How can a graph of a polynomial regression with a categorical variable be plotted?

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In the R statistical package, is there a way to plot a graph of a second order polynomial regression with one continuous variable and one categorical variable?

To generate a linear regression graph with one categorical variable:

library(ggplot2)
library(ggthemes) ## theme_few()
set.seed(1)
df <- data.frame(minutes = runif(60, 5, 15), endtime=60, category="a")
df$category = df$category=letters[seq( from = 1, to = 2 )]
df$endtime = df$endtime + df$minutes^3/180 + df$minutes*runif(60, 1, 2)
ggplot(df, aes(y=endtime, x=minutes, col = category)) +
  geom_point() +
  geom_smooth(method=lm) + 
  theme_few()

To plot a polynomial graph with one one continuous variable:

 ggplot(df, aes(x=minutes, y=endtime)) +    
        geom_point() +           
        stat_smooth(method='lm', formula = y ~ poly(x,2), size = 1) + 
        xlab('Minutes of warm up') +
        ylab('End time')

But I can’t figure out how to plot a polynomial graph with one continuous variable and one categorical variable.

regression ggplot2 r
1 Answers

Just add a colour or group mapping. This will make ggplot fit and display separate polynomial regressions for each category. (1) It's not possible to display an additive mixed-polynomial regression (i.e. lm(y ~ poly(x,2) + category)); (2) what's shown here is not quite equivalent to the results of the interaction model lm(y ~ poly(x,2)*col), because the residual variances (and hence the widths of the confidence ribbons) are estimated separately for each group.

ggplot(df, aes(x=minutes, y=endtime, col = category)) +    
  geom_point() +           
  stat_smooth(method='lm', formula = y ~ poly(x,2)) + 
  labs(x = 'Minutes of warm up', y = 'End time') +
  theme_few()

enter image description here

user_1902770
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