Package 'multilevelcoda'

Description

Internal function only.

Usage

.meanvar.complr(x, weight = c("equal", "proportional"), parts = 1, ...)

Arguments

Description

This is a constructor function for a complr object. It checks that the input list has the necessary structure and components to be considered a complr object, and if so, it assigns the class "complr" to it. This allows for method dispatch on complr objects.

Usage

as.complr(x)

Arguments

Value

A 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 = FALSE, ...)

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

Arguments

Description

This is a constructor function for a diagnostics object. It checks that the input list has the necessary structure and components to be considered a diagnostics object, and if so, it assigns the class "diagnostics" to it. This allows for method dispatch on diagnostics objects.

Usage

as.diagnostics(x)

Arguments

Value

A diagnostics object.

Description

Compute Bayes factors from marginal likelihoods

Usage

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

Arguments

See Also

bayes_factor.brmsfit

Description

Fit a brm model with multilevel ILR coordinates

Usage

brmcoda(complr, formula, ...)

Arguments

Value

A brmcoda with two elements

Examples

if(requireNamespace("cmdstanr")){
  x1 <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

  # inspect variables before passing to brmcoda
  get_variables(x1)

  ## model with compositional predictor at between and within-person levels
  m1 <- brmcoda(complr = x1,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## model with compositional outcome
  m2 <- brmcoda(complr = x1,
                formula = mvbind(z1_1, z2_1, z3_1, z4_1) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## model with compositional predictor and outcome
  x2 <- complr(data = mcompd,
                parts = list(c("TST", "WAKE"), c("MVPA", "LPA", "SB")),
                total = list(c(480), c(960)),
                idvar = "ID",
                transform = "ilr")

  m3 <- brmcoda(complr = x2,
                formula = mvbind(z1_2, z2_2) ~ z1_1 + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  }

Description

This function is an alias of substitution to estimates 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,
  ref = "grandmean",
  level = "between",
  summary = TRUE,
  aorg = TRUE,
  at = NULL,
  parts = 1,
  base,
  type = "one-to-one",
  weight = "equal",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)

Arguments

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.

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 between-person levels
m <- brmcoda(complr = cilr, 
             formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 + 
                                wz1_1 + wz2_1 + wz3_1 + wz4_1 + Female + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
subm <- bsub(object = m, base = psub, delta = 5)
}

Description

This function is an alias of substitution to estimates 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

bsubmargin(
  object,
  delta,
  ref = "clustermean",
  level = "between",
  summary = TRUE,
  at = NULL,
  parts = 1,
  base,
  type = "one-to-one",
  weight = "proportional",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)

Arguments

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.

See Also

substitution

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 + 
                                wz1_1 + wz2_1 + wz3_1 + wz4_1 + 
                                Female + (1 | ID), 
             chains = 1, iter = 500,
             backend = "cmdstanr")
             
subm <- bsubmargin(object = m, base = psub, delta = 5)
}

Description

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

Usage

build.base(parts, type = NULL)

Arguments

Value

A data table of all possible pairwise substitution.

Examples

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

ps2 <- build.base(c("WAKE", "MVPA", "LPA", "SB"), type = "one-to-all")
print(ps2)

Description

Build a dataset for fitted.brmcoda used in substitution model

Usage

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

Arguments

Value

A reference grid consisting of a combination of covariates in brmcoda

Description

Build a default sequential binary partition for complr object. The default sequential binary partition is a pivot balance that allows the effect of this first balance coordinate to be interpreted as the change in the prediction for the dependent variable when that given part increases while all remaining parts decrease by a common proportion.

Usage

build.sbp(parts)

Arguments

Value

A matrix sequential binary partition.

Examples

sbp1 <- build.sbp(parts = c("TST", "WAKE", "MVPA", "LPA", "SB"))
print(sbp1)

sbp2 <- build.sbp(c("WAKE", "MVPA", "LPA", "SB"))
print(sbp2)

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, ...)

Arguments

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.

