How to overlay a line for an lm object on a ggplot2 scatterplot - graphics

Question

I have some data,
calvarbyruno.1<-structure(list(Nominal = c(1, 3, 6, 10, 30, 50, 150, 250), Run = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("1", "2", "3"), class = "factor"),
PAR = c(1.25000000000000e-05, 0.000960333333333333, 0.00205833333333334,
0.00423333333333333, 0.0322333333333334, 0.614433333333334,
1.24333333333333, 1.86333333333333), PredLin = c(-0.0119152187070942,
0.00375925114245899, 0.0272709559167888, 0.0586198956158952,
0.215364594111427, 0.372109292606959, 1.15583278508462, 1.93955627756228
), PredQuad = c(-0.0615895732702735, -0.0501563307416599,
-0.0330831368244257, -0.0104619953693943, 0.100190275883806,
0.20675348710041, 0.6782336426345, 1.04748729725370)), .Names = c("Nominal",
"Run", "PAR", "PredLin", "PredQuad"), row.names = c(NA, 8L), class = "data.frame")
calweight <- -2
for which I've created both a linear and a quadratic lm model
callin.1<-lm(PAR~Nominal,data=calvarbyruno.1,weight=Nominal^calweight)
calquad.1<-lm(PAR~Nominal+I(Nominal^2),data=calvarbyruno.1,weight=Nominal^calweight)
I can then plot my data values using ggplot2
qplot(PAR,Nominal,data=calvarbyruno.1)
But can't work out how to overlay a line representing the two lm objects... Any ideas ?

Answer 1

The easiest option is to use geom_smooth() and let ggplot2 fit the model for you.
ggplot(calvarbyruno.1, aes(y = PAR, x = Nominal, weight=Nominal^calweight)) +
geom_smooth(method = "lm") +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), colour = "red") +
geom_point() +
coord_flip()
Or you can create a new dataset with the predicted values.
newdata <- data.frame(Nominal = pretty(calvarbyruno.1$Nominal, 100))
newdata$Linear <- predict(callin.1, newdata = newdata)
newdata$Quadratic <- predict(calquad.1, newdata = newdata)
require(reshape2)
newdata <- melt(newdata, id.vars = "Nominal", variable.name = "Model")
ggplot(calvarbyruno.1, aes(x = PAR, y = Nominal, weight=Nominal^calweight)) +
geom_line(data = newdata, aes(x = value, colour = Model)) +
geom_point()

Answer 2

Earlier I asked a related question and Hadley had this good answer. Using the predict function from that post you can add two columns to your data. One for each model:
calvarbyruno.1$calQuad <- predict(calquad.1)
calvarbyruno.1$callin <- predict(callin.1)
Then it's a matter of plotting the point and adding each model in as a line:
ggplot() +
geom_point(data=calvarbyruno.1, aes(PAR, Nominal), colour="green") +
geom_line(data=calvarbyruno.1, aes(calQuad, Nominal), colour="red" ) +
geom_line(data=calvarbyruno.1, aes(callin, Nominal), colour="blue" ) +
opts(aspect.ratio = 1)
And that results in this nice picture (yeah the colors could use some work):
(source: cerebralmastication.com)