on 07/26/2008 10:37 AM [EMAIL PROTECTED] wrote:
I have ussed lm to generate a basic line correlation:
fit = lm(hours.of.sleep ~ ToSleep)
Note: From "Bayesian Computation with R", Jim Albert, p. 7
I understand the simple y = mx + b line that this fits the data to.
Now apparently I don't understand formulas. The documentation
indicates that there is an implied "intercept" in the formula so now
I want to try and fit the line to a second degree polynomial so I
tried:
ft = lm(hours.of.sleep ~ ToSleep ^ 2 + ToSleep)
and I still seem to get results that indicate a slope intercept, y =
mx + b, type of fit. Can anyone give me a short tutorial on the
formula syntax? I would like to fit the data to 2nd and higher order
polynomials, 1 / x, log(x), etc. I am sorry but I could not glean
this information from the help page on lm.
The help system is not intended to be a tutorial, but a reference
showing the syntax of function calls, what they do, what they return,
some potential gotchas, references/citations and a _limited_ number of
common examples of use.
The first place to start is to read "An Introduction to R", which is
available with your R installation and online at:
http://cran.r-project.org/doc/manuals/R-intro.html
The apropos section in that document is "Statistical models in R":
http://cran.r-project.org/doc/manuals/R-intro.html#Statistical-models-in-R
which provides more in-depth examples, including your situation.
There are also other books available for R listed here:
http://www.r-project.org/doc/bib/R-books.html
which will provide more generalized overviews of R, since subject
focused books, such as Jim Albert's, will by necessity, have rather
brief introductions to the language.
There are also some contributed documents here:
http://cran.r-project.org/other-docs.html
HTH,
Marc Schwartz
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