the same responses.
`...`	Other arguments passed to `bayes_factor.brmsfit`.

Fit Bayesian generalised (non-)linear multilevel compositional model via full Bayesian inference

Description

Fit a brm model with multilevel ILR coordinates

Usage

brmcoda(complr, formula, ...)
brmcoda(complr, formula, ...)

Arguments

`complr`	A `complr` object containing data of composition, ILR coordinates, and other variables used in the model.
`formula`	A object of class `formula`, `brmsformula`: A symbolic description of the model to be fitted. Details of the model specification can be found in `brmsformula`.
`...`	Further arguments passed to `brm`.

Value

A brmcoda with two elements

`complr`	An object of class `complr` used in the `brm` model.
`model`	An object of class `brmsfit`, which contains the posterior draws along with many other useful information about the model.

Examples


if(requireNamespace("cmdstanr")){
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")
  
  # inspects ILRs before passing to brmcoda
  names(cilr$between_logratio)
  names(cilr$within_logratio)
  names(cilr$logratio)
  
  # model with compositional predictor at between and within-person levels
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                   wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  # model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  }
if(requireNamespace("cmdstanr")){
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")
  
  # inspects ILRs before passing to brmcoda
  names(cilr$between_logratio)
  names(cilr$within_logratio)
  names(cilr$logratio)
  
  # model with compositional predictor at between and within-person levels
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                   wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  # model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  }

Between-person Simple 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 between level using a single reference composition (e.g., compositional mean at sample level). It is recommended that users run substitution model using the substitution function.

Usage

bsub(
  object,
  delta,
  basesub,
  summary = TRUE,
  ref = "grandmean",
  level = "between",
  weight = "equal",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
bsub(
  object,
  delta,
  basesub,
  summary = TRUE,
  ref = "grandmean",
  level = "between",
  weight = "equal",
  scale = c("response", "linear"),
  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 `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`summary`	A logical value. Should the estimate at each level of the reference grid (`FALSE`) or their average (`TRUE`) be returned? Default is `TRUE`. Only applicable for model with covariates in addition to the isometric log-ratio coordinates (i.e., adjusted model).
`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.
`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`.

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 between-person levels
m <- brmcoda(complr = cilr, 
             formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 + 
                                wilr1 + wilr2 + wilr3 + wilr4 + Female + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
subm <- bsub(object = m, basesub = psub, delta = 5)
}
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 between-person levels
m <- brmcoda(complr = cilr, 
             formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 + 
                                wilr1 + wilr2 + wilr3 + wilr4 + Female + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
subm <- bsub(object = m, basesub = psub, delta = 5)
}

Between-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 between 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

bsubmargins(
  object,
  delta,
  basesub,
  ref = "clustermean",
  level = "between",
  weight = "proportional",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
bsubmargins(
  object,
  delta,
  basesub,
  ref = "clustermean",
  level = "between",
  weight = "proportional",
  scale = c("response", "linear"),
  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 `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`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.
`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`.

Examples


if(requireNamespace("cmdstanr")){
cilr <- complr(data = mcompd[ID %in% 1:10, .SD[1:3], by = ID], 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), 
             chains = 1, iter = 500,
             backend = "cmdstanr")
             
subm <- bsubmargins(object = m, basesub = psub, delta = 5)
}
if(requireNamespace("cmdstanr")){
cilr <- complr(data = mcompd[ID %in% 1:10, .SD[1:3], by = ID], 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), 
             chains = 1, iter = 500,
             backend = "cmdstanr")
             
subm <- bsubmargins(object = m, basesub = psub, delta = 5)
}

Reference Grid for `substitution` model.

Description

Build a dataset for fitted.brmcoda used in substitution model

Usage

build.rg(object, ref, level, weight, fill = FALSE)
build.rg(object, ref, level, weight, fill = FALSE)

Arguments

`object`	A fitted `brmcoda` object.
`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"`.
`fill`	Logical value only relevant when `ref` is an user's specified reference grid in which information about some, but not all covariates is provided (e.g., models including age and sex as covariate but only age was provided in the reference grid). If `TRUE`, the unspecified covariates are filled with the default reference grid. If `FALSE`, users will be asked to provide a full reference grid. Currently only support the default to `FALSE`.

Value

A reference grid consisting of a combination of covariates in brmcoda

Model Coefficients

Description

Extract model coefficients, which are the sum of population-level effects and corresponding group-level effects of the brmsfit object in a brmcoda object.

Usage

## S3 method for class 'brmcoda'
coef(object, ...)
## S3 method for class 'brmcoda'
coef(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to `coef.brmsfit`.

Value

A list of 3D arrays (one per grouping factor). If summary is TRUE, the 1st dimension contains the factor levels, the 2nd dimension contains the summary statistics (see posterior_summary), and the 3rd dimension contains the group-level effects. If summary is FALSE, the 1st dimension contains the posterior draws, the 2nd dimension contains the factor levels, and the 3rd dimension contains the group-level effects.

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract population and group-level coefficients separately
  fixef(m)
  ranef(m)
  
  ## extract combined coefficients
  coef(m)
}
## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract population and group-level coefficients separately
  fixef(m)
  ranef(m)
  
  ## extract combined coefficients
  coef(m)
}

Indices from a (dataset of) Multilevel Composition(s) (deprecated.)

Description

Indices from a (dataset of) Multilevel Composition(s) (deprecated.)

Usage

compilr(...)
compilr(...)

Arguments

...

arguments passed to complr.

Value

A complr object with at least the following elements.

