David,
I was working on a project involving a linear model, and wanted to
extract the standard error of a predictor. I am able to do so, but not
in the way I would expect.
I would have expected that if a created a model such as Model1 <-
lm(y~x,z,d), the object Model1 would contain that information even
though it does not print it out when I simply type Model1.
You can always see what information objects contain by using the ?str
function on them.
In this instance, str(Model1) will show the components of the Model1
object.
I would also
have (wrongly) suspected that if I type summary(Model1) R would simply
look at the object Model1 and find whatever it needs. But it doesn't
work that way.
Correct. You can always see what a function does by printing its
definition at the R prompt. In this case, typing summary.lm will
show you what the summary function does when passed an 'lm' object.
If I want that standard error I have to first create a
summary of Model1 and then extract the standard error from the summary
with something like summary(Model1)$coefficients or, more specifically,
summary(Model)$coefficients[2,2].
Use the ?coef function for this purpose, which works with most modelling
functions, including 'lm'.
[I know that I can cram all of that
into one line if I want to.] But doesn't that mean that when I ask for a
summary R has to recreate the linear model all over again before pulling
out the standard error. (Venables and Ripley, p. 77)
Which book? I don't think MASS 4th edition.
suggest that this
could happen if the method is not written correctly, but how is it not
happening anyway?)
The Model1 object contains the necessary components to calculate the
quantity. Look at how it's done in summary.lm.
And if so, if Model1 doesn't contain the raw data,
how does summary produce an answer even if I delete one of the variables
before calling it?
By default, an 'lm' object will contain the model.matrix. See the ?lm
value section for other components.
In general, it would be bad practice to have a function like summary.lm
depend not only on a supplied argument, but on some other object that it
is not a function of being present in the workspace.
As you can see, I have figured out how to get what I want, but I don't
understand the process of building objects, which is the important thing
to understand. Perhaps I don't understand "methods" well enough.
I think a combination of the ?str function, looking at the actual lm and
summary.lm functions, and a careful reading of the help pages will help.
Below is sample code:
#Sample for linear model
x <- c(3,7,9,15,18)
y <- c(5,4,8,6,9)
reg <- lm(y~x)
reg
#Produces only the regression coefficients and using str(reg) indicates
that
# that is all that it has.
regsummary <- summary(reg)
#Produces what I need and str(regsummary) shows that st. errors are part
of the object.
regsummary$coefficients[1:2, 1:4]
rm(y)
out <- summary(reg)
# works just fine although y is no longer available and reg doesn't look
like it
# could supply it.
reg$model
reg$qr
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