Day 4: A challenge to learn basics of Structural Equation Modeling (SEM) using lavaan and semPlot packages in R

During the next 12 days, I will learn and repeat the basics of structural equation modeling (SEM) using lavaan and semPlot packages in R.

You can search my lavaan posts by typing: #UsmanZafarParacha_lavaan , and semPlot posts by typing: #UsmanZafarParacha_semPlot

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During this day, essential libraries, including lavaan and semPlot are loaded. Then, an SEM model is specified showing how diet and exercise affect cardiovascular disease both directly and indirectly through mental health. Following lines of codes can be used:

 

library(lavaan)

library(semPlot)

 

# Model specification

model <- '

  # Direct effects

  mental_health ~ diet + exercise

  cardiovascular_disease ~ diet + exercise + mental_health

 

  # Indirect effects (via mental health)

  cardiovascular_disease ~ mental_health

'

 

Then a supposed data is generated using the following lines of codes:

 

set.seed(123)

n <- 200  # number of participants

 

# Simulate data

diet <- rnorm(n, mean = 50, sd = 10)

exercise <- rnorm(n, mean = 30, sd = 8)

mental_health <- 0.4 * diet + 0.6 * exercise + rnorm(n)

cardiovascular_disease <- 0.5 * diet + 0.3 * exercise + 0.7 * mental_health + rnorm(n)

 

# Combine into a data frame

data <- data.frame(diet, exercise, mental_health, cardiovascular_disease)

 

Then the lavaan function is used to fit the specified model to the data, using the following lines of codes:

 

fit <- sem(model, data = data)

 

# View the summary of the results

summary(fit, standardized = TRUE, fit.measures = TRUE)

 

Then, the SEM can be visualized using the semPlot using the following lines of codes:

 

# Create a SEM plot

semPaths(fit, whatLabels = "std", layout = "tree", edge.label.cex = 1.2,

         sizeMan = 7, sizeLat = 10, asize = 2, color = "lightblue")

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