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.
1 Answer
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 (i.e. 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 (e.g. 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()
