How many parameters are you trying to estimate? How many observations do
you have?
What is wrong is that half of your parameter estimates are statistically
meaningless:
dd <- data.frame(a=c(1, 2, 3, 4, 5, 6), b=c(3, 5, 6, 7, 9, 10))
overparameterized.model <- glm(b~poly(a,3),data=dd)
summary(overparameterized.model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.6667 0.1725 38.644 0.000669 ***
poly(a, 3)1 5.7371 0.4226 13.576 0.005382 **
poly(a, 3)2 -0.1091 0.4226 -0.258 0.820395
poly(a, 3)3 0.2236 0.4226 0.529 0.649562
Charles Annis, P.E.
[EMAIL PROTECTED]
phone: 561-352-9699
eFax: 614-455-3265
http://www.StatisticalEngineering.com
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Jarek Jasiewicz
Sent: Saturday, January 12, 2008 11:50 AM
To: R-help@r-project.org
Subject: [R] glm expand model to more values
Hi
I have the problem with fitting curve to data with lm and glm. When I
use polynominal dependiency, fitted values from model are OK, but I
cannot recive proper values when I use coefficents to caltulate this.
Let me present simple example:
I have simple data.frame: (dd)
a: 1 2 3 4 5 6
b: 3 5 6 7 9 10
I try to fit it to model:
model=glm(b~poly(a,3),data=dd)
I have following data fitted to model (as I expected)
> fitted(model)
1 2 3 4 5 6
3.095238 4.738095 6.095238 7.333333 8.619048 10.119048
and coef(model)
(Intercept) poly(a, 3)1 poly(a, 3)2 poly(a, 3)3
6.6666667 5.7370973 -0.1091089 0.2236068
so when I try to expand the model to other data (simple extrapolation),
let say: s=seq(1:10,by=1)
I do:
extra=sapply(s,function(x) coef(model) %*% x^(0:3))
and here is result:
[1] 12.51826 19.49328 28.93336 42.18015 60.57528 85.46040 118.17714
[8] 160.06715 212.47207 276.73354
the data form expanding coefs are completly differnd from fitted
What's going wrong?
Jarek
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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
