我在Rstudio中运行了具有准泊松误差的泊松分布模型
glm(formula = MI ~ corr_data$Temperature + corr_data$Humidity +
corr_data$Sun + corr_data$Rain, family = quasipoisson(),
data = corr_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.5323 -1.1149 -0.1346 0.8591 3.2585
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.9494713 1.2068332 3.273 0.00144 **
corr_data$Temperature -0.0281248 0.0144238 -1.950 0.05381 .
corr_data$Humidity -0.0099800 0.0144047 -0.693 0.48992
corr_data$Sun -0.0767811 0.0414464 -1.853 0.06670 .
corr_data$Rain -0.0003076 0.0004211 -0.731 0.46662
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasipoisson family taken to be 1.873611)
Null deviance: 249.16 on 111 degrees of freedom
Residual deviance: 206.36 on 107 degrees of freedom
(24 observations deleted due to missingness)
我已经读过，色散参数应该理想地接近1
我的累积降雨量值中有一些零值。
我最好如何找到合适的模型？
我接下来尝试了负二项式
Call:
glm.nb(formula = Incidence ~ Humidity + Sun + Rain, data = corr_data,
init.theta = 22.16822882, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.53274 -0.85380 -0.08705 0.73230 2.48643
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.3626266 1.0970701 1.242 0.2142
Humidity 0.0111537 0.0124768 0.894 0.3713
Sun -0.0295395 0.0345469 -0.855 0.3925
Rain -0.0006170 0.0003007 -2.052 0.0402 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Negative Binomial(22.1682) family taken to be 1)
Null deviance: 120.09 on 111 degrees of freedom
Residual deviance: 113.57 on 108 degrees of freedom
(24 observations deleted due to missingness)
AIC: 578.3
Number of Fisher Scoring iterations: 1
Theta: 22.2
Std. Err.: 11.8
2 x log-likelihood: -568.299
非常感谢任何建议。我不熟悉R和建模！