Plotting multiple lm() models in one plot - r

Question

I have fitted 6 lm() models and 1 gam() model on the same dataset.
Now I want to plot them all in one plot on top of each other. Can I do this without defining the models again in ggplot?
My case is this
I have
model1 <- lm(y~1, data = data) %>% coef()
model2 <- lm(y~x, data = data) %>% coef()
model3 <- lm(y~abs(x), data = data) %>% coef()
...
model7 <- gam(y~s(x), data = data) %>% coef()
can I feed the stored coefficients of my models to ggplot?
ggplot(data, mapping = aes(x = x, y = y)) +
geom_point() +
geom_abline(model1) +
geom_abline(model2) +
....
Or do Is the only way to plot the model prediction lines to manualy fill out the parameters like this:
ggplot(data, mapping = aes(x = x, y = y)) +
geom_point() +
geom_abline(intercept = model1[1]) +
geom_abline(slope = model2[2], intercept = model2[1]) +
geom_abline(slope = model3[2], intercept = model3[1]) +
...
Example code
set.seed(123)
x <- rnorm(50)
y <- rweibull(50,1)
d <- as.data.frame(cbind(x,y))
model1 <- coef(lm(y~1, data = d))
model2 <- coef(lm(y~x, data = d))
model3 <- coef(lm(y~abs(x), data = d))
Including the SE for each line/model and a legend would be welcome as well.

Answer 1

In order for this to work, you really need to save the whole model. So if we assume you have the entire model
# set.seed(101) used for sample data
model1 <- lm(y~1, data = d)
model2 <- lm(y~x, data = d)
model3 <- lm(y~abs(x), data = d)
We can write a helper function to predict new values from these models over a the given range of x values. Here's such a function
newvalsforx <- function(x) {
xrng <- seq(min(x), max(x), length.out=100)
function(m) data.frame(x=xrng, y=predict(m, data.frame(x=xrng)))
}
pred <- newvals(d$x)
This pred() will make predictions from the models over the observed range of x. We can then use these as new data to pass to geom_lines that we can add to a plot. For example
ggplot(d, aes(x,y)) +
geom_point() +
geom_line(data=pred(model1), color="red") +
geom_line(data=pred(model2), color="blue") +
geom_line(data=pred(model3), color="green")
This gives me