`comp`	A vector of class `acomp` representing one closed composition or a matrix of class `acomp` representing multiple closed compositions each in one row.
`between_comp`	A vector of class `acomp` representing one closed between-person composition or a matrix of class `acomp` representing multiple closed between-person compositions each in one row.
`within_comp`	A vector of class `acomp` representing one closed within-person composition or a matrix of class `acomp` representing multiple closed within-person compositions each in one row.
`logratio`	Log ratio transform of composition.
`between_logratio`	Log ratio transform of between-person composition.
`within_logratio`	Log ratio transform of within-person composition.
`data`	The user's dataset or imputed dataset if the input data contains zeros.
`transform`	Type of transform applied on compositional data.
`parts`	Names of compositional variables.
`idvar`	Name of the variable containing IDs.
`total`	Total amount to which the compositions is closed.

Indices from a (dataset of) Multilevel Composition(s)

Description

Compute sets of compositions and log ratio transformation for multilevel compositional data

Usage

complr(data, parts, sbp = NULL, total = 1, idvar = NULL, transform = "ilr")
complr(data, parts, sbp = NULL, total = 1, idvar = NULL, transform = "ilr")

Arguments

`data`	A `data.frame` or `data.table` containing data of all variables used in the analysis. It must include a composition and a ID variable. Required.
`parts`	A character vector specifying the names of compositional variables to be used.
`sbp`	A signary matrix indicating sequential binary partition.
`total`	A numeric value of the total amount to which the compositions should be closed. Default is `1`.
`idvar`	Only for multilevel data, a character string specifying the name of the variable containing IDs.
`transform`	A character value naming a log ratio transformation to be applied on compositional data. Can be either `"ilr"` (isometric logratio), `"alr"` (additive logratio), or `"clr"` (centered logratio). Default is `"ilr"`.

Details

The ilr-transform maps the D-part compositional data from the simplex into non-overlapping subgroups in the (D-1)-dimension Euclidean space isometrically by using an orthonormal basis, thereby preserving the compositional properties and yielding a full-rank covariance matrix. ilr transformation should be preferred. However, the alr and clr are alternatives. The alr-transform maps a D-part composition in the Aitchison-simplex non-isometrically to a (D-1)-dimension Euclidian vectors, commonly treating the last part as the common denominator of the others. alr transformation does not rely on distance which breaks the constraint of compositional data. clr-transform maps a D-part composition in the Aitchison-simplex isometrically to a D-dimensional Euclidian vector subspace. clr transformation is not injetive, resulting in singular covariance matrices.

Value

A complr object with at least the following elements.

`comp`	A vector of class `acomp` representing one closed composition or a matrix of class `acomp` representing multiple closed compositions each in one row.
`between_comp`	A vector of class `acomp` representing one closed between-person composition or a matrix of class `acomp` representing multiple closed between-person compositions each in one row.
`within_comp`	A vector of class `acomp` representing one closed within-person composition or a matrix of class `acomp` representing multiple closed within-person compositions each in one row.
`logratio`	Log ratio transform of composition.
`between_logratio`	Log ratio transform of between-person composition.
`within_logratio`	Log ratio transform of within-person composition.
`data`	The user's dataset or imputed dataset if the input data contains zeros.
`transform`	Type of transform applied on compositional data.
`parts`	Names of compositional variables.
`idvar`	Name of the variable containing IDs.
`total`	Total amount to which the compositions is closed.

Examples

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

calr <- complr(data = mcompd, sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID",
                transform = "alr")
str(calr)

cclr <- complr(data = mcompd, sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID",
                 transform = "clr")
str(cclr)

cilr_wide <- complr(data = mcompd[!duplicated(ID)], sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"))
str(cilr_wide)
cilr <- complr(data = mcompd, sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID", total = 1440)
str(cilr)

calr <- complr(data = mcompd, sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID",
                transform = "alr")
str(calr)

cclr <- complr(data = mcompd, sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID",
                 transform = "clr")
str(cclr)

cilr_wide <- complr(data = mcompd[!duplicated(ID)], sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"))
str(cilr_wide)

Constructor function for `substitution` class.

Description

Constructor function for substitution class.

Usage

create_substitution(
  between_simple_sub,
  within_simple_sub,
  simple_sub,
  between_avg_sub,
  within_avg_sub,
  avg_sub,
  delta,
  ref,
  level,
  weight,
  parts,
  summary
)
create_substitution(
  between_simple_sub,
  within_simple_sub,
  simple_sub,
  between_avg_sub,
  within_avg_sub,
  avg_sub,
  delta,
  ref,
  level,
  weight,
  parts,
  summary
)

Arguments

`between_simple_sub`	A list of results from `bsub` or `NULL`
`within_simple_sub`	A list of results from `wsub` or `NULL`
`simple_sub`	A list of results from `sub` or `NULL`
`between_avg_sub`	A list of results from `bsubmargins` or `NULL`
`within_avg_sub`	A list of results from `wsubmargins` or `NULL`
`avg_sub`	A list of results from `submargins` or `NULL`
`delta`	A numeric vector of the amount of substitution
`ref`	A character value specifying the reference grid
`level`	A character value specifying the level of substitution
`weight`	The weight to use in calculation of the reference composition
`parts`	The parts of the composition
`summary`	A logical value specifying whether to summarize the results

Value

An object of class substitution

Extract Diagnostic Quantities from `brmsfit` Models in `brmcoda`

Description

Extract Diagnostic Quantities from brmsfit Models in brmcoda

Usage

## S3 method for class 'brmcoda'
log_posterior(object, ...)

## S3 method for class 'brmcoda'
nuts_params(object, ...)

## S3 method for class 'brmcoda'
rhat(x, ...)

## S3 method for class 'brmcoda'
neff_ratio(object, ...)
## S3 method for class 'brmcoda'
log_posterior(object, ...)

## S3 method for class 'brmcoda'
nuts_params(object, ...)

## S3 method for class 'brmcoda'
rhat(x, ...)

## S3 method for class 'brmcoda'
neff_ratio(object, ...)

Arguments

`...`	Arguments passed to individual methods (if applicable).
`x`, `object`	A `brmcoda` object or another R object for which the methods are defined.