See Also

coef.brmsfit

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                  wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                                  chain = 1, iter = 500,
                                  backend = "cmdstanr")

  ## extract population and group-level coefficients separately
  fixef(m)
  ranef(m)

  ## extract combined coefficients
  coef(m)
}

Description

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

Usage

compilr(...)

Arguments

Value

A complr object with at least the following elements.

See Also

complr

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")

Arguments

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.

Examples

x1 <- complr(data = mcompd,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID", total = 1440)
str(x1)

x2 <- complr(data = mcompd,
                parts = list(c("TST", "WAKE"), c("MVPA", "LPA", "SB")),
                total = list(c(480), c(960)),
                idvar = "ID",
                transform = "ilr")
str(x2)

x3 <- complr(data = mcompd, sbp = sbp,
                parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                idvar = "ID",
                 transform = "ilr")
str(x3)

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

Description

Create generator specifications for gamma and beta variables.

Usage

gen_gamma(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  ...,
  scale_fixed_intercept = NULL,
  random_cov = NULL
)

gen_beta(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  ...,
  scale_fixed_intercept = NULL,
  random_cov = NULL
)

Arguments

Value

An mlsim_generator_spec object for use in simulate_data().

See Also

Other predictor generators: count-generators, gen_categorical(), gen_custom(), gen_mvn(), gen_outcome(), gen_template()

Examples

sim <- simulate_data(
  n = 5,
  seed = 5,
  generators = list(
    positive = gen_gamma(
      "positive",
      fixed_intercept = log(1.5),
      scale_fixed_intercept = log(2)
    ),
    proportion = gen_beta(
      "proportion",
      fixed_intercept = stats::qlogis(0.4),
      scale_fixed_intercept = log(10)
    )
  )
)
sim$data

Description

Create generator specifications for binomial, Poisson, and negative binomial count variables.

Usage

gen_binomial(
  vars,
  level = c("single", "level2", "multilevel"),
  size = NULL,
  fixed_intercept = NULL,
  random_cov = NULL
)

gen_poisson(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  random_cov = NULL
)

gen_negbin(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  ...,
  scale_fixed_intercept = NULL,
  random_cov = NULL
)

Arguments

Details

Count generators use link-scale intercepts at every level. Negative-binomial size is exp(scale_fixed_intercept).

Value

An mlsim_generator_spec object for use in simulate_data().

See Also

Other predictor generators: continuous-generators, gen_categorical(), gen_custom(), gen_mvn(), gen_outcome(), gen_template()

Examples

sim <- simulate_data(
  n = 5,
  seed = 4,
  generators = list(
    events = gen_poisson("events", fixed_intercept = log(2)),
    successes = gen_binomial(
      "successes",
      size = 4,
      fixed_intercept = stats::qlogis(0.5)
    ),
    overdispersed = gen_negbin(
      "overdispersed",
      fixed_intercept = log(2),
      scale_fixed_intercept = log(3)
    )
  )
)
sim$data

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, ...)

Arguments

Value

The exact form of the output depends on the method.

See Also

log_posterior.brmsfit

nuts_params.brmsfit

rhat.brmsfit

neff_ratio.brmsfit

Description

This is a generic function for diagnostics methods in multilevelcoda. See diagnostics.complr for the method for complr objects.

Usage

diagnostics(x, ...)

Arguments

Value

diagnostics class object

Description

Uses robust minimum covariance determinant estimates to calculate squared Mahalanobis distances for the total, between, or within log-ratio coordinates stored in a complr object. This can be used to identify extreme values (outliers).

Usage

## S3 method for class 'complr'
diagnostics(
  x,
  level = c("total", "between", "within"),
  parts = 1,
  ev.perc = 0.001,
  extremevalues = c("theoretical", "empirical"),
  ...
)

Arguments

Value

A diagnostics object with the raw composition matrix in x, a data.table of robust distances in distance, the extreme-value cutoff in cutoff, the requested upper-tail proportion in ev.perc, the cutoff type in extremevalues, and the diagnosed coordinate level in levels. The distance element includes an is_extremevalue column indicating whether each fitted observation is flagged as an extreme value. For level = "between", diagnostics are fitted using one observation per idvar.

