On Mar 15, 2011, at 10:41 AM, Matthew McKinney wrote:
> I have just started using R so forgive me if this question is very
> simple. I have a data set (in a data frame called dm) that looks like
> this
>
> x (Cells) y(males)
> 1 0
> 2 2
> 3 7
> 4 12
> 5 12
> 6 19
> 7 22
> 8 23
> 9 25
> 10 23
> 11 23
> 12 11
> 13 8
> 14 3
> 15 0
> 16 0
>
>
> I centered the dependent and predictor variables and then squared the
> predictor to make a quadratic variable.
> This left me with 3 variables:
> MalesC
> CellsC
> CellsC2
>
> I then used: >quadraticModel <- lm(MalesC ~ CellsC + CellsC2, data =
> dm)
>
> This has given me R^2= 0.8821, F= 48.63, and p<0.001
>
> I ran the exact same data in sigma plot and got identical results. My
> problem comes from the estimated coefficients I am getting in R when
> using >summary(quadraticModel). My coefficient estimates in sigma plot
> fit my data set well and agree with Microsoft excels estimates.
Around these parts agreement with Excel may be evidence _against_
correctness.
> The
> results from R appear to be the coefficients of a curve fitting the
> residuals. If I >plot(quadraticModel) it does not draw a curve fitting
> my data, but rather the residuals.
You give no basis for your conclusions regarding "fitting residuals",
nor code, nor specific results. This would be a more "standard" way of
using the facilities of R for polynomial regression:
> sec.degr.mod <- lm(y ~ poly(x, 2), data=dm)
> sec.degr.mod
Call:
lm(formula = y ~ poly(x, 2), data = dm)
Coefficients:
(Intercept) poly(x, 2)1 poly(x, 2)2
11.875 -2.386 -34.243
> summary(sec.degr.mod)
Call:
lm(formula = y ~ poly(x, 2), data = dm)
Residuals:
Min 1Q Median 3Q Max
-4.4998 -2.0758 -0.4526 2.7860 4.9534
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.8750 0.8701 13.647 4.40e-09 ***
poly(x, 2)1 -2.3862 3.4805 -0.686 0.505
poly(x, 2)2 -34.2429 3.4805 -9.838 2.17e-07 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Residual standard error: 3.481 on 13 degrees of freedom
Multiple R-squared: 0.8821, Adjusted R-squared: 0.864
F-statistic: 48.63 on 2 and 13 DF, p-value: 9.221e-07
>
> What I would like to know is how I can get the coefficient estimates
> for the data rather than the residuals, and how to plot that in R.
As far as I know those regression coefficients reflect operations the
data.
This produced sensible output:
> plot(dm$x, dm$y)
> lines(dm$x, predict(sec.degr.mod))
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Poly2.plot.predict.pdf
Type: application/pdf
Size: 70438 bytes
Desc: not available
URL:
<https://stat.ethz.ch/pipermail/r-help/attachments/20110315/cb048b9b/attachment.pdf>
-------------- next part --------------
--
David Winsemius, MD
West Hartford, CT