Predicted vs. Actual plot - r

Question

I'm new to R and statistics and haven't been able to figure out how one would go about plotting predicted values vs. Actual values after running a multiple linear regression. I have come across similar questions (just haven't been able to understand the code). I would greatly appreciate it if you explain the code.
This is what I have done so far:
# Attach file containing variables and responses
q <- read.csv("C:/Users/A/Documents/Design.csv")
attach(q)
# Run a linear regression
model <- lm(qo~P+P1+P4+I)
# Summary of linear regression results
summary(model)
The plot of predicted vs. actual is so I can graphically see how well my regression fits on my actual data.

Answer 1

It would be better if you provided a reproducible example, but here's an example I made up:
set.seed(101)
dd <- data.frame(x=rnorm(100),y=rnorm(100),
z=rnorm(100))
dd$w <- with(dd,
rnorm(100,mean=x+2*y+z,sd=0.5))
It's (much) better to use the data argument -- you should almost never use attach() ..
m <- lm(w~x+y+z,dd)
plot(predict(m),dd$w,
xlab="predicted",ylab="actual")
abline(a=0,b=1)

Answer 2

Besides predicted vs actual plot, you can get an additional set of plots which help you to visually assess the goodness of fit.
--- execute previous code by Ben Bolker ---
par(mfrow = c(2, 2))
plot(m)

Answer 3

A tidy way of doing this would be to use modelsummary::augment():
library(tidyverse)
library(cowplot)
library(modelsummary)
set.seed(101)
# Using Ben's data above:
dd <- data.frame(x=rnorm(100),y=rnorm(100),
z=rnorm(100))
dd$w <- with(dd,rnorm(100,mean=x+2*y+z,sd=0.5))
m <- lm(w~x+y+z,dd)
m %>% augment() %>%
ggplot() +
geom_point(aes(.fitted, w)) +
geom_smooth(aes(.fitted, w), method = "lm", se = FALSE, color = "lightgrey") +
labs(x = "Actual", y = "Fitted") +
theme_bw()
This will work nicely for deep nested regression lists especially.
To illustrate this, consider some nested list of regressions:
Reglist <- list()
Reglist$Reg1 <- dd %>% do(reg = lm(as.formula("w~x*y*z"), data = .)) %>% mutate( Name = "Type 1")
Reglist$Reg2 <- dd %>% do(reg = lm(as.formula("w~x+y*z"), data = .)) %>% mutate( Name = "Type 2")
Reglist$Reg3 <- dd %>% do(reg = lm(as.formula("w~x"), data = .)) %>% mutate( Name = "Type 3")
Reglist$Reg4 <- dd %>% do(reg = lm(as.formula("w~x+z"), data = .)) %>% mutate( Name = "Type 4")
Now is where the power of the above tidy plotting framework comes to life...:
Graph_Creator <- function(Reglist){
Reglist %>% pull(reg) %>% .[[1]] %>% augment() %>%
ggplot() +
geom_point(aes(.fitted, w)) +
geom_smooth(aes(.fitted, w), method = "lm", se = FALSE, color = "lightgrey") +
labs(x = "Actual", y = "Fitted",
title = paste0("Regression Type: ", Reglist$Name) ) +
theme_bw()
}
Reglist %>% map(~Graph_Creator(.)) %>%
cowplot::plot_grid(plotlist = ., ncol = 1)

Answer 4

Same as #Ben Bolker's solution but getting a ggplot object instead of using base R
#first generate the dd data set using the code in Ben's solution, then...
require(ggpubr)
m <- lm(w~x+y+z,dd)
ggscatter(x = "prediction",
y = "actual",
data = data.frame(prediction = predict(m),
actual = dd$w)) +
geom_abline(intercept = 0,
slope = 1)