I'm using R 2.10.1 with the latest version of all packages (updated today).
I'm confused as to why I'm getting a hard singularity in a simple set
of experimental data:
> blots
ID Lot Age Conc
1 1 A 3 4.44
2 2 A 3 4.56
3 3 B 41 4.03
4 4 B 41 4.57
5 5 C 229 4.49
6 6 C 229 4.66
7 7 D 238 3.88
8 8 D 238 3.93
9 9 E 349 4.43
10 10 E 349 4.22
11 11 F 391 4.42
12 12 F 391 4.46
> fit2<- lm(Conc ~ Age + Lot, data=blots)
> summary(fit2)
Call:
lm(formula = Conc ~ Age + Lot, data = blots)
Residuals:
Min 1Q Median 3Q Max
-2.700e-01 -6.625e-02 6.072e-17 6.625e-02 2.700e-01
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.5004639 0.1273234 35.347 3.42e-08 ***
Age -0.0001546 0.0004605 -0.336 0.7485
LotB -0.1941237 0.1706005 -1.138 0.2986
LotC 0.1099485 0.1554378 0.707 0.5059
LotD -0.5586598 0.1558853 -3.584 0.0116 *
LotE -0.1214948 0.1698331 -0.715 0.5013
LotF NA NA NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
Residual standard error: 0.1787 on 6 degrees of freedom
Multiple R-squared: 0.7464, Adjusted R-squared: 0.535
F-statistic: 3.532 on 5 and 6 DF, p-value: 0.07811
Why the NA's here?
===============================================================Robert A.
LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com
Least Cost Formulations, Ltd. URL: http://lcfltd.com/
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Virginia Beach, VA 23464-3239 Fax: 757-467-2947
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