R glm Function


glm() function fits linear models to the dataset.

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