Value

The exact form of the output depends on the method.

Index `brmcoda` objects

Description

Index brmcoda objects

Usage

## S3 method for class 'brmcoda'
variables(x, ...)

## S3 method for class 'brmcoda'
nvariables(x, ...)

## S3 method for class 'brmcoda'
niterations(x)

## S3 method for class 'brmcoda'
nchains(x)

## S3 method for class 'brmcoda'
ndraws(x)
## S3 method for class 'brmcoda'
variables(x, ...)

## S3 method for class 'brmcoda'
nvariables(x, ...)

## S3 method for class 'brmcoda'
niterations(x)

## S3 method for class 'brmcoda'
nchains(x)

## S3 method for class 'brmcoda'
ndraws(x)

Arguments

`x`	An object of class `brmcoda`.
`...`	Arguments passed to individual methods.

Expected Values of the Posterior Predictive Distribution

Description

Compute posterior draws of the expected value of the posterior predictive distribution of a brmsfit model in the brmcoda object. Can be performed for the data used to fit the model (posterior predictive checks) or for new data. By definition, these predictions have smaller variance than the posterior predictions performed by the predict.brmcoda method. This is because only the uncertainty in the expected value of the posterior predictive distribution is incorporated in the draws computed by fitted while the residual error is ignored there. However, the estimated means of both methods averaged across draws should be very similar.

Usage

## S3 method for class 'brmcoda'
fitted(object, scale = c("linear", "response"), summary = TRUE, ...)
## S3 method for class 'brmcoda'
fitted(object, scale = c("linear", "response"), summary = TRUE, ...)

Arguments

`object`	An object of class `brmcoda`.
`scale`	Specifically for models with compositional responses, either `"response"` or `"linear"`. If `"linear"`, results are returned on the log-ratio scale. If `"response"`, results are returned on the compositional scale of the response variable.
`summary`	Should summary statistics be returned instead of the raw values? Default is `TRUE`.
`...`	Further arguments passed to `fitted.brmsfit` that control additional aspects of prediction.

Value

An array of predicted mean response values. If summary = FALSE the output resembles those of posterior_epred.brmsfit.

If summary = TRUE the output depends on the family: For categorical and ordinal families, the output is an N x E x C array, where N is the number of observations, E is the number of summary statistics, and C is the number of categories. For all other families, the output is an N x E matrix. The number of summary statistics E is equal to 2 + length(probs): The Estimate column contains point estimates (either mean or median depending on argument robust), while the Est.Error column contains uncertainty estimates (either standard deviation or median absolute deviation depending on argument robust). The remaining columns starting with Q contain quantile estimates as specified via argument probs.

In multivariate models, an additional dimension is added to the output which indexes along the different response variables.

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  ## compute composition and ilr coordinates
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)
  
  ## fit a model
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                  wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## compute expected predictions
  epred <- fitted(m1)
  head(epred)
  
  ## fit a model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## expected predictions on compositional scale
  epredcomp <- fitted(m2, scale = "response")
  head(epredcomp)
}
## fit a model
if(requireNamespace("cmdstanr")){
  ## compute composition and ilr coordinates
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)
  
  ## fit a model
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                  wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## compute expected predictions
  epred <- fitted(m1)
  head(epred)
  
  ## fit a model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## expected predictions on compositional scale
  epredcomp <- fitted(m2, scale = "response")
  head(epredcomp)
}

Population-Level Estimates

Description

Extract the population-level ('fixed') effects from the brmsfit object in a brmcoda object.

Usage

## S3 method for class 'brmcoda'
fixef(object, ...)
## S3 method for class 'brmcoda'
fixef(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to `fixef.brmsfit`.

Value

If summary is TRUE, a matrix returned by posterior_summary for the population-level effects. If summary is FALSE, a matrix with one row per posterior draw and one column per population-level effect.

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  ## fit a model
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract population-Level coefficients
  fixef(m)
}
## fit a model
if(requireNamespace("cmdstanr")){
  ## fit a model
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract population-Level coefficients
  fixef(m)
}

Helper functions used only internally to estimate substitution model

Description

Helper functions used only internally to estimate substitution model

Checks if argument is a `brmcoda` object

Description

Checks if argument is a brmcoda object

Usage

is.brmcoda(x)
is.brmcoda(x)

Arguments

`x`	An object of class `brmcoda`.

Checks if argument is a `complr` object

Description

Checks if argument is a complr object

Usage

is.complr(x)
is.complr(x)

Arguments

`x`	An object of class `complr`.

Checks if argument is a `substitution` object

Description

Checks if argument is a substitution object

Usage

is.substitution(x)
is.substitution(x)

Arguments

`x`	An object of class `substitution`.

Interface to shinystan

Description

Provide an interface to shinystan for models fitted with brms

Usage

## S3 method for class 'brmcoda'
launch_shinystan(object, ...)
## S3 method for class 'brmcoda'
launch_shinystan(object, ...)

