Psigh! Why do people think that it is perfectly OK to undertake
statistical analyses without knowing or understanding any statistics?
(I guess it's slightly less dangerous than undertaking to do your own
wiring without knowing anything about being an electrician, but still ....)
However, to stop venting and answer your question: It is because
"CDSTotal" is perfectly confounded (in the given design) with the other
predictors. That is, CDSTotal is exactly equal to a linear combination
of the other predictors (and the constant "1").
Try:
lm(CDSTotal ~ Age + Gender + LOC + PC + Stability, data=Controlgroup)
and you will find that the error sum of squares is zero (to within
numerical tolerance).
cheers,
Rolf Turner
--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
On 22/07/15 06:56, matthewjones43 wrote:
Hi, I am not a statistician and so I am sure whatever it is I am doing wrong
must be an obvious error for those who are...Basically I can not understand
why I get NA for variable 'CDSTotal' when running a glm? Does anyone have an
idea of why this might be happening?
Call: glm(formula = cbind(SRAS - 26, 182 - SRAS) ~ Age + Gender + LOC +
PC + Stability + CDSTotal, family = binomial, data = Controlgroup)
Coefficients:
(Intercept) Age Gender LOC PC Stability
-2.575071 0.009148 0.354143 0.018295 -0.011317 0.090759
CDSTotal
NA
Degrees of Freedom: 64 Total (i.e. Null); 59 Residual
Null Deviance: 2015
Residual Deviance: 1264 AIC: 1614
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