View source: R/ggplot_parallel_slopes.R
gg_parallel_slopes  R Documentation 
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
gg_parallel_slopes(y, num_x, cat_x, data, alpha = 1)
y 
Character string of outcome variable in 
num_x 
Character string of numerical explanatory/predictor variable in

cat_x 
Character string of categorical explanatory/predictor variable
in 
data 
an optional data frame, list or environment (or object
coercible by 
alpha 
Transparency of points 
A ggplot2::ggplot()
object.
geom_parallel_slopes()
## 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)
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