Examples

data(mcompd)
data(sbp)

ids <- unique(mcompd$ID)[1:20]
cilr <- complr(
  data = mcompd[ID %in% ids, .SD[1:3], by = ID],
  sbp = sbp,
  parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
  idvar = "ID",
  total = 1440
)

# One diagnostic object per coordinate level
total_diag <- diagnostics(cilr, level = "total")
between_diag <- diagnostics(cilr, level = "between")
within_diag <- diagnostics(cilr, level = "within")

# Use an empirical cutoff and a larger tail proportion
empirical_diag <- diagnostics(
  cilr,
  level = "between",
  ev.perc = .05,
  extremevalues = "empirical"
)

is.diagnostics(between_diag)
head(between_diag$distance)

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)

Arguments

See Also

variables.brmsfit

nvariables.brmsfit

niterations.brmsfit

nchains.brmsfit

ndraws.brmsfit

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"), parts = 1, summary = TRUE, ...)

Arguments

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.

See Also

fitted.brmsfit

Examples

## fit a model
if(requireNamespace("cmdstanr")){
  ## compute composition and ilr coordinates
  x <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)

  ## fit a model
  m1 <- brmcoda(complr = x,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (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 = x,
                formula = mvbind(z1_1, z2_1, z3_1, z4_1) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## expected predictions on compositional scale
  epredcomp <- fitted(m2, scale = "response")
  head(epredcomp)
}

Description

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

Usage

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

Arguments

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.

See Also

fixef.brmsfit

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                  wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                                  chain = 1, iter = 500,
                                  backend = "cmdstanr")

  ## extract population-Level coefficients
  fixef(m)
}

Description

Create a generator specification for binary or multicategory categorical variables.

Usage

gen_categorical(
  vars,
  level = c("single", "level2", "multilevel"),
  categories = NULL,
  fixed_intercept = NULL,
  random_cov = NULL,
  reference = NULL,
  output = c("factor", "character", "integer"),
  ordered = FALSE
)

Arguments

Value

An mlsim_generator_spec object for use in simulate_data().

See Also

Other predictor generators: continuous-generators, count-generators, gen_custom(), gen_mvn(), gen_outcome(), gen_template()

Examples

sim <- simulate_data(
  n = 6,
  seed = 3,
  generators = list(
    arm = gen_categorical(
      "arm",
      categories = c("control", "treatment"),
      fixed_intercept = stats::qlogis(0.5)
    )
  )
)
sim$data

Description

Wrap a custom generator function so it can participate in simulate_data().

Usage

gen_custom(
  vars,
  generator,
  level = c("single", "level2", "multilevel"),
  ...,
  metadata = list()
)

Arguments

Value

An mlsim_generator_spec object for use in simulate_data().

See Also

Other predictor generators: continuous-generators, count-generators, gen_categorical(), gen_mvn(), gen_outcome(), gen_template()

Examples

constant_generator <- function(context, vars, level, value = 1) {
  list(data = data.frame(value = rep(value, context$n_rows)), names = vars)
}

sim <- simulate_data(
  n = 3,
  generators = list(x = gen_custom("x", constant_generator, value = 7))
)
sim$data

Description

Create a generator specification for univariate or multivariate Gaussian variables, optionally transformed from ILR coordinates to compositional parts.

Usage

gen_mvn(
  vars,
  level = c("single", "level2", "multilevel"),
  fixed_intercept = NULL,
  residual_cov = NULL,
  random_cov = NULL,
  ...,
  scale_fixed_intercept = NULL,
  residual_cor = NULL,
  compositional = FALSE,
  parts = NULL,
  total = 1,
  keep_ilr = TRUE,
  sbp = NULL
)

Arguments

Details

Compositional MVN generators simulate ILR coordinates first, then use the SBP basis to transform the coordinates into positive composition parts that sum to total.

