clinpubr (version 1.1.1)

interaction_plot: Plot interactions

Description

Plot interactions between variables. Both logistic and Cox proportional hazards regression models are supported. The predictor variables in the model are can be used both in linear form or in restricted cubic spline form.

Usage

interaction_plot(
  data,
  y,
  predictor,
  group_var,
  time = NULL,
  time2 = NULL,
  covars = NULL,
  cluster = NULL,
  group_colors = NULL,
  save_plot = FALSE,
  filename = NULL,
  height = 4,
  width = 4,
  xlab = predictor,
  ylab = NULL,
  show_n = TRUE,
  group_title = group_var,
  ...
)

Value

A ggplot object.

Arguments

data

A data frame.

y

A character string of the outcome variable.

predictor

A character string of the predictor variable.

group_var

A character string of the group variable. The variable should be categorical. If a numeric variable is provided, it will be split by the median value.

time

A character string of the time variable. If NULL, logistic regression is used. Otherwise, Cox proportional hazards regression is used.

time2

A character string of the ending time of the interval for interval censored or counting process data only.

covars

A character vector of covariate names.

cluster

A character string of the cluster variable. If set, correct for heteroscedasticity and for correlated responses from cluster samples using rms::robcov().

group_colors

A character vector of colors for the plot. If NULL, the default colors are used.

save_plot

A logical value indicating whether to save the plot.

filename

The name of the file to save the plot. Support both .png and .pdf formats.

height

The height of the saved plot.

width

The width of the saved plot.

xlab

The label of the x-axis.

ylab

The label of the y-axis.

show_n

A logical value indicating whether to show the number of observations in the plot.

group_title

The title of the group variable.

...

Additional arguments passed to the ggplot function.

Examples

data(cancer, package = "survival")
interaction_plot(cancer,
  y = "status", time = "time", predictor = "age", group_var = "sex",
  save_plot = FALSE
)
interaction_plot(cancer,
  y = "status", predictor = "age", group_var = "sex",
  save_plot = FALSE
)
interaction_plot(cancer,
  y = "wt.loss", predictor = "age", group_var = "sex",
  save_plot = FALSE
)