Good morning,
for my data I've perform a Quasi-Poisson distribution and now I want to
perform a Double Poisson distribution
Could someone tell me if it is possible and how do it in R
I attach below my data
Tanks for help, best regards
Roberta Marino
> intdata<- read.table("dati-traffico-guide.dat")
> intdata
V1 V2 V3 V4 V5
1 1 4 4 21.193913 3.053714
2 2 4 2 8.400438 2.128284
3 3 4 0 20.443000 3.017641
4 4 3 3 14.434567 2.669626
5 5 3 5 28.549402 3.351636
6 6 5 2 8.400438 2.128284
7 7 4 1 3.329605 1.202854
8 8 3 4 21.193913 3.053714
9 9 4 3 14.434567 2.669626
10 10 5 1 3.329605 1.202854
11 11 4 3 14.434567 2.669626
12 12 5 1 3.329605 1.202854
13 13 4 1 3.329605 1.202854
14 14 3 3 14.434567 2.669626
15 15 3 2 8.400438 2.128284
16 16 4 4 21.193913 3.053714
17 17 5 5 28.549402 3.351636
18 18 5 1 3.329605 1.202854
19 19 4 2 8.400438 2.128284
20 20 3 6 36.417738 3.595056
21 21 5 3 14.434567 2.669626
22 22 4 4 21.193913 3.053714
23 23 4 3 14.434567 2.669626
24 24 4 6 36.417738 3.595056
25 25 4 5 28.549402 3.351636
26 26 5 1 3.329605 1.202854
27 27 4 0 15.678000 2.752258
28 28 4 3 14.434567 2.669626
29 29 4 2 8.400438 2.128284
30 30 4 1 3.329605 1.202854
31 31 4 7 44.739851 3.800865
32 32 4 6 36.417738 3.595056
33 33 5 5 28.549402 3.351636
34 34 5 3 14.434567 2.669626
35 35 4 3 14.434567 2.669626
36 36 4 2 8.400438 2.128284
37 37 5 4 21.193913 3.053714
38 38 5 4 21.193913 3.053714
39 39 4 5 28.549402 3.351636
40 40 4 3 14.434567 2.669626
41 41 4 2 8.400438 2.128284
42 42 4 6 36.417738 3.595056
43 43 5 2 8.400438 2.128284
44 44 3 2 8.400438 2.128284
45 45 3 2 8.400438 2.128284
46 46 4 1 3.329605 1.202854
47 47 3 3 14.434567 2.669626
48 48 6 0 40.723000 3.706793
49 49 5 7 44.739851 3.800865
50 50 5 3 14.434567 2.669626
51 51 4 3 14.434567 2.669626
52 52 5 5 28.549402 3.351636
53 53 4 1 3.329605 1.202854
54 54 3 3 14.434567 2.669626
55 55 4 4 21.193913 3.053714
56 56 3 6 36.417738 3.595056
57 57 4 3 14.434567 2.669626
58 58 4 7 44.739851 3.800865
59 59 4 0 46.851000 3.846972
60 60 3 1 3.329605 1.202854
61 61 4 7 44.739851 3.800865
62 62 3 9 62.577007 4.136398
63 63 4 3 14.434567 2.669626
64 64 4 0 13.210000 2.580974
65 65 3 1 3.329605 1.202854
66 66 4 1 3.329605 1.202854
67 67 4 3 14.434567 2.669626
68 68 4 4 21.193913 3.053714
69 69 4 3 14.434567 2.669626
70 70 3 3 14.434567 2.669626
71 71 4 3 14.434567 2.669626
72 72 3 4 21.193913 3.053714
73 73 4 3 14.434567 2.669626
74 74 5 4 21.193913 3.053714
75 75 3 5 28.549402 3.351636
76 76 4 5 28.549402 3.351636
77 77 3 5 28.549402 3.351636
78 78 3 6 36.417738 3.595056
79 79 3 3 14.434567 2.669626
80 80 5 7 44.739851 3.800865
81 81 3 1 3.329605 1.202854
82 82 3 0 5.184000 1.645577
83 83 3 0 7.742000 2.046660
84 84 6 2 8.400438 2.128284
85 85 5 0 80.941000 4.393720
86 86 3 14 112.876553 4.726295
87 87 4 17 146.278901 4.985515