Value

An mlsim_generator_spec object for use in simulate_data().

See Also

Other predictor generators: continuous-generators, count-generators, gen_categorical(), gen_custom(), gen_outcome(), gen_template()

Examples

sim <- simulate_data(
  n = 4,
  seed = 2,
  generators = list(
    x = gen_mvn("x", fixed_intercept = 0, residual_cov = 1),
    z = gen_mvn(
      c("z1", "z2"),
      fixed_intercept = c(0, 0),
      residual_cov = diag(2),
      compositional = TRUE,
      parts = c("sleep", "activity", "sedentary")
    )
  )
)
sim$data
rowSums(as.matrix(sim$data[, c("sleep", "activity", "sedentary"), with = FALSE]))

Description

Create an outcome generator for simulate_data(). Outcomes are simulated on a Gaussian scale, optionally as ILR coordinates that are back-transformed to strictly positive compositions. If ar1() appears in the location formula, it defines a residual VAR(1) process, not an observed lagged-outcome predictor.

Usage

gen_outcome(
  formula,
  scale,
  params,
  burnin,
  composition = list(parts = NULL, total = 24, sbp = NULL, keep_ilr = TRUE),
  ar_stability = c("resample", "shrink", "error"),
  max_stability_attempts = 1000,
  shrink_target_radius = 0.98
)

Arguments

Value

An mlsim_generator_spec object for use in simulate_data().

See Also

Other predictor generators: continuous-generators, count-generators, gen_categorical(), gen_custom(), gen_mvn(), gen_template()

Examples

beta_location <- matrix(
  c(0, 0, 0.2, -0.1),
  nrow = 2,
  dimnames = list(c("(Intercept)", "treatmenttreatment"), c("ilr1", "ilr2"))
)
beta_scale <- matrix(
  log(c(0.4, 0.35)),
  nrow = 1,
  dimnames = list("(Intercept)", c("ilr1", "ilr2"))
)
beta_ar <- array(
  c(0.25, 0.02, -0.01, 0.2),
  dim = c(1, 2, 2),
  dimnames = list("ar1()", c("ilr1", "ilr2"), c("ilr1", "ilr2"))
)
corr <- diag(2)
dimnames(corr) <- list(c("ilr1", "ilr2"), c("ilr1", "ilr2"))

sim <- simulate_data(
  n_groups = 4,
  n_per_group = 4,
  group_id = "ID",
  time_id = "day",
  seed = 1,
  generators = list(
    treatment = gen_categorical(
      "treatment",
      level = "level2",
      categories = c("control", "treatment"),
      fixed_intercept = stats::qlogis(0.5)
    ),
    outcome = gen_outcome(
      mvbind(ilr1, ilr2) ~ treatment + ar1(),
      scale = sigma ~ 1,
      params = list(
        location = list(beta = beta_location),
        scale = list(beta = beta_scale, correlation = corr),
        ar = list(beta = beta_ar)
      ),
      burnin = 10,
      composition = list(parts = c("sleep", "active", "sedentary"), total = 24)
    )
  )
)
head(sim$data)

Description

Build a generator that parses a gen_outcome() specification and records a complete parameter template without simulating outcome values. Use this inside simulate_data() after any generators that define predictors used by the outcome formula, especially predictors referenced by between() or within().

Usage

gen_template(
  formula,
  scale,
  burnin,
  composition = list(parts = NULL, total = 24, sbp = NULL, keep_ilr = TRUE),
  ar_stability = c("resample", "shrink", "error"),
  max_stability_attempts = 1000,
  shrink_target_radius = 0.98
)

Arguments

Details

The generated template is stored at sim$generator_metadata[[name]]$params, where name is the name given to this generator in the generators list. The template must be created with the same simulation design, previous generators, factor levels, formula, scale formula, and composition settings that will be used for the later gen_outcome() call.

Template values are initialized to zero for regression, AR, and group-level covariance parameters, and to identity for residual ILR correlations. The object is ready to edit and pass as params to gen_outcome().

