Well..... *_* ,
I think it should have been clear that this was not a question for which
any code exists. In fact, I gave two very specific examples of function
calls. The entire point of my question was not "what's up with my
(putative) code and data " but rather to try to understand the
overarching philosophy of the way lm() treats the function it's given.
I do understand the sneaky ways to make it do a linear fit with or
without forcing the origin. And, sure, I could have run a data set thru
a bunch of different quadratic-like functions to try to see what happens.
Let me pick a more complicated example. The general case of a sin fit
might be Y = a + b*sin(d*x+phi) .(where, to be pedantic, x is the only
data input. All others are coefficients to be found)
If I try y<-lm(yin~I(sin(x))), what is the actual fit function? And so on.
That's why I was hoping for a more general explanation of what lm() does.
Charles C. Berry wrote:
On Mon, 12 Jan 2009, c...@witthoft.com wrote:
[nothing deleted]
matplot(1:100, lm(rnorm(100)~poly(1:100,4),x=T)$x ) # for example
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......^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Charles C. Berry (858) 534-2098
Dept of Family/Preventive
Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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