88 88 4 7 44.739851 3.800865
89 89 5 3 14.434567 2.669626
90 90 5 3 14.434567 2.669626
91 91 4 4 21.193913 3.053714
92 92 3 5 28.549402 3.351636
93 93 4 2 8.400438 2.128284
94 94 3 7 44.739851 3.800865
95 95 4 8 53.471253 3.979144
96 96 4 2 8.400438 2.128284
97 97 3 5 28.549402 3.351636
98 98 6 1 3.329605 1.202854
99 99 5 3 14.434567 2.669626
100 100 4 7 44.739851 3.800865
101 101 4 8 53.471253 3.979144
102 102 3 5 28.549402 3.351636
103 103 4 3 14.434567 2.669626
104 104 5 3 14.434567 2.669626
105 105 3 5 28.549402 3.351636
106 106 4 9 62.577007 4.136398
107 107 4 5 28.549402 3.351636
108 108 4 4 21.193913 3.053714
109 109 4 7 44.739851 3.800865
110 110 4 9 62.577007 4.136398
111 111 3 1 3.329605 1.202854
112 112 3 1 3.329605 1.202854
113 113 4 2 8.400438 2.128284
114 114 4 2 8.400438 2.128284
115 115 4 4 21.193913 3.053714
116 116 3 4 21.193913 3.053714
117 117 3 3 14.434567 2.669626
118 118 5 2 8.400438 2.128284
119 119 4 4 21.193913 3.053714
120 120 3 1 3.329605 1.202854
121 121 4 3 14.434567 2.669626
122 122 4 5 28.549402 3.351636
123 123 4 5 28.549402 3.351636
124 124 4 4 21.193913 3.053714
125 125 3 4 21.193913 3.053714
126 126 3 3 14.434567 2.669626
127 127 4 2 8.400438 2.128284
128 128 5 0 6.436000 1.861907
129 129 5 1 3.329605 1.202854
130 130 5 1 3.329605 1.202854
131 131 5 1 3.329605 1.202854
132 132 4 3 14.434567 2.669626
133 133 3 1 3.329605 1.202854
134 134 3 3 14.434567 2.669626
135 135 3 3 14.434567 2.669626
136 136 4 1 3.329605 1.202854
137 137 4 3 14.434567 2.669626
138 138 4 2 8.400438 2.128284
139 139 6 3 14.434567 2.669626
140 140 4 5 28.549402 3.351636
141 141 4 6 36.417738 3.595056
142 142 4 2 8.400438 2.128284
143 143 4 4 21.193913 3.053714
144 144 3 4 21.193913 3.053714
145 145 4 7 44.739851 3.800865
146 146 4 0 30.794000 3.427320
147 147 4 5 28.549402 3.351636
148 148 4 6 36.417738 3.595056
149 149 4 6 36.417738 3.595056
150 150 3 0 38.973000 3.662869
151 151 4 14 112.876553 4.726295
152 152 3 2 8.400438 2.128284
153 153 3 3 14.434567 2.669626
154 154 6 5 28.549402 3.351636
155 155 4 0 23.111000 3.140309
156 156 3 1 3.329605 1.202854
> glmpois <- summary(glm(intdata[,3]~intdata[,5], family=quasipoisson
(link=log),na.action=na.exclude))
> glmpois
Call:
glm(formula = intdata[, 3] ~ intdata[, 5], family = quasipoisson(link log),
na.action = na.exclude)
Deviance Residuals:
Min 1Q Median 3Q Max
-4.3819 0.0868 0.1331 0.2151 0.5854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.90085 0.10679 -8.436 2.23e-14 ***
intdata[, 5] 0.71981 0.03184 22.604 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasipoisson family taken to be 0.3594917)
Null deviance: 300.33 on 155 degrees of freedom
Residual deviance: 105.23 on 154 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 4
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