Value

An mlsim_generator_spec object for use in simulate_data(). It emits no data columns and stores the parameter template in generator metadata.

See Also

Other predictor generators: continuous-generators, count-generators, gen_categorical(), gen_custom(), gen_mvn(), gen_outcome()

Examples

template_sim <- simulate_data(
  n_groups = 3,
  n_per_group = 2,
  group_id = "ID",
  seed = 1,
  generators = list(
    treatment = gen_categorical(
      "treatment",
      level = "level2",
      categories = c("control", "treatment"),
      fixed_intercept = stats::qlogis(0.5)
    ),
    outcome_template = gen_template(
      mvbind(ilr1, ilr2) ~ treatment,
      scale = sigma ~ 1,
      burnin = 0,
      composition = list(parts = c("sleep", "active", "sedentary"), total = 24)
    )
  )
)

params <- template_sim$generator_metadata$outcome_template$params
params$location$beta["treatmenttreatment", "ilr1"] <- 0.2

Description

Extract Sequential Binary Partition from a complr object.

Usage

get_sbp(object)

Arguments

Description

Generic function to extract variable names from a supported object.

Usage

get_variables(object)

## S3 method for class 'complr'
get_variables(object)

## S3 method for class 'brmcoda'
get_variables(object)

Arguments

Value

A list of variable names.

Examples

# For a complr object:
# get_variables(complr_object)

# For a brmcoda object:
# get_variables(brmcoda_object)

Description

Functions used only internally to estimate substitution model

Description

Checks if argument is a brmcoda object

Usage

is.brmcoda(x)

Arguments

Description

Checks if argument is a complr object

Usage

is.complr(x)

Arguments

Description

Checks if argument is a diagnostics object

Usage

is.diagnostics(x)

Arguments

Description

Checks if argument is a substitution object

Usage

is.substitution(x)

Arguments

Description

Provide an interface to shinystan for models fitted with brms

Usage

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

Arguments

Value

An S4 shinystan object

See Also

launch_shinystan

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, ...)

Arguments

Value

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

See Also

loo.brmsfit

Description

Call MCMC plotting functions implemented in the bayesplot package.

Usage

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

Arguments

Value

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

See Also

mcmc_plot.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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
               chain = 1, iter = 500)
mcmc_plot(fit)

## End(Not run)

Description

A simulated dataset containing multiple days of compositional data.

Usage

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

Description

Mean amounts and mean compositions presented in a complr object.

Usage

## S3 method for class 'complr'
mean(x, weight = c("equal", "proportional"), parts = 1, ...)

Arguments

Examples

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

Description

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

Usage

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

Arguments

Description

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

Usage

multilevelcoda_sim()

Value

An S4 shiny object

Description

Extract Number of Observations from brmcoda object

Usage

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

Arguments

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, ...)

Arguments

See Also

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
               chain = 1, iter = 500)
pairs(fit)

## End(Not run)

Description

This function estimates pivot balance coordinates for each compositional part by either "rotate" the sequential binary partition using the same brmcoda object or "refit" the brmcoda object.

Usage

pivot_coord(
  object,
  summary = TRUE,
  method = c("rotate", "refit"),
  parts = 1,
  ...
)

Arguments

Value

Estimated pivot balance coordinates representing the effect of increasing one compositional part relative to the remaining compositional parts.

Examples

if(requireNamespace("cmdstanr")){
  x <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID",
                 total = 1440)
  
  # inspects ILRs before passing to brmcoda
  names(x$between_logratio)
  names(x$within_logratio)
  names(x$logratio)
  
  # model with compositional predictor at between and within-person levels
  m <- brmcoda(complr = x,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  m_pivot_coord <- pivot_coord(m)
  summary(m_pivot_coord)
  }

Description

This function is an alias of pivot_coord to estimates the pivot balance coordinates by "refit" the brmcoda object.

Usage

pivot_coord_refit(object, summary = TRUE, parts = 1, ...)

