Package index • multilevelcoda

All functions

VarCorr(<brmcoda>): Extract Variance and Correlation Components

as.data.frame(<complr>) as.matrix(<complr>): Extract amounts and compositions in conventional formats as data.frames, matrices, or arrays.

bayes_factor(<brmcoda>): Bayes Factors from Marginal Likelihoods

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

bsub(): Between-person Simple Substitution

bsubmargin(): Between-person Average Substitution

build.base(): Build Base Pairwise Substitution

build.rg(): Reference Grid for substitution model.

build.sbp(): Build Sequential Binary Partition

coef(<brmcoda>): Model Coefficients

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

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

log_posterior(<brmcoda>) nuts_params(<brmcoda>) rhat(<brmcoda>) neff_ratio(<brmcoda>): Extract Diagnostic Quantities from brmsfit Models in brmcoda

variables(<brmcoda>) nvariables(<brmcoda>) niterations(<brmcoda>) nchains(<brmcoda>) ndraws(<brmcoda>): Index brmcoda objects

fitted(<brmcoda>): Expected Values of the Posterior Predictive Distribution

fixef(<brmcoda>): Population-Level Estimates

get-substitution: Substitution analysis helper functions

get_sbp(): Extract Sequential Binary Partition from a complr object.

get_variables(): Extract variable names from an object

is.brmcoda(): Checks if argument is a brmcoda object

is.complr(): Checks if argument is a complr object

is.substitution(): Checks if argument is a substitution object

launch_shinystan(<brmcoda>): Interface to shinystan

loo(<brmcoda>): Efficient approximate leave-one-out cross-validation (LOO)

mcmc_plot(<brmcoda>): MCMC Plots Implemented in bayesplot

mcompd: Multilevel Compositional Data

mean(<complr>): Mean amounts and mean compositions presented in a complr object.

model.frame(<brmcoda>): Extracting the Model Frame from a Formula or Fit from brmcoda object

multilevelcoda_sim(): multilevelcoda Simulation Study Results

nobs(<brmcoda>): Extract Number of Observations from brmcoda object

pairs(<brmcoda>): Create a matrix of output plots from a brmcoda's brmsfit object

pivot_coord(): Estimate pivot balance coordinates

pivot_coord_refit(): Estimate pivot balance coordinates by refitting model.

pivot_coord_rotate(): Estimate pivot balance coordinates by rotating sequential binary partition.

plot(<brmcoda>): Trace and Density Plots for MCMC Draws plot

plot(<substitution>): Substitution Plot

pp_check(<brmcoda>): Posterior Predictive Checks for brmcoda Objects

predict(<brmcoda>): Draws from the Posterior Predictive Distribution

print(<brmcoda>): Print a Summary for a fitted brmsfit model in a brmcoda object

print(<complr>): Print a Summary for a complr object

print(<substitution>): Print a Summary for a substitution object

prior_summary(<brmcoda>): Extract Priors of a brmsfit from a brmcoda object

psub: Possible Pairwise Substitutions

ranef(<brmcoda>): Group-Level Estimates

residuals(<brmcoda>): Posterior Draws of Residuals/Predictive Errors

sbp: Sequential Binary Partition

sim: multilevelcoda Simulation Study results

sub(): Simple Substitution

submargin(): Average Substitution

substitution(): Multilevel Compositional Substitution Analysis

summary(<brmcoda>): Create a Summary of a fitted brmsfit model in a brmcoda object

summary(<complr>): Create a Summary of a complr object

summary(<pivot_coord>): Create a Summary of a fitted brmsfit model from a pivot_coord object

summary(<substitution>): Create a Summary of a Substitution Model represented by a substitution object

update(<brmcoda>): Update brmcoda models

var(<complr>): Variance of compositions presented in a complr object.

vcov(<brmcoda>): Covariance and Correlation Matrix of Population-Level Effects

wsub(): Within-person Simple Substitution

wsubmargin(): Within-person Average Substitution