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Title:Bayesian Multilevel Mediation
Version:1.3.15
Date:2023-09-25
Description:Easy estimation of Bayesian multilevel mediation models with Stan.
URL:https://github.com/mvuorre/bmlm/
BugReports:https://github.com/mvuorre/bmlm/issues/
License:GPL (≥ 3)
LazyData:true
NeedsCompilation:yes
Depends:R (≥ 3.4.0), Rcpp (≥ 0.12.0)
Imports:ggplot2 (≥ 2.0.0), methods, RcppParallel (≥ 5.0.1), rstan(≥ 2.26.0), rstantools (≥ 2.3.1.1)
Suggests:qgraph, knitr, rmarkdown, reshape2, dplyr
LinkingTo:BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0),RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥2.26.0)
RoxygenNote:7.1.2
Encoding:UTF-8
SystemRequirements:GNU make
Biarch:true
Packaged:2023-09-25 11:42:03 UTC; matti
Author:Matti VuorreORCID iD [aut, cre]
Maintainer:Matti Vuorre <m.j.vuorre@tilburguniversity.edu>
Repository:CRAN
Date/Publication:2023-09-25 14:22:24 UTC

bmlm: Easy estimation of Bayesian multilevel mediation models with Stan.

Description

Seehttps://mvuorre.github.io/bmlm/ for a short tutorial.

Relationship between work stressors, work dissatisfaction,and relationship dissatisfaction.

Description

Simulated data from Intensive Longitudinal Methods:An Introduction to Diary and Experience Sampling Research.(Bolger, & Laurenceau, 2013, chapter 9;http://www.intensivelongitudinal.com/index.html).

Usage

data(BLch9)

Format

A data frame with 2100 rows and 8 variables:

id

ID of study participant

time

Time

fwkstrs

Number of work stressors

fwkdis

Work dissatisfaction rating

freldis

Relationship dissatisfaction

x

Subject-mean deviated number of work stressors

m

Subject-mean deviated work dissatisfaction rating

y

Subject-mean deviated relationship dissatisfaction

Source

http://www.intensivelongitudinal.com/datasets.html

Judgments of performance in a video game

Description

Data from an experiment where participants rated theirperformance in a video game in two conditions.(Experiment 1 in Metcalfe, Eich, & Castel, 2010;https://www.sciencedirect.com/science/article/pii/S0010027710001113).

Usage

data(MEC2010)

Format

A data frame with 344 rows and 4 variables:

subj

Subject id number.

lag

Lag condition (0 = no lag, 1 = 250ms lag).

hr

Hit rate.

jop

Judgment of Performance.

Source

Metcalfe, J., Eich, T. S., & Castel, A. D. (2010).Metacognition of agency across the lifespan.Cognition, 116(2), 267-282.doi:10.1016/j.cognition.2010.05.009

Create isolated within- (and optionally between-) person variables.

Description

Creates variables that represent pure within- and between-person predictors.

Usage

isolate(d = NULL, by = NULL, value = NULL, z = FALSE, which = "within")

Arguments

d

Adata.frame.

by

A vector of values ind by which the data is clustered.i.e. a vector of unique participant IDs.

value

Names of columns ind to isolate. Multiple values can begiven byvalue = c("var1", "var2", "var3")

z

Should the created values be standardized (defaults to FALSE).

which

Which component to return. "within" (default) returnswithin-person deviations only; "between" returns between-person means only;"both" returns both.

Value

Adata.frame with additional columns for the within- andbetween-person variables. The new columns are labelled _cw forcentered-within and _cb for centered-between.

Author(s)

Matti Vuorremv2521@columbia.edu

Examples

# Create within-person deviations of work stressors in BLch9.data(BLch9)BLch9 <- isolate(BLch9, by = "id", value = "fwkstrs")head(BLch9)  # Now has new column for within-person work stressors.

Estimate a multilevel mediation model

Description

Estimates a Bayesian multilevel mediation model using Stan.

Usage

mlm(  d = NULL,  id = "id",  x = "x",  m = "m",  y = "y",  priors = NULL,  binary_y = FALSE,  ...)