Arguments

Value

Estimated pivot balance coordinates representing the effect of increasing one compositional part relative to the remaining compositional parts.

See Also

pivot_coord

Examples

if(requireNamespace("cmdstanr", "brms")){
  x <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID",
                 total = 1440)
  
  m <- brmcoda(complr = x,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  m_pivot_coord_refit <- pivot_coord_refit(m)
  summary(m_pivot_coord_refit)
  
  m_pivot_coord_raw <-  pivot_coord_refit(m, summary = FALSE)
  lapply(m_pivot_coord_raw$output, brms::posterior_summary)
  
  }

Description

This function is an alias of pivot_coord to estimates the pivot balance coordinates by "rotate" the sequential binary partition on the same brmcoda object.

Usage

pivot_coord_rotate(object, summary = TRUE, parts = 1, ...)

Arguments

Value

Estimated pivot balance coordinates representing the effect of increasing one compositional part relative to the remaining compositional parts.

See Also

pivot_coord

Examples

if(requireNamespace("cmdstanr")){
  x <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID",
                 total = 1440)
  
  m <- brmcoda(complr = x,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  m_pivot_coord_rotate <- pivot_coord_rotate(m)
  summary(m_pivot_coord_rotate)
  
  m_pivot_coord_raw <-  pivot_coord_rotate(m, summary = FALSE)
  lapply(m_pivot_coord_raw$output, brms::posterior_summary)
  
  }

Description

Make a plot of brmcoda model results.

Usage

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

Arguments

Value

An invisible list of gtable objects.

See Also

plot.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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
               chain = 1, iter = 500)
plot(fit)

## End(Not run)

Description

Make a scatterplot matrix of diagnostics results from a complr object, with density and rug plots on the diagonal.

Usage

## S3 method for class 'diagnostics'
plot(x, transform = c("clr", "raw"), ...)

Arguments

Value

A ggplot graph object showing pairwise scatterplots for each composition part, with point colour indicating extreme value status. Diagonal panels show marginal densities and rug marks for observations flagged as extreme values.

Examples

data(mcompd)
data(sbp)

ids <- unique(mcompd$ID)[1:20]
cilr <- complr(
  data = mcompd[ID %in% ids, .SD[1:3], by = ID],
  sbp = sbp,
  parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
  idvar = "ID",
  total = 1440
)

between_diag <- diagnostics(cilr, level = "between", ev.perc = .05)

# Plot centered logratio transformed composition values
plot(between_diag)

# Plot on the raw composition scale
plot(between_diag, transform = "raw")

Description

Make a plot of substitution model results.

Usage

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

Arguments

Value

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

Description

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

Usage

## S3 method for class 'brmcoda'
pp_check(
  object,
  type = "dens_overlay",
  ndraws = NULL,
  prefix = c("ppc", "ppd"),
  newdata = NULL,
  draw_ids = NULL,
  nsamples = NULL,
  subset = NULL,
  scale = c("linear", "response"),
  parts = NULL,
  ...
)

Arguments

See Also

pp_check.brmsfit

Examples

if(requireNamespace("cmdstanr")){
  ## fit a model
  x <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)

  m1 <- brmcoda(complr = x,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## posterior predictive checks
  bayesplot::pp_check(m1, ndraws = 5)

  ## fit a model with compositional outcome
  m2 <- brmcoda(complr = x,
                formula = mvbind(z1_1, z2_1, z3_1, z4_1) ~ 
                          bz1_1 + bz2_1 + bz3_1 + bz4_1 + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## posterior predictive checks for compositional outcome -- linear scale
  bayesplot::pp_check(m2, resp = "z11", ndraws = 10)
  ## posterior predictive checks for compositional outcome -- original response scale
  bayesplot::pp_check(m2, parts = "WAKE", scale = "response", ndraws = 10)
}

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"), parts = 1, ...)

Arguments

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.

