Adding a linear abline model to the log-log plot in ggplot

Question

I cannot reproduce adding linear abline to log-log ggplot. The code below. Grateful for the idea where I am wrong.

d = data.frame(x = 100*sort(rlnorm(100)), y = 100*sort(rlnorm(100)))
(fit = lm(d$y ~ d$x))

# linear plot to check fit
ggplot(d, aes(x, y)) + geom_point() + geom_abline(intercept = coef(fit)[1], slope = coef(fit)[2], col='red')

# log-log base plot to replicate in ggplot (don't worry if fit line looks a bit off)
plot(d$x, d$y, log='xy')
abline(fit, col='red', untf=TRUE)

# log-log ggplot
ggplot(d, aes(x, y)) + geom_point() + 
  geom_abline(intercept = coef(fit)[1], slope = coef(fit)[2], col='red') +
  scale_y_log10() + scale_x_log10()

Answer 1

As you draw a linear relationship between x and y, you can use geom_smooth()c method="lm".

ggplot(d, aes(x, y)) + geom_point() + geom_smooth(method="lm",se=FALSE)+
  scale_y_log10() + scale_x_log10()  

UPDATE

It geom_abline()doesn't seem to have an argument untf=TRUElike for a function abline().

A workaround would be to use a geom_line()new data frame containing y values ​​calculated using the coefficients of your linear model or using a function predict().

ggplot(d, aes(x, y)) + geom_point() + 
  geom_line(data=data.frame(x=d$x,y=coef(fit)[1]+coef(fit)[2]*d$x))+
  scale_y_log10() + scale_x_log10()

ggplot(d, aes(x, y)) + geom_point() + 
  geom_line(data=data.frame(x=d$x,y=predict(fit)))+
  scale_y_log10() + scale_x_log10()

Answer 2

, , geom_abline

d = data.frame(x = 100*sort(rlnorm(100)), y = 100*sort(rlnorm(100)))
(fit = lm(log(d$y) ~ log(d$x)))

p <- ggplot(d, aes(x, y)) + geom_point() +
    geom_abline(intercept = coef(fit)[1], slope = coef(fit)[2], col='red') +
    scale_y_continuous(trans=log_trans()) +
    scale_x_continuous(trans=log_trans())