R print equation of linear regression on the plot itself

You use ?text. In addition, you should not use abline(lm(Signups ~ cost)), as this is a different model (see my answer on CV here: Is there a difference between 'controling for' and 'ignoring' other variables in multiple regression). At any rate, consider:

set.seed(1) Signups <- rnorm(20) cost <- rnorm(20) targeting <- rnorm(20) fit <- lm(Signups ~ cost + targeting) summary(fit) # ... # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 0.1494 0.2072 0.721 0.481 # cost -0.1516 0.2504 -0.605 0.553 # targeting 0.2894 0.2695 1.074 0.298 # ... windows();{ plot(cost, Signups, xlab="cost", ylab="Signups", main="Signups") abline(coef(fit)[1:2]) text(-2, -2, adj=c(0,0), labels="Signups = .15 -.15cost + .29targeting") } 

Here's a solution using tidyverse packages.

The key is the broom package, whcih simplifies the process of extracting model data. For example:

fit1 <- lm(mpg ~ cyl, data = mtcars) summary(fit1) fit1 %>% tidy() %>% select(estimate, term) 

Result

# A tibble: 2 x 2 estimate term <dbl> <chr> 1 37.9 (Intercept) 2 -2.88 cyl 

I wrote a function to extract and format the information using dplyr:

get_formula <- function(object) { object %>% tidy() %>% mutate( term = if_else(term == "(Intercept)", "", term), sign = case_when( term == "" ~ "", estimate < 0 ~ "-", estimate >= 0 ~ "+" ), estimate = as.character(round(abs(estimate), digits = 2)), term = if_else(term == "", paste(sign, estimate), paste(sign, estimate, term)) ) %>% summarize(terms = paste(term, collapse = " ")) %>% pull(terms) } get_formula(fit1) 

Result

[1] " 37.88 - 2.88 cyl" 

Then use ggplot2 to plot the line and add a caption

mtcars %>% ggplot(mapping = aes(x = cyl, y = mpg)) + geom_point() + geom_smooth(formula = y ~ x, method = "lm", se = FALSE) + labs( x = "Cylinders", y = "Miles per Gallon", caption = paste("mpg =", get_formula(fit1)) ) 

Plot using geom_smooth()

This approach of plotting a line really only makes sense to visualize the relationship between two variables. As @Glen_b pointed out in the comment, the slope we get from modelling mpg as a function of cyl (-2.88) doesn't match the slope we get from modelling mpg as a function of cyl and other variables (-1.29). For example:

fit2 <- lm(mpg ~ cyl + disp + wt + hp, data = mtcars) summary(fit2) fit2 %>% tidy() %>% select(estimate, term) 

Result

# A tibble: 5 x 2 estimate term <dbl> <chr> 1 40.8 (Intercept) 2 -1.29 cyl 3 0.0116 disp 4 -3.85 wt 5 -0.0205 hp 

That said, if you want to accurately plot the regression line for a model that includes variables that don't appear included in the plot, use geom_abline() instead and get the slope and intercept using broom package functions. As far as I know geom_smooth() formulas can't reference variables that aren't already mapped as aesthetics.

mtcars %>% ggplot(mapping = aes(x = cyl, y = mpg)) + geom_point() + geom_abline( slope = fit2 %>% tidy() %>% filter(term == "cyl") %>% pull(estimate), intercept = fit2 %>% tidy() %>% filter(term == "(Intercept)") %>% pull(estimate), color = "blue" ) + labs( x = "Cylinders", y = "Miles per Gallon", caption = paste("mpg =", get_formula(fit2)) ) 

Plot using geom_abline()