On Jul 9, 2009, at 6:18 PM, Alexander V Stolpovsky wrote:
Dear experts,
I am trying to obtain a function from a model, so that I could
further manipulate it, plot it, etc. I can get model estimates and
manually construct a function, but this gets tedious when trying out
different functions to fit the data. There must be a better way of
doing it, no?
x <- c(1:10)
y <- c(1:10)
fit <- lm(y ~ x)
f <- function(x){fit$coef[1] + fit$coef[2]*x} # Manually
constructing function
# Would be nice to do
something like this:
# f<-getFunction(fit)
plot(f, 0, 10)
Thanks,
Alex Stolpovsky
If you want to get the model fitted y values, just use:
fitted(fit)
As an aside, to get the model coefficients, there is also:
coef(fit)
and
coef(summary(fit))
which like fitted(), is one of several 'extractor' functions that can
be used on model object to get specific components.
If you want to generate model predicted y values based upon 'new' x
values, use:
newdata <- data.frame(x = YourNewValues)
predict(fit, newdata = newdata)
Note that the 'newdata' data frame must contain columns with the SAME
names as the independent variables in your original model.
See ?predict.lm for more information, which can also generate various
intervals, etc.
If you want to plot your original data in a scatterplot and then add
the model fitted line, use:
plot(x, y)
abline(fit)
See ?abline for more information there.
BTW, much of this is covered in An Introduction to R, which is
included in your R installation and on the main R web site under
Manuals link.
HTH,
Marc Schwartz
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