When we fit a statistical model in R, say
lm(y ~ x, data=dat)
We use R's special formula syntax: "y~x"
Is there something that converts from such a formula to the corresponding equation? In this case it could be written as:
y = B0 + B1*x
This would be very useful! For one, because with more complicated formulae I don't trust my translation. Second, in scientific papers written with R/Sweave/knitr, sometimes the model should be reported in equation form and for fully reproducible research, we'd like to do this in automated fashion.
Just had a quick play and got this working:
# define a function to take a linear regression
# (anything that supports coef() and terms() should work)
expr.from.lm <- function (fit) {
# the terms we're interested in
con <- names(coef(fit))
# current expression (built from the inside out)
expr <- quote(epsilon)
# prepend expressions, working from the last symbol backwards
for (i in length(con):1) {
if (con[[i]] == '(Intercept)')
expr <- bquote(beta[.(i-1)] + .(expr))
else
expr <- bquote(beta[.(i-1)] * .(as.symbol(con[[i]])) + .(expr))
}
# add in response
expr <- bquote(.(terms(fit)[[2]]) == .(expr))
# convert to expression (for easy plotting)
as.expression(expr)
}
# generate and fit dummy data
df <- data.frame(iq=rnorm(10), sex=runif(10) < 0.5, weight=rnorm(10), height=rnorm(10))
f <- lm(iq ~ sex + weight + height, df)
# plot with our expression as the title
plot(resid(f), main=expr.from.lm(f))
Seems to have lots of freedom about what variables are called, and whether you actually want the coefficients in there as well—but seems good for a start.
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