Function reference • manymome

Main Functions

Compute conditional indirect effects, conditional effects, and indirect effects.

cond_indirect() cond_indirect_effects() indirect_effect() many_indirect_effects(): Conditional, Indirect, and Conditional Indirect Effects

all_indirect_paths() all_paths_to_df(): Enumerate All Indirect Effects in a Model

indirect_effects_from_list(): Coefficient Table of an 'indirect_list' Class Object

total_indirect_effect(): Total Indirect Effect Between Two Variables

Presenting and Exploring the Effects

index_of_mome() index_of_momome(): Index of Moderated Mediation and Index of Moderated Moderated Mediation

cond_indirect_diff(): Differences In Conditional Indirect Effects

plot(<cond_indirect_effects>): Plot Conditional Effects

plot_effect_vs_w(): Plot an Effect Against a Moderator

pseudo_johnson_neyman() print(<pseudo_johnson_neyman>): Pseudo Johnson-Neyman Probing

indirect_proportion(): Proportion of Effect Mediated

delta_med(): Delta_Med by Liu, Yuan, and Li (2023)

Moderator Levels

Set the levels of moderators to be used in cond_indirect_effects() and cond_indirect().

mod_levels() mod_levels_list(): Create Levels of Moderators

merge_mod_levels(): Merge the Generated Levels of Moderators

Bootstrapping

Generate bootstrap estimates to be used by the main functions to form bootstrap confidence intervals

do_boot(): Bootstrap Estimates for 'indirect_effects' and 'cond_indirect_effects'

lm2boot_out() lm2boot_out_parallel(): Bootstrap Estimates for lm Outputs

fit2boot_out() fit2boot_out_do_boot(): Bootstrap Estimates for a lavaan Output

Monte Carlo

Generate simulated estimates to be used by the main functions to form Monte Carlo confidence intervals

do_mc() gen_mc_est(): Monte Carlo Estimates for 'indirect_effects' and 'cond_indirect_effects'

fit2mc_out(): Monte Carlo Estimates for a lavaan Output

For ‘lavaan’

factor2var(): Create Dummy Variables

For ‘lm’

lm2list(): Join 'lm()' Output to Form an 'lm_list`-Class Object

Methods

Methods and utility functions for the output of the main functions.

coef(<indirect>): Extract the Indirect Effect or Conditional Indirect Effect

confint(<indirect>): Confidence Interval of Indirect Effect or Conditional Indirect Effect

print(<indirect>): Print an 'indirect' Class Object

`+`(<indirect>) `-`(<indirect>): Math Operators for 'indirect'-Class Objects

coef(<indirect_list>): Extract the Indirect Effects from a 'indirect_list' Object

confint(<indirect_list>): Confidence Intervals of Indirect Effects in an 'indirect_list' Object

print(<indirect_list>): Print an 'indirect_list' Class Object

coef(<cond_indirect_effects>): Estimates of Conditional Indirect Effects or Conditional Effects

confint(<cond_indirect_effects>): Confidence Intervals of Indirect Effects or Conditional Indirect Effects

print(<cond_indirect_effects>): Print a 'cond_indirect_effects' Class Object

`[`(<cond_indirect_effects>): Extraction Methods for 'cond_indirect_effects' Outputs

get_one_cond_indirect_effect() get_one_cond_effect(): Get The Conditional Indirect Effect for One Row of 'cond_indirect_effects' Output

coef(<cond_indirect_diff>): Print the Output of 'cond_indirect_diff()'

confint(<cond_indirect_diff>): Confidence Interval of the Output of 'cond_indirect_diff()'

print(<cond_indirect_diff>): Print the Output of 'cond_indirect_diff'

print(<lm_list>): Print an lm_list-Class Object

summary(<lm_list>) print(<summary_lm_list>): Summary of an lm_list-Class Object

`[`(<wlevels>) `[<-`(<wlevels>) `[[<-`(<wlevels>): Extraction Methods for a 'wlevels'-class Object

print(<all_paths>): Print 'all_paths' Class Object

print(<boot_out>): Print a boot_out-Class Object

print(<mc_out>): Print a mc_out-Class Object

print(<indirect_proportion>): Print an 'indirect_proportion'-Class Object

coef(<indirect_proportion>): Extract the Proportion of Effect Mediated

print(<delta_med>): Print a 'delta_med' Class Object

coef(<delta_med>): Delta_Med in a 'delta_med'-Class Object

confint(<delta_med>): Confidence Interval for Delta_Med in a 'delta_med'-Class Object

Advanced helpers

Helper functions exported for advanced users.

indirect_i(): Indirect Effect (No Bootstrapping)

check_path(): Check a Path Exists in a Model

lm_from_lavaan_list(): 'lavaan'-class to 'lm_from_lavaan_list'-Class

coef(<lm_from_lavaan>): Coefficients of an 'lm_from_lavaan'-Class Object

terms(<lm_from_lavaan>): Model Terms of an 'lm_from_lavaan'-Class Object

predict(<lm_from_lavaan>): Predicted Values of a 'lm_from_lavaan'-Class Object

predict(<lm_from_lavaan_list>): Predicted Values of an 'lm_from_lavaan_list'-Class Object

predict(<lm_list>): Predicted Values of an 'lm_list'-Class Object

get_prod(): Product Terms (if Any) Along a Path

Datasets

Datasets used in examples.

data_med: Sample Dataset: Simple Mediation

data_med_complicated: Sample Dataset: A Complicated Mediation Model

data_med_complicated_mg: Sample Dataset: A Complicated Mediation Model With Two Groups

data_med_mg: Sample Dataset: Simple Mediation With Two Groups

data_med_mod_a: Sample Dataset: Simple Mediation with a-Path Moderated

data_med_mod_ab: Sample Dataset: Simple Mediation with Both Paths Moderated (Two Moderators)

data_med_mod_ab1: Sample Dataset: Simple Mediation with Both Paths Moderated By a Moderator

data_med_mod_b: Sample Dataset: Simple Mediation with b-Path Moderated

data_med_mod_b_mod: Sample Dataset: A Simple Mediation Model with b-Path Moderated-Moderation

data_med_mod_parallel: Sample Dataset: Parallel Mediation with Two Moderators

data_med_mod_parallel_cat: Sample Dataset: Parallel Moderated Mediation with Two Categorical Moderators

data_med_mod_serial: Sample Dataset: Serial Mediation with Two Moderators

data_med_mod_serial_cat: Sample Dataset: Serial Moderated Mediation with Two Categorical Moderators

data_med_mod_serial_parallel: Sample Dataset: Serial-Parallel Mediation with Two Moderators

data_med_mod_serial_parallel_cat: Sample Dataset: Serial-Parallel Moderated Mediation with Two Categorical Moderators

data_mod: Sample Dataset: One Moderator

data_mod2: Sample Dataset: Two Moderators

data_mod_cat: Sample Dataset: Moderation with One Categorical Moderator

data_mome_demo: Sample Dataset: A Complicated Moderated-Mediation Model

data_mome_demo_missing: Sample Dataset: A Complicated Moderated-Mediation Model With Missing Data

data_parallel: Sample Dataset: Parallel Mediation

data_sem: Sample Dataset: A Latent Variable Mediation Model With 4 Factors

data_serial: Sample Dataset: Serial Mediation

data_serial_parallel: Sample Dataset: Serial-Parallel Mediation

data_serial_parallel_latent: Sample Dataset: A Latent Mediation Model With Three Mediators

modmed_x1m3w4y1: Sample Dataset: Moderated Serial Mediation

simple_mediation_latent: Sample Dataset: A Simple Latent Mediation Model