Title:	Structural Equation Model Effect Analysis and Visualization
Version:	1.2.3
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. For a general overview of the methods used in this package, see Rosseel (2012) <doi:10.18637/jss.v048.i02> and Lefcheck (2016) <doi:10.1111/2041-210X.12512>.
URL:	https://github.com/PhDMeiwp/semEffect/
BugReports:	https://github.com/PhDMeiwp/semEffect/issues
Depends:	R (≥ 4.4.0)
License:	GPL-3
Encoding:	UTF-8
Imports:	lavaan, piecewiseSEM, plspm, ggplot2, tidyr, dplyr, utils, checkmate, RColorBrewer
Suggests:	testthat
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-06-30 06:54:47 UTC; meiwe
Author:	Weiping Mei [aut, cre]
Maintainer:	Weiping Mei <meiweipingg@163.com>
Repository:	CRAN
Date/Publication:	2025-07-04 19:30:01 UTC

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

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"))