sem_effects: Structural Equation Model Effect Analysis and Visualization

In semEffect: Structural Equation Model Effect Analysis and Visualization

Structural Equation Model Effect Analysis and Visualization

Description

Provides standardized effect decomposition (direct, indirect, and total effects) for three major structural equation modeling frameworks: 'lavaan', 'piecewiseSEM', and 'plspm'. Automatically handles zero-effect variables, generates publication-ready 'ggplot2' visualizations, and returns both wide-format and long-format effect tables. Supports effect filtering, multi-model object inputs, and customizable visualization parameters.

Usage

sem_effects(
  object,
  target,
  plot = TRUE,
  delete_zero_effect = TRUE,
  total_only = FALSE,
  total_color = "skyblue",
  color_palette = c("darkgreen", "skyblue", "orange")
)

Arguments

object	SEM object (lavaan/psem/plspm).
target	Character string specifying the target variable name for effect analysis.
plot	Logical indicating whether to generate effect visualization plots (default: TRUE).
delete_zero_effect	Logical indicating whether to removes rows where all specified effect columns contain only zeros (default: TRUE).
total_only	Logical controlling plot mode. If TRUE, shows only total effects with customizable colors; if FALSE, displays all effect types with palette coloring (default: FALSE).
total_color	Single color or vector of colors for total effect bars when total_only=TRUE (default: "skyblue").
color_palette	Character vector of 3 colors for direct/indirect/total effects when total_only=FALSE (default: c("darkgreen", "skyblue", "orange")).

Value

A list containing three components:

• effect_table: A data frame with variables and their standardized effect values (direct, indirect, total)

• effect_long: A long-format version of effect_table

• plot_object: A ggplot2 object (if plot=TRUE), NULL otherwise

Author(s)

Weiping Mei

See Also

sem, psem, plspm

Examples

# Example 01: lavaan -------------------------------

library(lavaan)

model <- '
  # Measurement model
  ind60 =~ x1 + x2 + x3
  dem60 =~ y1 + y2 + y3 + y4
  dem65 =~ y5 + y6 + y7 + y8

  # Structural model
  dem60 ~ ind60
  dem65 ~ ind60 + dem60
'
fit <- sem(model, data = PoliticalDemocracy)

# Analyze effects for target variable "dem65"
results <- sem_effects(fit, target = "dem65")

print(results$effect_table)
print(results$effect_long)
print(results$plot_object)

# Customize plot appearance
results$plot_object +
  ggplot2::coord_flip()+
  ggplot2::theme_minimal() +
  ggplot2::ggtitle("Standardized effects for dem65")


# Example 02: piecewiseSEM --------------------------

library(piecewiseSEM)
pmod <- psem(
  lm(rich ~ cover, data = keeley),
  lm(cover ~ firesev, data = keeley),
  lm(firesev ~ age, data = keeley),
  data = keeley
  )

sem_effects(pmod, target = "rich",
        color_palette = c("darkgreen", "grey80", "purple"))


# Example 03: plspm ---------------------------------

library(plspm)
data(satisfaction)

# path matrix
IMAG = c(0,0,0,0,0,0)
EXPE = c(1,0,0,0,0,0)
QUAL = c(0,1,0,0,0,0)
VAL = c(0,1,1,0,0,0)
SAT = c(1,1,1,1,0,0)
LOY = c(1,0,0,0,1,0)
sat_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, LOY)

# blocks of outer model
sat_blocks = list(1:5, 6:10, 11:15, 16:19, 20:23, 24:27)

# vector of modes (reflective indicators)
sat_mod = rep("A", 6)

# apply plspm
plsmodel = plspm(satisfaction, sat_path, sat_blocks, modes = sat_mod)

sem_effects(plsmodel, target = "LOY", plot = TRUE, delete_zero_effect = TRUE,
            total_only = TRUE,
            total_color = RColorBrewer::brewer.pal(5,"Set3"))

semEffect documentation built on Aug. 9, 2025, 1:06 a.m.