I am trying to (i) visualise a multiple linear regression (>3 independent variables) and (ii) plot a standard deviation corridor for my linear regression line, but I am not sure how to do this. Specifically, I would like to plot the line - SD. An example with iris:
lm1 <- lm(iris$Sepal.Length ~ iris$Sepal.Width iris$Petal.Width iris$Species iris$Petal.Width)
summary(lm1)
library(car)
avPlots(lm1, intercept = TRUE)
library(ggplot2)
ggplot(iris, aes(x = iris$Sepal.Width, y = Sepal.Length,
col = factor(Species))) geom_point(size=1) theme_bw() geom_smooth(method=lm,se=FALSE, fullrange=TRUE)
Which gives me the added variable plots and the regression line for Sepal.Length~Sepal.Width Species
. I was wondering if (i) there is a more effective way than the added variable plots to summarise the results (I have already seen threads like
Example of intended output on the regression line Sepal.Length~Sepal.Width
in red:
CodePudding user response:
Maybe something like this? Here, the band represents the standard error, which is a measure of how wrong the regression line is likely to be based on sampling error alone.
library(tidyverse); library(ggforce)
ggplot(iris, aes(x = .panel_x, y = .panel_y))
geom_point()
geom_smooth(method = "lm")
facet_matrix(vars(Sepal.Length:Petal.Width), layer.diag = 3)