ggplot2 geom_smooth，方法的扩展模型= lm

Question

I would like to use geom_smooth to get a fitted line from a certain linear regression model.

It seems to me that the formula can only take x and y and not any additional parameter.

To show more clearly what I want:

library(dplyr)
library(ggplot2)
set.seed(35413)
df <- data.frame(pred = runif(100,10,100),
           factor = sample(c("A","B"), 100, replace = TRUE)) %>%
  mutate(
    outcome = 100 + 10*pred + 
    ifelse(factor=="B", 200, 0) + 
    ifelse(factor=="B", 4, 0)*pred +
    rnorm(100,0,60))

With

ggplot(df, aes(x=pred, y=outcome, color=factor)) +
  geom_point(aes(color=factor)) +
  geom_smooth(method = "lm") +
  theme_bw()

I produce fitted lines that, due to the color=factor option, are basically the output of the linear model lm(outcome ~ pred*factor, df)

In some cases, however, I prefer the lines to be the output of a different model fit, like lm(outcome ~ pred + factor, df) , for which I can use something like:

fit <- lm(outcome ~ pred+factor, df)
predval <- expand.grid(
  pred = seq(
    min(df$pred), max(df$pred), length.out = 1000),
  factor = unique(df$factor)) %>%
  mutate(outcome = predict(fit, newdata = .))

ggplot(df, aes(x=pred, y=outcome, color=factor)) +
  geom_point() +
  geom_line(data = predval) +
  theme_bw()

which results in :

My question: is there a way to produce the latter graph exploiting the geom_smooth instead? I know there is a formula = - option in geom_smooth but I can't make something like formula = y ~ x + factor or formula = y ~ x + color (as I defined color = factor ) work.

Answer 1

This is a very interesting question. Probably the main reason why geom_smooth is so "resistant" to allowing custom models of multiple variables is that it is limited to producing 2-D curves; consequently, its arguments are designed for handling two-dimensional data (ie formula = response variable ~ independent variable).

The trick to getting what you requested is using the mapping argument within geom_smooth , instead of formula . As you've probably seen from looking at the documentation , formula only allows you to specify the mathematical structure of the model (eg linear, quadratic, etc.). Conversely, the mapping argument allows you to directly specify new y-values - such as the output of a custom linear model that you can call using predict() .

Note that, by default, inherit.aes is set to TRUE , so your plotted regressions will be coloured appropriately by your categorical variable. Here's the code:

# original plot
plot1 <- ggplot(df, aes(x=pred, y=outcome, color=factor)) +
  geom_point(aes(color=factor)) +
  geom_smooth(method = "lm") +
  ggtitle("outcome ~ pred") +
  theme_bw()

# declare new model here
plm <- lm(formula = outcome ~ pred + factor, data=df)

# plot with lm for outcome ~ pred + factor
plot2 <-ggplot(df, aes(x=pred, y=outcome, color=factor)) +
  geom_point(aes(color=factor)) +
  geom_smooth(method = "lm", mapping=aes(y=predict(plm,df))) +
  ggtitle("outcome ~ pred + factor") +
  theme_bw()