summary.negbin.RdIdentical to summary.glm, but with three lines of additional output: the
ML estimate of theta, its standard error, and twice the log-likelihood
function.
# S3 method for negbin summary(object, dispersion = 1, correlation = FALSE, ...)
| object | fitted model object of class |
|---|---|
| dispersion | as for |
| correlation | as for |
| ... | arguments passed to or from other methods. |
As for summary.glm; the additional lines of output are not included in
the resultant object.
A summary table is produced as for summary.glm, with the additional
information described above.
summary.glm is used to produce the majority of the output and supply the
result.
This function is a method for the generic function
summary() for class "negbin".
It can be invoked by calling summary(x) for an
object x of the appropriate class, or directly by
calling summary.negbin(x) regardless of the
class of the object.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
#> #> Call: #> glm.nb(formula = Days ~ Eth * Age * Lrn * Sex, data = quine, #> link = log, init.theta = 1.928360145) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -3.2377 -0.9079 -0.2019 0.5173 1.7043 #> #> Coefficients: (4 not defined because of singularities) #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) 3.0564 0.3760 8.128 4.38e-16 *** #> EthN -0.1386 0.5334 -0.260 0.795023 #> AgeF1 -0.6227 0.5125 -1.215 0.224334 #> AgeF2 -2.3632 1.0770 -2.194 0.028221 * #> AgeF3 -0.3784 0.4546 -0.832 0.405215 #> LrnSL -1.9577 0.9967 -1.964 0.049493 * #> SexM -0.4914 0.5104 -0.963 0.335653 #> EthN:AgeF1 0.1029 0.7123 0.144 0.885175 #> EthN:AgeF2 -0.5546 1.6798 -0.330 0.741297 #> EthN:AgeF3 0.0633 0.6396 0.099 0.921159 #> EthN:LrnSL 2.2588 1.3019 1.735 0.082743 . #> AgeF1:LrnSL 2.6421 1.0821 2.442 0.014618 * #> AgeF2:LrnSL 4.8585 1.4423 3.369 0.000755 *** #> AgeF3:LrnSL NA NA NA NA #> EthN:SexM -0.7524 0.7220 -1.042 0.297400 #> AgeF1:SexM 0.4092 0.8299 0.493 0.621973 #> AgeF2:SexM 3.1098 1.1655 2.668 0.007624 ** #> AgeF3:SexM 1.1145 0.6365 1.751 0.079926 . #> LrnSL:SexM 1.5900 1.1499 1.383 0.166750 #> EthN:AgeF1:LrnSL -3.5493 1.4270 -2.487 0.012876 * #> EthN:AgeF2:LrnSL -3.3315 2.0919 -1.593 0.111256 #> EthN:AgeF3:LrnSL NA NA NA NA #> EthN:AgeF1:SexM -0.3105 1.2055 -0.258 0.796735 #> EthN:AgeF2:SexM 0.3469 1.7965 0.193 0.846875 #> EthN:AgeF3:SexM 0.8329 0.8970 0.929 0.353092 #> EthN:LrnSL:SexM -0.1639 1.5250 -0.107 0.914411 #> AgeF1:LrnSL:SexM -2.4285 1.4201 -1.710 0.087246 . #> AgeF2:LrnSL:SexM -4.1914 1.6201 -2.587 0.009679 ** #> AgeF3:LrnSL:SexM NA NA NA NA #> EthN:AgeF1:LrnSL:SexM 2.1711 1.9192 1.131 0.257963 #> EthN:AgeF2:LrnSL:SexM 2.1029 2.3444 0.897 0.369718 #> EthN:AgeF3:LrnSL:SexM NA NA NA NA #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> (Dispersion parameter for Negative Binomial(1.9284) family taken to be 1) #> #> Null deviance: 272.29 on 145 degrees of freedom #> Residual deviance: 167.45 on 118 degrees of freedom #> AIC: 1097.3 #> #> Number of Fisher Scoring iterations: 1 #> #> #> Theta: 1.928 #> Std. Err.: 0.269 #> #> 2 x log-likelihood: -1039.324