1 Load packages
library(tidyverse) # data wrangling
library(plotly) # 3D plot interactive2 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")
p15 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")
p2Note that the plot is interactive.
6 Reproducibility
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