xmed: Function to performed exploratory mediation with continuous...

In Rjacobucci/regsem: Regularized Structural Equation Modeling

Function to performed exploratory mediation with continuous and categorical variables

Description

Function to performed exploratory mediation with continuous and categorical variables

Usage

xmed(
  data,
  iv,
  mediators,
  dv,
  covariates = NULL,
  type = "lasso",
  nfolds = 10,
  show.lambda = F,
  epsilon = 0.001,
  seed = NULL
)

Arguments

data	Name of the dataset
iv	Name (or vector of names) of independent variable(s)
mediators	Name of mediators
dv	Name of dependent variable
covariates	Name of covariates to be included in model.
type	What type of penalty. Options include lasso, ridge, and enet.
nfolds	Number of cross-validation folds.
show.lambda	Displays lambda values in output
epsilon	Threshold for determining whether effect is 0 or not.
seed	Set seed to control CV results

Value

Coefficients from best fitting model

Examples

# example
library(ISLR)
College1 = College[which(College$Private=="Yes"),]
Data = data.frame(scale(College1[c("Grad.Rate","Accept","Outstate","Room.Board","Books","Expend")]))
Data$Grad.Rate <- ifelse(Data$Grad.Rate > 0,1,0)
Data$Grad.Rate <- as.factor(Data$Grad.Rate)
#lavaan model with all mediators
model1 <-
 ' # direct effect (c_prime)
Grad.Rate ~ c_prime*Accept
# mediators
Outstate ~ a1*Accept
Room.Board ~ a2*Accept
Books ~ a3*Accept
Expend ~ a6*Accept
Grad.Rate ~ b1*Outstate + b2*Room.Board + b3*Books + b6*Expend
# indirect effects (a*b)
a1b1 := a1*b1
a2b2 := a2*b2
a3b3 := a3*b3
a6b6 := a6*b6
# total effect (c)
c := c_prime + (a1*b1) + (a2*b2) + (a3*b3) + (a6*b6)
'
#p-value approach using delta method standard errors
fit.delta = sem(model1,data=Data,fixed.x=TRUE,ordered="Grad.Rate")
summary(fit.delta)

#xmed()

iv <- "Accept"
dv <- "Grad.Rate"
mediators <- c("Outstate","Room.Board","Books","Expend")

out <- xmed(Data,iv,mediators,dv)
out

Rjacobucci/regsem documentation built on June 3, 2023, 5:20 a.m.