glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control = list(...), model = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, ...)
>Orange #R growth of orange trees dataset
Tree age circumference 1 1 118 30 2 1 484 58 3 1 664 87 4 1 1004 115 5 1 1231 120 6 1 1372 142 7 1 1582 145 8 2 118 33 9 2 484 69 10 2 664 111 11 2 1004 156 12 2 1231 172 13 2 1372 203 14 2 1582 203 15 3 118 30 16 3 484 51 17 3 664 75 18 3 1004 108 19 3 1231 115 20 3 1372 139 21 3 1582 140 22 4 118 32 23 4 484 62 24 4 664 112 25 4 1004 167 26 4 1231 179 27 4 1372 209 28 4 1582 214 29 5 118 30 30 5 484 49 31 5 664 81 32 5 1004 125 33 5 1231 142 34 5 1372 174 35 5 1582 177
> attach(Orange)#put age, Tree, circumference into R search path > g <- glm(circumference ~ age + Tree) > g
Call: glm(formula = circumference ~ age + Tree) Coefficients: (Intercept) age Tree.L Tree.Q Tree.C Tree^4 17.3997 0.1068 39.9350 2.5199 -8.2671 -4.6955 Degrees of Freedom: 34 Total (i.e. Null); 29 Residual Null Deviance: 112400 Residual Deviance: 6754 AIC: 297.5
>summary(g)
Call: glm(formula = circumference ~ age + Tree) Deviance Residuals: Min 1Q Median 3Q Max -30.505 -8.790 3.737 7.650 21.859 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 17.399650 5.543461 3.139 0.00388 ** age 0.106770 0.005321 20.066 < 2e-16 *** Tree.L 39.935049 5.768048 6.923 1.31e-07 *** Tree.Q 2.519892 5.768048 0.437 0.66544 Tree.C -8.267097 5.768048 -1.433 0.16248 Tree^4 -4.695541 5.768048 -0.814 0.42224 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 232.8927) Null deviance: 112366.3 on 34 degrees of freedom Residual deviance: 6753.9 on 29 degrees of freedom AIC: 297.51 Number of Fisher Scoring iterations: 2