Arguments

`object`	A fitted model object of class `brmcoda`.
`...`	Optional arguments to pass to `launch_shinystan` or `runApp`.

Value

An S4 shinystan object

Efficient approximate leave-one-out cross-validation (LOO)

Description

Perform approximate leave-one-out cross-validation based on the posterior likelihood using the loo package. For more details see loo.

Usage

## S3 method for class 'brmcoda'
loo(x, ...)
## S3 method for class 'brmcoda'
loo(x, ...)

Arguments

`x`	A `brmcoda` object.
`...`	More `brmsfit` objects or further arguments passed to the underlying post-processing functions. In particular, see `prepare_predictions` for further supported arguments.

Value

If just one object is provided, an object of class loo. If multiple objects are provided, an object of class loolist.

MCMC Plots Implemented in bayesplot

Description

Call MCMC plotting functions implemented in the bayesplot package.

Usage

## S3 method for class 'brmcoda'
mcmc_plot(object, ...)
## S3 method for class 'brmcoda'
mcmc_plot(object, ...)

Arguments

`object`	A `brmcoda` class object.
`...`	Further arguments passed to `mcmc_plot.brmsfit`.

Value

A ggplot object that can be further customized using the ggplot2 package.

Examples

## Not run: 
cilr <- complr(data = mcompd, sbp = sbp,
        parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = cilr,
               formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
               chain = 1, iter = 500)
mcmc_plot(fit)

## End(Not run)
## Not run: 
cilr <- complr(data = mcompd, sbp = sbp,
        parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = cilr,
               formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
               chain = 1, iter = 500)
mcmc_plot(fit)

## End(Not run)

Multilevel Compositional Data

Description

A simulated dataset containing multiple days of compositional data.

Usage

mcompd
mcompd

Format

A data table containing 10 variables.

ID: A unique identifier for each individual
Time: Recurrence time of repeated measures by individual
Stress: Self report stress measures on a 0 to 10 scale — repeated measure
TST: Total Sleep Time (minutes) — repeated measure
WAKE: Wake time while in bed, trying to sleep (minutes) — repeated measure
MVPA: Moderate to Vigorous Physical Activity (minutes) — repeated measure
LPA: Light Physical Activity (minutes) — repeated measure
SB: Sedentary Behavior (minutes) — repeated measure
Age: Age in years — baseline measure only
Female: Binary: whether participants identified as female (1) or not (0) — baseline measure only

Mean amounts and mean compositions presented in a `complr` object.

Description

Mean amounts and mean compositions presented in a complr object.

Usage

## S3 method for class 'complr'
mean(x, weight = c("equal", "proportional"), ...)
## S3 method for class 'complr'
mean(x, weight = c("equal", "proportional"), ...)

Arguments

`x`	An object of class `complr`.
`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 is `equal`.
`...`	generic argument, not in use.

Examples

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

Extracting the Model Frame from a Formula or Fit from brmcoda object

Description

Extracting the Model Frame from a Formula or Fit from brmcoda object

Usage

## S3 method for class 'brmcoda'
model.frame(formula, ...)
## S3 method for class 'brmcoda'
model.frame(formula, ...)

Arguments

`formula`	A `brmcoda` object.
`...`	Further arguments to be passed to methods.

multilevelcoda Simulation Study Results

Description

Provide the full results for a simulation study testing the performance of multilevelcoda

Usage

multilevelcoda_sim()
multilevelcoda_sim()

Value

An S4 shiny object

Extract Number of Observations from brmcoda object

Description

Extract Number of Observations from brmcoda object

Usage

## S3 method for class 'brmcoda'
nobs(object, ...)
## S3 method for class 'brmcoda'
nobs(object, ...)

Arguments

`object`	A `brmcoda` object.
`...`	Further arguments to be passed to methods.

Create a matrix of output plots from a `brmcoda`'s `brmsfit` object

Description

A pairs method that is customized for MCMC output.

Usage

## S3 method for class 'brmcoda'
pairs(x, ...)
## S3 method for class 'brmcoda'
pairs(x, ...)

Arguments

`x`	A `brmcoda` class object.
`...`	Further arguments passed to `pairs.brmsfit`.

Examples

## Not run: 
cilr <- complr(data = mcompd, sbp = sbp,
        parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = cilr,
               formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
               chain = 1, iter = 500)
pairs(fit)

## End(Not run)
## Not run: 
cilr <- complr(data = mcompd, sbp = sbp,
        parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = cilr,
               formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
               chain = 1, iter = 500)
pairs(fit)

## End(Not run)

Trace and Density Plots for MCMC Draws plot

Description

Make a plot of brmcoda model results.

Usage

## S3 method for class 'brmcoda'
plot(x, ...)
## S3 method for class 'brmcoda'
plot(x, ...)

Arguments

`x`	A `brmcoda` class object.
`...`	Further arguments passed to `plot.brmsfit`.

Value

An invisible list of gtable objects.

Examples

## Not run: 
cilr <- complr(data = mcompd, sbp = sbp,
        parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = cilr,
               formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
               chain = 1, iter = 500)
plot(fit)

## End(Not run)
## Not run: 
cilr <- complr(data = mcompd, sbp = sbp,
        parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = cilr,
               formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
               chain = 1, iter = 500)
plot(fit)

## End(Not run)

Substitution Plot

Description

Make a plot of substitution model results.

Usage

## S3 method for class 'substitution'
plot(x, to, ref, level, ...)
## S3 method for class 'substitution'
plot(x, to, ref, level, ...)

