petersenlab (version 1.1.0)

semPlotInteraction: Plot interaction from SEM model.

Description

Generates a plot of a 2-way interaction from a structural equation model (SEM) that was estimated using the lavaan package.

Usage

semPlotInteraction(
  data,
  fit,
  predictor,
  centered_predictor,
  moderator,
  centered_moderator,
  interaction,
  outcome,
  covariates = NULL,
  predStr = NULL,
  modStr = NULL,
  outStr = NULL
)

Value

Plot of two-way interaction from structural equation model.

Arguments

data

the dataframe object from which the model was derived

fit

the fitted model lavaan object

predictor

the variable name of the predictor variable that is in its raw metric (in quotes)

centered_predictor

the variable name of the mean-centered predictor variable as it appears in the model object syntax in lavaan (in quotes)

moderator

the variable name of the moderator variable that is in its raw metric (in quotes)

centered_moderator

the variable name of the mean-centered moderator variable that as it appears in the model object syntax in lavaan (in quotes)

interaction

the variable name of the interaction term as it appears in the model object syntax in lavaan (in quotes)

outcome

the variable name of the outcome variable as it appears in the model object syntax in lavaan (in quotes)

covariates

default NULL; a vector of the names of the covariate variables as they appear in the model object syntax in lavaan (each in quotes)

predStr

default NULL; optional addition of an x-axis title for the name of the predictor variable (in quotes); if left unset, plot label will default to "Predictor"

modStr

default NULL; optional addition of an z-axis title for the name of the moderator variable (in quotes); if left unset, plot label will default to "Moderator"

outStr

default NULL; optional addition of an x-axis title for the name of the outcome variable (in quotes); if left unset, plot label will default to "Outcome"

Details

Created by Johanna Caskey ([e-mail]).

See Also

Other plot: addText(), plot2WayInteraction(), ppPlot(), vwReg()

Other multipleRegression: lmCombine(), plot2WayInteraction(), ppPlot(), update_nested()

Other structural equation modeling: equiv_chi(), make_esem_model(), puc(), satorraBentlerScaledChiSquareDifferenceTestStatistic()

Examples

states <- as.data.frame(state.x77)
names(states)[which(names(states) == "HS Grad")] <- "HS.Grad"
states$Income_rescaled <- states$Income/100

# Mean Center Predictors
states$Illiteracy_centered <- scale(states$Illiteracy, scale = FALSE)
states$Murder_centered <- scale(states$Murder, scale = FALSE)

# Compute Interaction Term
states$interaction <- states$Illiteracy_centered * states$Murder_centered

# Specify model syntax
moderationModel <- '
  Income_rescaled ~ Illiteracy_centered + Murder_centered + interaction +
  HS.Grad
'

# Fit the model
moderationFit <- lavaan::sem(
  moderationModel,
  data = states,
  missing = "ML",
  estimator = "MLR",
  fixed.x = FALSE)

# Pass model to function (unlabeled plot)
semPlotInteraction(
  data = states,
  fit = moderationFit,
  predictor = "Illiteracy",
  centered_predictor = "Illiteracy_centered",
  moderator = "Murder",
  centered_moderator = "Murder_centered",
  interaction = "interaction",
  outcome = "Income_rescaled",
  covariates = "HS.Grad")

# Pass model to function (labeled plot)
semPlotInteraction(
  data = states,
  fit = moderationFit,
  predictor = "Illiteracy",
  centered_predictor = "Illiteracy_centered",
  moderator = "Murder",
  centered_moderator = "Murder_centered",
  interaction = "interaction",
  outcome = "Income_rescaled",
  covariates = "HS.Grad",
  predStr = "Illiteracy Level",
  modStr = "Murder Rate",
  outStr = "Income")