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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