Arguments

`x`	A `substitution` class object.
`to`	A character value or vector specifying the names of the compositional parts that were reallocated to in the model.
`ref`	A character value of ((`"grandmean"` or `"clustermean"` or `"users"`),
`level`	A character value of (`"between"`, `"within"`), or `"aggregate"`).
`...`	Further components to the plot, followed by a plus sign (+).

Value

A ggplot graph object showing the estimated difference in outcome when each pair of compositional variables are substituted for a specific time.

Posterior Predictive Checks for `brmcoda` Objects

Description

Perform posterior predictive checks with the help of the bayesplot package.

Usage

## S3 method for class 'brmcoda'
pp_check(object, ...)
## S3 method for class 'brmcoda'
pp_check(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to `predict.brmsfit` as well as to the PPC function specified in `type`.

Draws from the Posterior Predictive Distribution

Description

Compute posterior draws of the posterior predictive distribution of a brmsfit model in the brmcoda object. Can be performed for the data used to fit the model (posterior predictive checks) or for new data. By definition, these draws have higher variance than draws of the expected value of the posterior predictive distribution computed by fitted.brmcoda. This is because the residual error is incorporated in posterior_predict. However, the estimated means of both methods averaged across draws should be very similar.

Usage

## S3 method for class 'brmcoda'
predict(object, scale = c("linear", "response"), summary = TRUE, ...)
## S3 method for class 'brmcoda'
predict(object, scale = c("linear", "response"), summary = TRUE, ...)

Arguments

`object`	An object of class `brmcoda`.
`scale`	Specifically for models with compositional responses, either `"response"` or `"linear"`. If `"linear"`, results are returned on the log-ratio scale. If `"response"`, results are returned on the compositional scale of the response variable.
`summary`	Should summary statistics be returned instead of the raw values? Default is `TRUE`.
`...`	Further arguments passed to `predict.brmsfit` that control additional aspects of prediction.

Value

An array of predicted response values. If summary = FALSE the output resembles those of posterior_predict.brmsfit.

If summary = TRUE the output depends on the family: For categorical and ordinal families, the output is an N x C matrix, where N is the number of observations, C is the number of categories, and the values are predicted category probabilities. For all other families, the output is a N x E matrix where E = 2 + length(probs) is the number of summary statistics: The Estimate column contains point estimates (either mean or median depending on argument robust), while the Est.Error column contains uncertainty estimates (either standard deviation or median absolute deviation depending on argument robust). The remaining columns starting with Q contain quantile estimates as specified via argument probs.

Examples


if(requireNamespace("cmdstanr")){
  ## fit a model
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)
  
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                  wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## predicted responses
  pred <- predict(m1)
  head(pred)
  
  ## fit a model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## predicted responses on compositional scale
  predcomp <- predict(m2, scale = "linear")
  head(predcomp)
}
if(requireNamespace("cmdstanr")){
  ## fit a model
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)
  
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                  wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## predicted responses
  pred <- predict(m1)
  head(pred)
  
  ## fit a model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  ## predicted responses on compositional scale
  predcomp <- predict(m2, scale = "linear")
  head(predcomp)
}

Print a Summary for a fitted `brmsfit` model in a `brmcoda` object

Description

Print a Summary for a fitted brmsfit model in a brmcoda object

Usage

## S3 method for class 'brmcoda'
print(x, ...)
## S3 method for class 'brmcoda'
print(x, ...)

Arguments

`x`	An object of class `brmcoda`.
`...`	Other arguments passed to `summary.brmcoda`.

Examples


if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
 print(m)
}
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
 print(m)
}

Print a Summary for a `complr` object

Description

Print a Summary for a complr object

Usage

## S3 method for class 'complr'
print(x, ...)
## S3 method for class 'complr'
print(x, ...)

Arguments

`x`	An object of class `complr`.
`...`	Other arguments passed to `summary.complr`.

Examples


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

Print a Summary for a `substitution` object

Description

Print a Summary for a substitution object

Usage

## S3 method for class 'substitution'
print(x, ...)
## S3 method for class 'substitution'
print(x, ...)

Arguments

`x`	A `substitution` object.
`...`	Additional arguments to be passed to to method `summary` of `substitution`.

Examples


if(requireNamespace("cmdstanr")){
  ## fit a model with compositional predictor at between and between-person levels
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  subm <- substitution(object = m, delta = 5)
  print(subm)
}
if(requireNamespace("cmdstanr")){
  ## fit a model with compositional predictor at between and between-person levels
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  subm <- substitution(object = m, delta = 5)
  print(subm)
}

Summary for a `complr` object

Description

Summary for a complr object

Usage

## S3 method for class 'summary.complr'
print(x, ...)
## S3 method for class 'summary.complr'
print(x, ...)

Arguments

`x`	An object of class `summary.complr`.
`...`	Other arguments passed to `summary.complr`.

Examples


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

Extract Priors of a `brmsfit` from a `brmcoda` object

Description

Compute Bayes factors from marginal likelihoods

Usage

## S3 method for class 'brmcoda'
prior_summary(object, ...)
## S3 method for class 'brmcoda'
prior_summary(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to or from other methods.

Possible Pairwise Substitutions

Description

A dataset containing possible pairwise subsitutions.

Usage

psub
psub

Format

A data table containing 5 variables.

TST: first compositional variable
WAKE: second compositional variable
MVPA: third compositional variable
LPA: fourth compositional variable
SB: fifth compositional variable

Group-Level Estimates

Description

Extract the group-level ('random') effects of each level of the brmsfit object in a brmcoda object.

