Package {ModLR}

ModLR: Information-Theoretic Moderation Analysis

Description

The ModLR package implements an information-theoretic framework for moderation analysis using multi-model inference based on Akaike's Information Criterion (AIC and AICc).

Provides a robust implementation of information-theoretic moderation analysis using multi-model inference based on Akaike's Information Criterion (AIC) and its small-sample corrected form (Corrected AIC). The package enables researchers to compare competing model specifications and helps distinguish true interaction effects from nonlinear relationships that may produce spurious moderation. The methods build on Daryanto (2019) doi:10.1016/j.jbusres.2019.06.012.

Details

It enables researchers to compare alternative moderation models and reduces the risk of spurious moderation effects arising from nonlinear relationships.

The methodology is based on Daryanto (2019), which introduces an information-theoretic approach to moderation analysis.

Author(s)

Maintainer: Ahmad Daryanto ahdar_2000@yahoo.com

Authors:

• Ahmad Daryanto ahdar_2000@yahoo.com

References

Daryanto, A. (2019). Avoiding spurious moderation effects: An information-theoretic approach to moderation analysis. Journal of Business Research, 103, 110–118.

See Also

Useful links:

• https://github.com/ahdar1/ModLR

Compare Moderation Models

Description

Compares candidate moderation models using information criteria (AIC/AICc).

Usage

compare_models(object, models = NULL, corrected = TRUE)

Arguments

Value

A data frame with model comparison results

Examples

set.seed(123)

n <- 100
x <- rnorm(n)
z <- 0.5 * x + sqrt(1 - 0.5^2) * rnorm(n)

y <- 0.3 * x + 0.3 * z + 0.8 * x * z + rnorm(n)

data <- data.frame(x, z, y)

result <- moderated_regression(data, iv = "x", moderator = "z", dv = "y")

compare_models(result)
compare_models(result, models = c(1, 2))

Johnson-Neyman Analysis

Description

Computes regions of significance for an interaction effect.

Usage

johnson_neyman(object, alpha = 0.05, robust = NULL)

Arguments

Value

A data.frame of Johnson-Neyman results

Examples

set.seed(123)

n <- 100
x <- rnorm(n)
z <- 0.5 * x + sqrt(1 - 0.5^2) * rnorm(n)
y <- 0.3 * x + 0.3 * z + 0.8 * x * z + rnorm(n)

data <- data.frame(x, z, y)

result <- moderated_regression(data, iv = "x", moderator = "z", dv = "y")

johnson_neyman(result)
johnson_neyman(result, robust = TRUE)

Moderated Regression Model

Description

Fits a moderated regression model with optional extensions.

Usage

moderated_regression(
  data,
  iv,
  moderator,
  dv,
  covariates = NULL,
  center = TRUE,
  quadratic = FALSE,
  robust_se = FALSE
)

Arguments

Value

A fitted model object

Examples

set.seed(123)

n <- 100
x <- rnorm(n)
w1 <- rnorm(n)
w2 <- rnorm(n)
z <- 0.5 * x + sqrt(1 - 0.5^2) * rnorm(n)

y <- 0.3 * x + 0.3 * z + 0.8 * x * z + rnorm(n)

data <- data.frame(w1, w2, x, z, y)

result <- moderated_regression(
 data,
 iv = "x",
 moderator = "z",
 dv = "y",
 covariates = c("w1", "w2")
 )

Plot Moderation Effect

Description

Produces a plot of the moderation effect.

Usage

plot_moderation(object)

Arguments

Value

A ggplot object

Examples

set.seed(123)

n <- 100
x <- rnorm(n)
z <- 0.5 * x + sqrt(1 - 0.5^2) * rnorm(n)

y <- 0.3 * x + 0.3 * z + 0.8 * x * z + rnorm(n)

data <- data.frame(x, z, y)

result <- moderated_regression(data, iv = "x", moderator = "z", dv = "y")

plot_moderation(result)

Simple Slopes Analysis

Description

Computes simple slopes for moderation analysis.

Usage

simple_slopes(object, values = NULL)

Arguments

Value

A data frame of slopes

Examples

set.seed(123)

n <- 100
x <- rnorm(n)
z <- 0.5 * x + sqrt(1 - 0.5^2) * rnorm(n)

y <- 0.3 * x + 0.3 * z + 0.8 * x * z + rnorm(n)

data <- data.frame(x, z, y)

result <- moderated_regression(data, iv = "x", moderator = "z", dv = "y")

simple_slopes(result)