Arguments

d

Adata.frame or adata_frame.

id

Column of participant IDs indata.

x

Column of X values indata.

m

Column of M values indata.

y

Column of Y values indata.

priors

A list of named values to be used as the prior scaleparameters. See details.

binary_y

Set to TRUE if y is binary and should be modelledwith logistic regression. Defaults to FALSE (y treated as continuous.)This feature is experimental.

...

Other optional parameters passed torstan::stan().

Details

Draw samples from the joint posterior distribution of amultilevel mediation model using Stan.

Priors

Users may pass a list of named values for thepriors argument.The values will be used to define the scale parameter of therespective prior distributions.This list may specify some or all of the following parameters:

dy, dm

Regression intercepts (for Y and M as outcomes, respectively.)

a, b, cp

Regression slopes.

tau_x

Varying effects SDs for above parameters (e.g replace x with a.)

lkj_shape

Shape parameter for the LKJ prior.

See examples for specifying the following: Gaussian distributions with SD = 10as priors for the intercepts, Gaussians with SD = 2 for the slopes,Half-Cauchy distributions with scale parameters 1 for the varying effectsSDs, and an LKJ prior of 2.

Value

An object of S4 class stanfit, with all its available methods.

Author(s)

Matti Vuorremv2521@columbia.edu

Examples

## Not run: ## Run example from Bolger and Laurenceau (2013)data(BLch9)fit <- mlm(BLch9)mlm_summary(fit)### With priorsPriors <- list(dy = 10, dm = 10, a = 2, b = 2, cp = 2,               tau_dy = 1, tau_dm = 1, tau_a = 1, tau_b = 1, tau_cp = 1,               lkj_shape = 2)fit <- mlm(BLch9, priors = Priors)## End(Not run)

Plot estimated parameters of multilevel mediation model

Description

Plot the model's estimated parameters as histograms or a coefficient plot.

Usage

mlm_pars_plot(  mod = NULL,  type = "hist",  color = "black",  p_shape = 15,  p_size = 1.2,  level = 0.95,  nrow = 3,  pars = c("a", "b", "cp", "covab", "me", "c", "pme"))

Arguments

mod

A Stanfit model estimated withmlm().

type

Type of the plot,hist,coef, orviolin.

color

Color (and fill) for plots.

p_shape

Shape of points for coefplot.

p_size

Size of points for coefplot.

level

X level for Credible Intervals. (Defaults to .95.)

nrow

Number of rows for multiple histograms.

pars

Which parameters to plot.

Details

The point estimate for the coefficient plot is the posterior mean.

Value

A ggplot2 object.

Author(s)

Matti Vuorremv2521@columbia.edu

Plotbmlm's mediation model as a path diagram

Description

Plots a path diagram for an estimated multilevel mediation model.

Usage

mlm_path_plot(  mod = NULL,  xlab = "X",  ylab = "Y",  mlab = "M",  level = 0.95,  random = TRUE,  text = FALSE,  id = NULL,  digits = 2,  ...)

Arguments

mod

A Stanfit model estimated withmlm().

xlab

Label for X

ylab

Label for Y

mlab

Label for M

level

"Confidence" level for credible intervals. (Defaults to .95.)

random

Should the "random" effects SDs be displayed? (Default = TRUE)

text

Should additional parameter values be displayed?(Defaults to FALSE.)

id

Plot an individual-level path diagram by specifying ID number.

digits

Number of significant digits to show on graph. (Default = 2.)

...

Other arguments passed on toqgraph::qgraph().

Details

Plots a path diagram of the mediation model,with estimated parameter values and credible intervals. Can alsobe used to draw a template diagram of the mediation model by notspecifying input to themod argument.

To modify various settings of the underlying qgraph object, seeqgraph.

Value

A qgraph object.

Author(s)

Matti Vuorremv2521@columbia.edu

Examples

# Draw a template path diagram of the mediation modelmlm_path_plot()

Plot fitted values of M and Y from multilevel mediation model

Description

Plot population-level fitted values and Xvalues, for M and Y.