Usage

## S3 method for class 'brmcoda'
ranef(object, ...)
## S3 method for class 'brmcoda'
ranef(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to `ranef.brmsfit`.

Value

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract group-level coefficients
  ranef(m)
}
## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract group-level coefficients
  ranef(m)
}

Posterior Draws of Residuals/Predictive Errors

Description

Compute posterior draws of residuals/predictive errors

Usage

## S3 method for class 'brmcoda'
residuals(object, ...)
## S3 method for class 'brmcoda'
residuals(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to `residuals.brmsfit`.

Value

An array of predictive error/residual draws. If summary = FALSE the output resembles those of predictive_error.brmsfit. If summary = TRUE the output is an N x E matrix, where N is the number of observations and E denotes the summary statistics computed from the draws.

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract residuals
  res <- residuals(m)
  head(res)
}
## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  ## extract residuals
  res <- residuals(m)
  head(res)
}

Sequential Binary Partition

Description

A matrix of sequential binary partition.

Usage

sbp
sbp

Format

A matrix with 5 columns and 4 rows.

TST: first compositional variable
WAKE: second compositional variable
MVPA: third compositional variable
LPA: fourth compositional variable
SB: fifth compositional variable

multilevelcoda Simulation Study results

Description

A list of 4 components

Usage

sim
sim

Format

A list with 5 columns and 4 rows.

brmcoda_tab: Simulation results for brmcoda() for tables
sub_tab: Simulation results for substitution() for tables
brmcoda_plot: Simulation results for brmcoda() for graphs
sub_plot: Simulation results for substitution() for graphs

Simple 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) using a aggregate reference composition (e.g., compositional mean at sample level, not seperated by between- and within effects). It is recommended that users run substitution model using the substitution function.

Usage

sub(
  object,
  delta,
  basesub,
  summary = TRUE,
  ref = "grandmean",
  level = "aggregate",
  weight = "equal",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
sub(
  object,
  delta,
  basesub,
  summary = TRUE,
  ref = "grandmean",
  level = "aggregate",
  weight = "equal",
  scale = c("response", "linear"),
  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 `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`summary`	A logical value. Should the estimate at each level of the reference grid (`FALSE`) or their average (`TRUE`) be returned? Default is `TRUE`. Only applicable for model with covariates in addition to the isometric log-ratio coordinates (i.e., adjusted model).
`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.
`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`.

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 ~ ilr1 + ilr2 + ilr3 + ilr4 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
             
subm <- sub(object = m, basesub = psub, delta = 5)
}
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 ~ ilr1 + ilr2 + ilr3 + ilr4 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
             
subm <- sub(object = m, basesub = psub, delta = 5)
}

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) 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

submargins(
  object,
  delta,
  basesub,
  ref = "clustermean",
  level = "aggregate",
  weight = "proportional",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
submargins(
  object,
  delta,
  basesub,
  ref = "clustermean",
  level = "aggregate",
  weight = "proportional",
  scale = c("response", "linear"),
  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 `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`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.
`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`.

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 ~ ilr1 + ilr2 + ilr3 + ilr4 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
                     
subm <- submargins(object = m, basesub = psub, delta = 5)
}
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 ~ ilr1 + ilr2 + ilr3 + ilr4 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
                     
subm <- submargins(object = m, basesub = psub, delta = 5)
}

Multilevel Compositional Substitution Analysis

Description

Estimate the difference in an outcome when compositional parts are substituted for specific unit(s). The substitution output encapsulates the substitution results for all compositional parts present in the brmcoda object.

Usage

substitution(
  object,
  delta,
  basesub = NULL,
  summary = TRUE,
  ref = c("grandmean", "clustermean"),
  level = c("between", "within", "aggregate"),
  weight = c("equal", "proportional"),
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
substitution(
  object,
  delta,
  basesub = NULL,
  summary = TRUE,
  ref = c("grandmean", "clustermean"),
  level = c("between", "within", "aggregate"),
  weight = c("equal", "proportional"),
  scale = c("response", "linear"),
  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 `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`summary`	A logical value. Should the estimate at each level of the reference grid (`FALSE`) or their average (`TRUE`) be returned? Default is `TRUE`. Only applicable for model with covariates in addition to the isometric log-ratio coordinates (i.e., adjusted model).
`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.
`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`.

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 between-person levels of variance
  fit1 <- brmcoda(complr = cilr,
                  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                     wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                  chain = 1, iter = 500, backend = "cmdstanr")
  sub1 <- substitution(object = fit1, delta = 5, level = c("between", "within"))

  # model with compositional predictor at aggregate level of variance
  fit2 <- brmcoda(complr = cilr,
                  formula = Stress ~ ilr1 + ilr2 + ilr3 + ilr4 + (1 | ID),
                  chain = 1, iter = 500, backend = "cmdstanr")
  sub2 <- substitution(object = fit2, delta = 5, level = c("aggregate"))

}
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 between-person levels of variance
  fit1 <- brmcoda(complr = cilr,
                  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                     wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                  chain = 1, iter = 500, backend = "cmdstanr")
  sub1 <- substitution(object = fit1, delta = 5, level = c("between", "within"))

  # model with compositional predictor at aggregate level of variance
  fit2 <- brmcoda(complr = cilr,
                  formula = Stress ~ ilr1 + ilr2 + ilr3 + ilr4 + (1 | ID),
                  chain = 1, iter = 500, backend = "cmdstanr")
  sub2 <- substitution(object = fit2, delta = 5, level = c("aggregate"))

}

Create a Summary of a fitted `brmsfit` model in a `brmcoda` object

Description

Create a Summary of a fitted brmsfit model in a brmcoda object

Usage

## S3 method for class 'brmcoda'
summary(object, ...)
## S3 method for class 'brmcoda'
summary(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Other arguments passed to `summary.brmsfit`.