See Also

predict.brmsfit

Examples

if(requireNamespace("cmdstanr")){
  ## fit a model
  x <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
                 idvar = "ID", total = 1440)

  m1 <- brmcoda(complr = x,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## predicted responses
  pred <- predict(m1)
  head(pred)

  ## fit a model with compositional outcome
  m2 <- brmcoda(complr = x,
                formula = mvbind(z1_1, z2_1, z3_1, z4_1) ~ 
                          bz1_1 + bz2_1 + bz3_1 + bz4_1 + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## predicted responses on ilr scale
  predilr <- predict(m2, scale = "linear")
  head(predilr)

  ## predicted responses on compositional scale
  predcomp <- predict(m2, scale = "response")
  head(predcomp)
}

Description

Convert an simulate_data() result with a gen_outcome() generator into an analysis-ready data set and inferred brms formula. The helper recomputes observed between- and within-person predictors from the raw simulated columns, creates within-person centered lag columns for ar1() terms, and creates a complr() object when the outcome is compositional.

Usage

prep_sim_analysis(sim, outcome = NULL, drop_lag_na = FALSE)

Arguments

Details

The analysis formula is inferred from the stored gen_outcome() formula. Simulation terms between(x) and within(x) become observed-data columns named x_between and x_within, computed from x by the simulation group identifier. These columns are recomputed even when columns with the same names already exist in sim$data.

For dynamic formulas, ar1() is translated to within-person centered lag predictors. For compositional outcomes, the helper rebuilds the ILR coordinates through complr() using the simulator's parts and SBP metadata, then lags the generated z coordinates used by brmcoda().

Value

An mlsim_analysis object, a list with:

Examples

params <- list(
  location = list(beta = matrix(0, nrow = 1, dimnames = list("(Intercept)", "y"))),
  scale = list(beta = matrix(log(0.2), nrow = 1, dimnames = list("(Intercept)", "y")))
)
sim <- simulate_data(
  n = 5,
  seed = 1,
  generators = list(
    outcome = gen_outcome(
      y ~ 1,
      scale = sigma ~ 1,
      params = params,
      burnin = 0
    )
  )
)
analysis <- prep_sim_analysis(sim)
analysis$formula

Description

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

Usage

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

Arguments

See Also

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                     wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")

 print(m)
}

Description

Print a Summary for a complr object

Usage

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

Arguments

See Also

summary.complr

Examples

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

Description

Print a compact overview of an mlsim_data object.

Usage

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

Arguments

Value

x, invisibly.

Examples

sim <- simulate_data(
  n = 3,
  generators = list(
    x = gen_mvn("x", fixed_intercept = 0, residual_cov = 1)
  )
)
print(sim)

Description

Print a Summary for a substitution object

Usage

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

Arguments

See Also

summary.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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                     wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")

  subm <- substitution(object = m, delta = 5)
  print(subm)
}

Description

Print a Simulated Data Summary

Usage

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

Arguments

Value

x, invisibly.

Description

Compute Bayes factors from marginal likelihoods

Usage

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

Arguments

See Also

prior_summary.brmsfit

Description

A dataset containing possible pairwise subsitutions.

Usage

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

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, ...)

Arguments

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.

See Also

ranef.brmsfit

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                  wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                                  chain = 1, iter = 500,
                                  backend = "cmdstanr")

  ## extract group-level coefficients
  ranef(m)
}

Description

Compute posterior draws of residuals/predictive errors

Usage

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

Arguments

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.

See Also

residuals.brmsfit

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                  wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                                  chain = 1, iter = 500,
                                  backend = "cmdstanr")

  ## extract residuals
  res <- residuals(m)
  head(res)
}

Description

A matrix of sequential binary partition.

Usage

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

Description

A list of 4 components

Usage

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

Description

Build a single-level, grouped, or longitudinal simulation design and evaluate generator specifications into one shared data table.

