inst/doc/plot_interactions.R

In sjPlot: Data Visualization for Statistics in Social Science

## ----set-options, echo = FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", dev = "png", fig.width = 7, fig.height = 3.5, message = FALSE, warning = FALSE)
options(width = 800, tibble.width = Inf)

if (!requireNamespace("sjmisc", quietly = TRUE) ||
    !requireNamespace("ggplot2", quietly = TRUE) ||
    !requireNamespace("haven", quietly = TRUE) ||
    !requireNamespace("sjlabelled", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)
}

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library(sjPlot)
library(sjmisc)
library(ggplot2)
data(efc)
theme_set(theme_sjplot())

# make categorical
efc$c161sex <- to_factor(efc$c161sex)

# fit model with interaction
fit <- lm(neg_c_7 ~ c12hour + barthtot * c161sex, data = efc)

plot_model(fit, type = "pred", terms = c("barthtot", "c161sex"))

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plot_model(fit, type = "int")

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plot_model(fit, type = "pred", terms = c("c161sex", "barthtot [0, 100]"))

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# fit model with interaction, switching terms in formula
fit <- lm(neg_c_7 ~ c12hour + c161sex * barthtot, data = efc)
plot_model(fit, type = "int")

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plot_model(fit, type = "int", mdrt.values = "meansd")

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# fit model with 3-way-interaction
fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)

# select only levels 30, 50 and 70 from continuous variable Barthel-Index
plot_model(fit, type = "pred", terms = c("c12hour", "barthtot [30,50,70]", "c161sex"))

## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
plot_model(fit, type = "int")