Examples


if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                                 idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  summary(m)
}
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                                 idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  summary(m)
}

Create a Summary of a `complr` object

Description

Create a Summary of a complr object

Usage

## S3 method for class 'complr'
summary(object, weight = c("equal", "proportional"), ...)
## S3 method for class 'complr'
summary(object, weight = c("equal", "proportional"), ...)

Arguments

`object`	An object of class `complr`.
`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 is `equal`.
`...`	generic argument, not in use.

Examples

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

Create a Summary of a Substitution Model represented by a `substitution` object

Description

Create a Summary of a Substitution Model represented by a substitution object

Usage

## S3 method for class 'substitution'
summary(object, delta, to, from, ref, level, digits = 2, ...)
## S3 method for class 'substitution'
summary(object, delta, to, from, ref, level, digits = 2, ...)

Arguments

`object`	A `substitution` class object.
`delta`	A integer, numeric value or vector indicating the desired `delta` at which substitution results should be summarised. Default to all `delta` available in the `substitution` object.
`to`	A character value or vector specifying the names of the compositional parts that were reallocated to in the model.
`from`	A character value or vector specifying the names of the compositional parts that were reallocated from in the model.
`ref`	Either a character value or vector ((`"grandmean"` and/or `"clustermean"` or `"users"`), Default to all `ref` available in the `substitution` object.
`level`	A character string or vector (`"between"` and/or `"within"`). Default to all `level` available in the `substitution` object.
`digits`	A integer value used for number formatting. Default is `2`.
`...`	generic argument, not in use.

Value

A summary of substitution object.

`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. Either `between` or `within`.
`Reference`	Either `grandmean`, `clustermean`, or `users`.

Examples


if(requireNamespace("cmdstanr")){
  ## fit a model with compositional predictor at between and between-person levels
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  subm <- substitution(object = m, delta = 5)
  summary(subm)
}
if(requireNamespace("cmdstanr")){
  ## fit a model with compositional predictor at between and between-person levels
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  subm <- substitution(object = m, delta = 5)
  summary(subm)
}

Update `brmcoda` models

Description

This method allows for updating an existing brmcoda object.

Usage

## S3 method for class 'brmcoda'
update(object, formula. = NULL, newdata = NULL, newcomplr = NULL, ...)
## S3 method for class 'brmcoda'
update(object, formula. = NULL, newdata = NULL, newcomplr = NULL, ...)

Arguments

`object`	A fitted `brmcoda` object to be updated.
`formula.`	Changes to the formula; for details see `update.formula` and `brmsformula`.
`newdata`	A `data.frame` or `data.table` containing data of all variables used in the analysis. It must include a composition and the same ID variable as the existing `complr` object.
`newcomplr`	A `complr` object containing data of composition, ILR coordinates, and other variables used in the updated model.
`...`	Further arguments passed to `brm`.

Value

A brmcoda with two elements

`complr`	An object of class `complr` used in the `brm` model.
`model`	An object of class `brmsfit`, which contains the posterior draws along with many other useful information about the model.

Examples


if(requireNamespace("cmdstanr")){

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = complr(data = mcompd, sbp = sbp, 
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), 
                               idvar = "ID"), 
              formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID), 
              chain = 1, iter = 500,
              backend = "cmdstanr")

# removing the effect of wilr1
fit1 <- update(fit, formula. = ~ . - wilr1)

# using only a subset
fit2 <- update(fit, newdata = mcompd[ID != 1])
}
if(requireNamespace("cmdstanr")){

# model with compositional predictor at between and within-person levels
fit <- brmcoda(complr = complr(data = mcompd, sbp = sbp, 
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), 
                               idvar = "ID"), 
              formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                 wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID), 
              chain = 1, iter = 500,
              backend = "cmdstanr")

# removing the effect of wilr1
fit1 <- update(fit, formula. = ~ . - wilr1)

# using only a subset
fit2 <- update(fit, newdata = mcompd[ID != 1])
}

Update `complr`

Description

This method allows for updating an existing complr object.

Usage

## S3 method for class 'complr'
update(object, newdata, ...)
## S3 method for class 'complr'
update(object, newdata, ...)

Arguments

`object`	A `complr` class object to be updated.
`newdata`	A `data.frame` or `data.table` containing data of all variables used in the analysis. It must include a composition and the same ID variable as the existing `complr` object.
`...`	generic argument, not in use.

Value

A complr object with at least the following elements.

`comp`	A vector of class `acomp` representing one closed composition or a matrix of class `acomp` representing multiple closed compositions each in one row.
`between_comp`	A vector of class `acomp` representing one closed between-person composition or a matrix of class `acomp` representing multiple closed between-person compositions each in one row.
`within_comp`	A vector of class `acomp` representing one closed within-person composition or a matrix of class `acomp` representing multiple closed within-person compositions each in one row.
`logratio`	Log ratio transform of composition.
`between_logratio`	Log ratio transform of between-person composition.
`within_logratio`	Log ratio transform of within-person composition.
`data`	The user's dataset or imputed dataset if the input data contains zeros.
`transform`	Type of transform applied on compositional data.
`parts`	Names of compositional variables.
`idvar`	Name of the variable containing IDs.
`total`	Total amount to which the compositions is closed.

