Plot parallel slopes model

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

if (FALSE) { # \dontrun{
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"
  )
} # }