Reduced major axis line and CI for ggplot in R - r

Question

Is there anyway to add a reduced major axis line (and ideally CI) to a ggplot? I know I can use method="lm" to get an OLS fit, but there doesn't seem to be a default method for RMA. I can get the RMA coefs and the CI interval from package lmodel2, but adding them with geom_abline() doesn't seem to work. Here's dummy data and code. I just want to replace the OLS line and CI with a RMA line and CI:
dat <- data.frame(a=log10(rnorm(50, 30, 10)), b=log10(rnorm(50, 20, 2)))
ggplot(dat, aes(x=a, y=b) ) +
geom_point(shape=1) +
geom_smooth(method="lm")
Edit1: the code below gets the RMA (here called SMA - standardized major axis) coefs and CIs. Package lmodel2 provides more detailed output, while package smatr returns just the coefs and CIs, if that's any help:
library(lmodel2)
fit1 <- lmodel2(b ~ a, data=dat)
library(smatr)
fit2 <- line.cis(b, a, data=dat)

Answer 1

Building off Joran's answer, I think it's a little easier to pass the whole data frame to geom_abline:
library(ggplot2)
library(lmodel2)
dat <- data.frame(a=log10(rnorm(50, 30, 10)), b=log10(rnorm(50, 20, 2)))
mod <- lmodel2(a ~ b, data=dat,"interval", "interval", 99)
reg <- mod$regression.results
names(reg) <- c("method", "intercept", "slope", "angle", "p-value")
ggplot(dat) +
geom_point(aes(b, a)) +
geom_abline(data = reg, aes(intercept = intercept, slope = slope, colour = method))

Answer 2

As Chase commented, the actual lmodel2() code and the ggplot code you are using would be helpful. But here's an example that may point you in the right direction:
dat <- data.frame(a=log10(rnorm(50, 30, 10)), b=log10(rnorm(50, 20, 2)))
mod <- lmodel2(a ~ b, data=dat,"interval", "interval", 99)
#EDIT: mod is a list, with components (data.frames) regression.results and
# confidence.intervals containing the intercepts+slopes for different
# estimation methods; just put the right values into geom_abline
ggplot(dat,aes(x=b,y=a)) + geom_point() +
geom_abline(intercept=mod$regression.results[4,2],
slope=mod$regression.results[4,3],colour="blue") +
geom_abline(intercept=mod$confidence.intervals[4,2],
slope=mod$confidence.intervals[4,4],colour="red") +
geom_abline(intercept=mod$confidence.intervals[4,3],
slope=mod$confidence.intervals[4,5],colour="red") +
xlim(c(-10,10)) + ylim(c(-10,10))
Full disclosure: I know nothing about RMA regression, so I just plucked out the relevent slopes and intercepts and plopped them into geom_abline(), using some example code from lmodel2 as a guide. The CIs produced in this toy example don't seem to make much sense, since I had to force ggplot to zoom out using xlim() and ylim() in order to see the CI lines (red).
But maybe this will help you construct a working example in ggplot().
EDIT2: With OPs added code to extract the coefficients, the ggplot() would be something like this:
ggplot(dat,aes(x=b,y=a)) + geom_point() +
geom_abline(intercept=fit2[1,1],slope=fit2[2,1],colour="blue") +
geom_abline(intercept=fit2[1,2],slope=fit2[2,2],colour="red") +
geom_abline(intercept=fit2[1,3],slope=fit2[2,3],colour="red")

Answer 3

I found myself in the same situation.
Obtain fitted values and their confidence intervals using the ggpmisc package:
cibrary(ggpmisc)
ci <- predict.lmodel2(fit1, method= 'RMA', interval= "confidence")
Add the model predictions to your data:
datci <- cbind(dat, ci)
Plot using geom_smooth arguments like transparency and line width (of course, you can customize them)
p <- ggplot(datci, aes(x= b, y= a)) + geom_point() + geom_line(aes(x= b, y= a)), lwd= 1.1, alpha= 0.6)
Use geom_ribbon if you want to add confidence intervals:
p + geom_ribbon(aes(ymin= lwr, ymax= upr, fill= feather), alpha= 0.3, color= NA)