Usage

mlm_spaghetti_plot(  mod = NULL,  d = NULL,  id = "id",  x = "x",  m = "m",  y = "y",  level = 0.95,  n = 12,  binary_y = FALSE,  mx = "fitted",  fixed = TRUE,  random = TRUE,  h_jitter = 0,  v_jitter = 0,  bar_width = 0.2,  bar_size = 0.75,  n_samples = NA)

Arguments

mod

A multilevel mediation model estimated withmlm().

d

Adata.frame or adata_frame used in fitting model.

id

Name of id variable (identifying subjects) in data (d).

x

Name of X variable indata.

m

Name of M variable indata.

y

Name of Y variable indata.

level

X level for Credible Intervals. (Defaults to .95.)

n

Number of points along X to evaluate fitted values on.See details.

binary_y

Set to TRUE if the outcome variable (Y) is 0/1.

mx

Should the X axis of the M-Y figure be "fitted" values,or "data" values. Defaults to "fitted".

fixed

Should the population-level ("fixed") fitted values be shown?

random

Should the subject-level ("random") fitted values be shown?

h_jitter

Horizontal jitter of points. Defaults to 0.

v_jitter

Vertical jitter of points. Defaults to 0.

bar_width

Width of the error bars. Defaults to 0.2.

bar_size

Thickness of the error bars. Defaults to 0.75.

n_samples

Number of MCMC samples to use in calculating fitted values.See details.

Details

Ifn = 2, the fitted values will be represented as pointswith Xline with a Confidence Ribbon instead.If a very large model is fitted with a large number of MCMC iterations,the function might take a long time to run. In these cases, users can setn_samples to a smaller value (e.g. 1000), in which case the fittedvalues (and the CIs) will be based on a random subset ofn_samplesMCMC samples. The default value is NA, meaning that all MCMC samples areused.

Value

A list of two ggplot2 objects.

Author(s)

Matti Vuorremv2521@columbia.edu

Print a summary of the estimated multilevel mediation model

Description

Prints the estimated parameters (numerical summaries of the marginalposterior distributions).

Usage

mlm_summary(  mod = NULL,  level = 0.95,  pars = c("a", "b", "cp", "me", "c", "pme"),  digits = 2)

Arguments

mod

Astanfit object obtained frommlm()

level

"Confidence" level; Defines the limits of the credible intervals.Defaults to .95 (i.e. displays 95% CIs.)

pars

Parameters to summarize. Defaults to main average-levelparameters. See Details for more information.

digits

How many decimal points to display in the output. Defaults to 2.

Details

After estimating a model (drawing samples from the joint posteriorprobability distribution) withmlm(), show the estimated resultsby usingmlm_summary(fit), wherefit is an object containingthe fitted model.

The function shows, for each parameter specified withpars,the posterior mean, and limits of the Credible Interval as specifiedbylevel. For example,level = .91 shows a91% Credible Interval, which summarizes the central 91% mass ofthe marginal posterior distribution.

Parameters

By default,mlm() estimates and returns a large number of parameters,including the varying effects, and their associated standard deviations.However,mlm_summay() by default only displays a subset of theestimated parameters:

a

Regression slope of the X -> M relationship.

b

Regression slope of the M -> Y relationship.

cp

Regression slope of the X -> Y relationship. (Direct effect.)

me

Mediated effect (a * b + \sigma_{{a_j}{b_j}}).

c

Total effect of X on Y. (cp + me )

pme

Percent mediated effect.

The user may specifypars = NULL to display all estimated parameters.Other options include e.g.pars = "tau" to display the varyingeffects' standard deviations. To display all the group-level parameters(also known as random effects) only, specifypars = "random".With this argument,mlm_summary() prints the following parameters:

tau_a

Standard deviation of subject-levela_js.

tau_b

Standard deviation of subject-levelb_js.

tau_cp

Standard deviation of subject-levelc\'_js.

covab

Estimated covariance ofa_j andb_js.

corrab

Estimated correlation ofa_j andb_js.

To learn more about the additional parameters, refer to the Stan code(cat(get_stancode(fit))).

Value

Adata.frame summarizing the estimated multilevelmediation model:

Parameter

Name of parameter

Mean

Mean of parameter's posterior distribution.

Median

Median of parameter's posterior distribution.

SE

Standard deviation of parameter's posterior distribution.

ci_lwr

The lower limit of Credible Intervals.

ci_upr

The upper limit of Credible Intervals.

n_eff

Number of efficient samples.

Rhat

Should be 1.00.

Author(s)

Matti Vuorremv2521@columbia.edu

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