xmed {regsem}

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