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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