Usage

simulate_data(
  generators,
  n = NULL,
  n_groups = NULL,
  n_per_group = NULL,
  group_id = "group_id",
  obs_id = "obs_id",
  time_id = NULL,
  time_values = NULL,
  time_truncate = TRUE,
  seed = NULL
)

Arguments

Details

Generators are evaluated in list order. Each generator can depend on columns produced by earlier generators through the simulation context. Generated column names must be unique and must not collide after make.names().

Value

An object of class mlsim_data, a list with:

data

A data.table::data.table() containing design columns and generated variables.

metadata

Simulation design metadata, seed, index metadata, and generator order.

generator_specs

Generator specifications with simulator closures removed.

generator_metadata

Per-generator metadata recorded during simulation.

Examples

sim <- simulate_data(
  n_groups = 3,
  n_per_group = 2,
  seed = 10,
  generators = list(
    group_x = gen_mvn("group_x", level = "level2", fixed_intercept = 0, residual_cov = 1),
    y = gen_poisson("y", fixed_intercept = log(2))
  )
)
sim$data
summary(sim)

longitudinal <- simulate_data(
  n_groups = 2,
  n_per_group = 3,
  time_id = "visit",
  generators = list(x = gen_mvn("x", fixed_intercept = 0, residual_cov = 1))
)
longitudinal$metadata$index

Description

This function is an alias of substitution to estimates 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,
  ref = "grandmean",
  level = "aggregate",
  summary = TRUE,
  aorg = TRUE,
  at = NULL,
  parts = 1,
  base,
  type = "one-to-one",
  weight = "equal",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)

Arguments

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.

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 ~ z1_1 + z2_1 + z3_1 + z4_1 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
             
subm <- sub(object = m, base = psub, delta = 5)
}

Description

This function is an alias of substitution to estimates 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

submargin(
  object,
  delta,
  ref = "clustermean",
  level = "aggregate",
  summary = TRUE,
  at = NULL,
  parts = 1,
  base,
  type = "one-to-one",
  weight = "proportional",
  scale = c("response", "linear"),
  cores = NULL,
  ...
)

Arguments

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.

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 ~ z1_1 + z2_1 + z3_1 + z4_1 + (1 | ID), 
             chain = 1, iter = 500,
             backend = "cmdstanr")
                     
subm <- submargin(object = m, base = psub, delta = 5)
}

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,
  ref = c("grandmean", "clustermean"),
  level = c("between", "within", "aggregate"),
  summary = TRUE,
  at = NULL,
  parts = 1,
  base,
  type,
  weight = c("equal", "proportional"),
  scale = c("response", "linear"),
  aorg = NULL,
  cores = NULL,
  ...
)

Arguments

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.

Examples

if(requireNamespace("cmdstanr")){
  x <- 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
  m1 <- brmcoda(complr = x,
                  formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                     wz1_1 + wz2_1 + wz3_1 + wz4_1 +
                                     Female + (1 | ID),
                  chain = 1, iter = 500, backend = "cmdstanr")

  # one to one reallocation at between and within-person levels
  sub1 <- substitution(object = m1, delta = 5, level = c("between"))
  summary(sub1)

  # one to all reallocation at between and within-person levels
  sub2 <- substitution(object = m1, delta = 5, level = c("between", "within"),
                       type = "one-to-all")
  summary(sub2)

  # model with compositional predictor at aggregate level
  m2 <- brmcoda(complr = x,
                  formula = Stress ~ z1_1 + z2_1 + z3_1 + z4_1 + (1 | ID),
                  chain = 1, iter = 500, backend = "cmdstanr")
  sub3 <- substitution(object = m2, delta = 5, level = c("aggregate"))

}

Description

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

Usage

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

Arguments

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 ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                     wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
  chain = 1, iter = 500,
  backend = "cmdstanr")

  summary(m)
}

Description

Create a Summary of a complr object

Usage

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

Arguments

Examples

x1 <- complr(data = mcompd, sbp = sbp,
               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"),
               idvar = "ID")
summary(x1)
x2 <- complr(data = mcompd, sbp = sbp,
               parts = c("TST", "WAKE", "MVPA", "LPA", "SB"))
summary(x2)