Examples

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

# update with new data
newdat <- mcompd[ID != 1] # excluding ID 1
cilr1 <- update(object = cilr, newdata = newdat)
cilr <- complr(data = mcompd, sbp = sbp, 
               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

# update with new data
newdat <- mcompd[ID != 1] # excluding ID 1
cilr1 <- update(object = cilr, newdata = newdat)

Variance of compositions presented in a `complr` object.

Description

Variance of compositions presented in a complr object.

Usage

var.complr(x, weight = c("equal", "proportional"), ...)
var.complr(x, weight = c("equal", "proportional"), ...)

Arguments

`x`	An object of class `complr`.
`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 is `equal`.
`...`	generic argument, not in use.

Examples

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

Extract Variance and Correlation Components

Description

Calculates the estimated standard deviations, correlations and covariances of the group-level terms of the brmsfit object in a brmcoda object.

Usage

## S3 method for class 'brmcoda'
VarCorr(x, ...)
## S3 method for class 'brmcoda'
VarCorr(x, ...)

Arguments

`x`	An object of class `brmcoda`.
`...`	Further arguments passed to `VarCorr.brmsfit`.

Value

A list of lists (one per grouping factor), each with three elements: a matrix containing the standard deviations, an array containing the correlation matrix, and an array containing the covariance matrix with variances on the diagonal.

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  VarCorr(m)
}
## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  VarCorr(m)
}

Covariance and Correlation Matrix of Population-Level Effects

Description

Get a point estimate of the covariance or correlation matrix of population-level parameters of the brmsfit object in a brmcoda object.

Usage

## S3 method for class 'brmcoda'
vcov(object, ...)
## S3 method for class 'brmcoda'
vcov(object, ...)

Arguments

`object`	An object of class `brmcoda`.
`...`	Further arguments passed to `vcov.brmsfit`.

Value

covariance or correlation matrix of population-level parameters

Examples


## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  vcov(m)
}
## fit a model
if(requireNamespace("cmdstanr")){
  m <- brmcoda(complr = complr(data = mcompd, sbp = sbp,
                               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                               idvar = "ID", total = 1440),
  formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
    wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")
  
  vcov(m)
}

Within-person Simple 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 a single reference composition (e.g., compositional mean at sample level). It is recommended that users run substitution model using the substitution function.

Usage

wsub(
  object,
  basesub,
  delta,
  summary = TRUE,
  ref = "grandmean",
  level = "within",
  weight = "equal",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
wsub(
  object,
  basesub,
  delta,
  summary = TRUE,
  ref = "grandmean",
  level = "within",
  weight = "equal",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)

Arguments

`object`	A fitted `brmcoda` object.
`basesub`	A `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`delta`	A integer, numeric value or vector indicating the amount of substituted change between compositional parts.
`summary`	A logical value. Should the estimate at each level of the reference grid (`FALSE`) or their average (`TRUE`) be returned? Default is `TRUE`. Only applicable for model with covariates in addition to the isometric log-ratio coordinates (i.e., adjusted model).
`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.
`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`.

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 <- wsub(object = m, basesub = psub, delta = 60)
}
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 <- wsub(object = m, basesub = psub, delta = 60)
}

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,
  ref = "clustermean",
  level = "within",
  weight = "proportional",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)
wsubmargins(
  object,
  delta,
  basesub,
  ref = "clustermean",
  level = "within",
  weight = "proportional",
  scale = c("response", "linear"),
  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 `data.frame` or `data.table` of the base possible substitution of compositional parts. This data set can be computed using function `basesub`. If `NULL`, all possible pairwise substitution of compositional parts are used.
`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.
`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`.

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)
}
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)
}

Package 'multilevelcoda'

Help Index

Extract Compositional Data from complr object.

Description

Usage

Arguments

Base Pairwise Substitution

Description

Usage

Arguments

Value

Examples

Bayes Factors from Marginal Likelihoods

Description

Usage

Arguments

See Also

Fit Bayesian generalised (non-)linear multilevel compositional model via full Bayesian inference

Description

Usage

Arguments

Value

Examples

Between-person Simple Substitution

Description

Usage

Arguments

Value

See Also

Examples

Between-person Average Substitution

Description

Usage

Arguments

Value

See Also

Examples

Reference Grid for substitution model.

Description

Usage

Arguments

Value

Model Coefficients

Description

Usage

Arguments

Value

See Also

Examples

Indices from a (dataset of) Multilevel Composition(s) (deprecated.)

Description

Usage

Arguments

Value

See Also

Indices from a (dataset of) Multilevel Composition(s)

Description

Usage

Arguments

Details

Value

Examples

Constructor function for substitution class.

Description

Usage

Arguments

Value

See Also

Extract Diagnostic Quantities from brmsfit Models in brmcoda

Description

Usage

Arguments

Value

See Also

Index brmcoda objects

Description

Usage

Arguments

See Also

Expected Values of the Posterior Predictive Distribution

Extract Compositional Data from `complr` object.

Reference Grid for `substitution` model.

Constructor function for `substitution` class.

Extract Diagnostic Quantities from `brmsfit` Models in `brmcoda`

Index `brmcoda` objects

Checks if argument is a `brmcoda` object

Checks if argument is a `complr` object

Checks if argument is a `substitution` object

Mean amounts and mean compositions presented in a `complr` object.

Create a matrix of output plots from a `brmcoda`'s `brmsfit` object