ggiraphExtra (version 0.2.9)

ggEffect: Visualize the effect of interaction between two continuous independent variables on a response variable

Description

Visualize the effect of interaction between two continuous independent variables on a response variable

Usage

ggEffect(x, ...)
# S3 method for default
ggEffect(x, mapping, use.label = TRUE, use.labels = TRUE,
  ...)
# S3 method for formula
ggEffect(x, data, ...)
# S3 method for lm
ggEffect(x, no = 1, probs = c(0.1, 0.5, 0.9), point = TRUE,
  xvalue = NULL, digits = 2, use.rownames = FALSE, interactive = FALSE,
  ...)

Arguments

x

Object to ggEffect

...

additional arguments passed to the generic function

mapping

Set of aesthetic mappings created by aes or aes_.

use.label

Logical. Whether or not use column label in case of labelled data

use.labels

Logical. Whether or not use value labels in case of labelled data

data

A data.frame

no

an integer

probs

A vector of probability weights for obtaining the elements of the vector being sampled.Default value is c(0.10,0.5,0.90)

point

A logical value. If TRUE, draw points

xvalue

A numeric vector

digits

An integer indicating the number of decimal places

use.rownames

If TRUE, use rownames in label

interactive

A logical value. If TRUE, an interactive plot will be returned

Value

An interactive plot showing interaction

Methods (by class)

• default: Visualize the effect of interaction between two continuous independent variables on a response variable

• formula: Visualize the effect of interaction between two continuous independent variables on a response variable

• lm: Visualize the effect of interaction between two continuous independent variables on a response variable

Examples

# NOT RUN {
require(ggplot2)
require(ggiraph)
ggEffect(mtcars,aes(x=wt,y=mpg,color=hp))
require(moonBook)
ggEffect(acs,aes(x=height,y=weight,color=smoking))
require(ggplot2)
require(ggiraph)
require(moonBook)
ggEffect(NTAV~age*smoking,data=radial)
require(moonBook)
require(ggplot2)
require(ggiraph)
fit=lm(age~sex*smoking,data=acs)
ggEffect(fit,interactive=TRUE)
ggEffect(radial,aes(x=age,y=NTAV,group=smoking))
# }