Dear all,
apologies if this is a very trivial question :-
I have been using the glm.nb procedure, a typical output of which is shown
below. the model returns t-values for each explanatory variable, but I am
interested in the p.values. Is that just to be compared with a t-tables (and
would there be a way to automate that in Splus?), or is there anything more
subtle to do ?
Many thanks!
Helene
Call: glm.nb(formula = nymphs ~ y.coord + aspect + code + heatherheight,
data = splitdata, maxit = 50, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.080031 -0.7430399 -0.6535381 -0.4116674 2.537494
Coefficients:
Value Std. Error
t value
(Intercept) -3.0162209384 0.58795492675 -5.1300207
y.coord 0.0002604797 0.00009523648 2.7350836
aspect 0.0017502784 0.00111105168 1.5753348
code1 0.0713570397 0.19279615354 0.3701165
code2 -0.2234691871 0.12541783923 -1.7817975
heatherheight 0.0398734732 0.01703626392 2.3405057
(Dispersion Parameter for Negative Binomial family taken to be 1 )
Null Deviance: 252.0698 on 358 degrees of freedom
Residual Deviance: 233.3688 on 353 degrees of freedom
Number of Fisher Scoring Iterations: 1
Correlation of Coefficients:
(Intercept) y.coord aspect code1 code2
y.coord -0.5715094
aspect -0.6128368 -0.0672375
code1 0.5101737 -0.1430343 -0.2791874
code2 0.5821637 -0.0920575 -0.3402160 0.5749168
heatherheight -0.7844883 0.2159454 0.3703342 -0.6709585 -0.7514616
Theta: 0.685
Std. Err.: 0.212
2 x log-likelihood: -520.199
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