3D Regression plane with scatter plot

1 Load packages

library(tidyverse)  # data wrangling
library(plotly)  # 3D plot interactive

2 Define model

Here’s the linear model with 2 predictors, giving us a model that can be visualized in 3D:

lm1 <- lm(mpg ~ hp + disp, data = mtcars)

As is standard, we’ll predict mpg.

3 Define grid for regression plane

The regression plane describes one value for each predictor pair, hp and disp:

hp_disp_grid <- expand_grid(hp = seq(50, 500, by = 10), disp = seq(50, 500, by = 10))

Here, we define all possible pairs of the two variables, spanning a range of each from 50 to 500.

Now, let’s add the predicted value for mpg according to our modeL

grid2 <- 
  hp_disp_grid %>% 
  mutate(mpg_pred_lm1 = predict(lm1, newdata = data.frame(hp, disp)))

And now cast wider and in matrix form:

grid_wide <- 
  grid2 %>% 
  pivot_wider(names_from = disp, values_from = mpg_pred_lm1) %>% 
  select(-1) %>%  # kick the name's column out
  as.matrix()

4 Scatter Plot

p1 <- plot_ly(mtcars,
        x = ~ hp,
        y = ~ disp,
        z = ~ mpg,
        type = "scatter3d")
p1

5 Scatter plot with 3D surface

p2 <- add_trace(p = p1,
                z = grid_wide,
                x = seq(50, 300, by = 10),
                y = seq(50, 500, by = 10),
                type = "surface")
p2

Note that the plot is interactive.

6 Reproducibility

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