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Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).
To install from CRAN:
install.packages("regrrr")
library(regrrr)
You can also use devtools to install the latest development version:
devtools::install_github("RkzYang/regrrr")
library(regrrr)
# build regression models using mtcars dataset
data(mtcars)
m0 <- lm(mpg ~ vs + carb + hp + wt, data = mtcars)
m1 <- update(m0, . ~ . + wt * hp)
m2 <- update(m1, . ~ . + wt * vs)
# compile the correlation table
cor.table(data = m2$model)
# compile the regression table
regression_table <- rbind(
combine_long_tab(to_long_tab(summary(m0)$coef),
to_long_tab(summary(m1)$coef),
to_long_tab(summary(m2)$coef)),
compare_models(m0, m1, m2))
rownames(regression_table) <- NULL
print(regression_table)
# plot the moderating effect
plot_effect(reg.coef = summary(m2)$coefficients, data = mtcars, model = m2,
x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp",
confidence_interval = TRUE, CI_Ribbon = FALSE,
xlab = "Weight", ylab = "MPG", moderator.lab = "Horsepower") +
ggplot2::theme(text=ggplot2::element_text(family="Times New Roman", size = 16))
# plot the moderating effect with a linear spline
library(lspline)
m3 <- lm(mpg ~ vs + carb + hp + lspline(wt, knots = 4, marginal = FALSE) * hp, data = mtcars)
plot_effect(reg.coef=summary(m3)$coefficients, data = mtcars, model = m3,
x_var.name = "wt", y_var.name = "mpg", moderator.name = "hp",
xlab="Weight", ylab="MPG", moderator.lab="Horsepower") +
ggplot2::theme(text=ggplot2::element_text(family="Times New Roman", size = 16))
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.