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Jun 26th, 2019
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  1. > summary(p1)
  2.  
  3.  
  4.  
  5. Pearson residuals:
  6. Min 1Q Median 3Q Max
  7. -4.1235 -1.3127 -0.7073 1.2650 13.3738
  8.  
  9. Count model coefficients (poisson with log link):
  10. Estimate Std. Error z value Pr(>|z|)
  11. (Intercept) 11.974985 0.001449 8263.627 <2e-16 ***
  12. Day2 1.841290 0.001498 1229.524 <2e-16 ***
  13. GroupE -0.058573 0.002080 -28.160 <2e-16 ***
  14. GroupL -0.022356 0.002307 -9.692 <2e-16 ***
  15. Day2:GroupE 0.033049 0.002131 15.506 <2e-16 ***
  16. Day2:GroupL -0.510861 0.002359 -216.575 <2e-16 ***
  17.  
  18. Zero-inflation model coefficients (binomial with log link):
  19. Estimate Std. Error z value Pr(>|z|)
  20. (Intercept) -0.6933 0.4083 -1.698 0.0895 .
  21. Day2 -0.8112 0.7455 -1.088 0.2766
  22. GroupE 0.2876 0.4715 0.610 0.5419
  23. GroupL 0.5110 0.4282 1.193 0.2328
  24. Day2:GroupE -1.4917 1.2431 -1.200 0.2301
  25. Day2:GroupL -1.8972 1.2317 -1.540 0.1235
  26. ---
  27. Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  28.  
  29. Number of iterations in BFGS optimization: 14
  30. Log-likelihood: -1.208e+07 on 12 Df
  31.  
  32. > summary(m1)
  33.  
  34.  
  35. Deviance Residuals:
  36. Min 1Q Median 3Q Max
  37. -1349.5 -531.6 -227.5 311.6 2480.8
  38.  
  39. Coefficients:
  40. Estimate Std. Error z value Pr(>|z|)
  41. (Intercept) 11.281836 0.001449 7785.3 <2e-16 ***
  42. Day2 2.283125 0.001498 1524.6 <2e-16 ***
  43. GroupE -0.464042 0.002080 -223.1 <2e-16 ***
  44. GroupL -1.120968 0.002307 -486.0 <2e-16 ***
  45. Day2:GroupE 0.620839 0.002131 291.3 <2e-16 ***
  46. Day2:GroupL 0.781906 0.002359 331.5 <2e-16 ***
  47. ---
  48. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  49.  
  50. (Dispersion parameter for poisson family taken to be 1)
  51.  
  52. Null deviance: 57480533 on 68 degrees of freedom
  53. Residual deviance: 33466708 on 63 degrees of freedom
  54. AIC: 33467398
  55.  
  56. Number of Fisher Scoring iterations: 7
  57.  
  58. p1=zeroinfl(Count ~ Day*Group, link = "log", dist="poisson", data = df)
  59. m1=glm(formula = Count ~ Day*Group, family = poisson(link="log", data = df)
  60. vuong(p1,m1)
  61.  
  62.  
  63. Day Group Count
  64. 2 E 343874
  65. 2 E 1218298
  66. 2 E 1368672
  67. 2 E 354983
  68. 2 E 237219
  69. 2 E 635455
  70. 2 E 382917
  71. 2 E 596436
  72. 2 E 1759721
  73. 2 C 271107
  74. 2 C 406661
  75. 2 C 2277301
  76. 2 C 0
  77. 2 C 232378
  78. 2 C 3197440
  79. 2 C 0
  80. 2 C 388092
  81. 2 C 232378
  82. 2 L 813322
  83. 2 L 1138650
  84. 2 L 881098
  85. 2 L 46936
  86. 2 L 921765
  87. 2 L 433772
  88. 2 L 1358359
  89. 2 L 894074
  90. 2 L 990795
  91. 2 L 38730
  92. 2 L 0
  93. 2 L 542214
  94. 2 L 77459
  95. 2 L 154918
  96. 2 L 209319
  97. 2 L 162664
  98. 2 L 271107
  99. 2 L 1046595
  100. 2 E 1026504
  101. 2 E 203330
  102. 2 E 4164301
  103. 2 E 715259
  104. 2 E 0
  105. 2 E 650657
  106. 1 E 0
  107. 1 E 165952
  108. 1 E 162664
  109. 1 E 0
  110. 1 E 0
  111. 1 E 0
  112. 1 C 0
  113. 1 C 171206
  114. 1 C 271107
  115. 1 C 0
  116. 1 C 33888
  117. 1 C 0
  118. 1 L 0
  119. 1 L 0
  120. 1 L 0
  121. 1 L 90369
  122. 1 L 0
  123. 1 L 220080
  124. 1 L 0
  125. 1 L 0
  126. 1 L 0
  127. 1 L 0
  128. 1 L 0
  129. 1 L 0
  130. 1 E 120492
  131. 1 E 0
  132. 1 E 0
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