gg_parallel_slopes: Plot parallel slopes model

In moderndive: Tidyverse-Friendly Introductory Linear Regression

Plot parallel slopes model

Description

NOTE: This function is deprecated; please use geom_parallel_slopes() instead. Output a visualization of linear regression when you have one numerical and one categorical explanatory/predictor variable: a separate colored regression line for each level of the categorical variable

Usage

gg_parallel_slopes(y, num_x, cat_x, data, alpha = 1)

Arguments

y	Character string of outcome variable in data
num_x	Character string of numerical explanatory/predictor variable in data
cat_x	Character string of categorical explanatory/predictor variable in data
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
alpha	Transparency of points

Value

A ggplot2::ggplot() object.

See Also

geom_parallel_slopes()

Examples

## Not run: 
library(ggplot2)
library(dplyr)
library(moderndive)

# log10() transformations
house_prices <- house_prices %>%
  mutate(
    log10_price = log10(price),
    log10_size = log10(sqft_living)
  )

# Output parallel slopes model plot:
gg_parallel_slopes(
  y = "log10_price", num_x = "log10_size", cat_x = "condition",
  data = house_prices, alpha = 0.1
) +
  labs(
    x = "log10 square feet living space", y = "log10 price in USD",
    title = "House prices in Seattle: Parallel slopes model"
  )

# Compare with interaction model plot:
ggplot(house_prices, aes(x = log10_size, y = log10_price, col = condition)) +
  geom_point(alpha = 0.1) +
  geom_smooth(method = "lm", se = FALSE, size = 1) +
  labs(
    x = "log10 square feet living space", y = "log10 price in USD",
    title = "House prices in Seattle: Interaction model"
  )

## End(Not run)

moderndive documentation built on Dec. 2, 2022, 1:08 a.m.