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我想知道是否有人可以就我运行的负二项式模型给我一些建议。基本上我试图通过各种分类变量和一些连续变量(例如权重1)来预测人数(数字)。样本组成数据如下(省略一些变量):

**Designation   Habitat     Year    Weight1 Day         DayBro  Type_Day    People**   
SPA/SAC     Heathland   2004    200     Tuesday     NA  Weekday Autumn  10  
SPA/SAC     Heathland   2004    450     Tuesday     NA  Weekday Autumn  0  
SPA/SAC     Heathland   2004    5000    Tuesday     NA  Weekday Autumn  7  
SPA/SAC     Heathland   2004    60      Tuesday     NA  Weekday Autumn  0  
SSSI        Heathland   2004    800     Sunday      NA  Weekend Autumn  6  
SSSI        Heathland   2004    3000    Sunday      NA  Weekend Autumn  9  
SANG        Heathland   2004    20      Saturday    NA  Weekend Autumn  50  
SANG        Heathland   2004    60      Saturday    NA  Weekend Autumn  3  
SPA/SAC     Heathland   2004    50      Wednesday   NA  Weekday Autumn  88  
SPA/SAC     Heathland   2004    50      Wednesday   NA  Weekday Autumn  0  
SPA/SAC     Heathland   2004    70      Wednesday   NA  Weekday Autumn  5  

运行命令

model.nb = glm.nb(People2 ~ DayBro + Designation + Habitat + Type_Day + 
Season + Designation + Weight1 + Weight2 + Weight3, data = TAA3)

我得到以下结果

Call:
glm.nb(formula = People2 ~ Weight1 + Weight2 + Weight3 + DayBro + 
Designation + Habitat + Type_Day + Season + Designation, 
data = TAA3, init.theta = 0.7571378169, link = log)

Deviance Residuals: 
Min       1Q   Median       3Q      Max  
-2.7536  -1.0111  -0.4665   0.1315   3.7172  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)       3.883e+00  1.510e-01  25.720  < 2e-16 ***
Weight1          -2.581e-05  7.230e-06  -3.570 0.000357 ***
Weight2           3.057e-04  5.151e-05   5.935 2.94e-09 ***
Weight3          -6.328e-03  1.453e-03  -4.354 1.34e-05 ***
DayBroTerm       -3.345e-01  9.597e-02  -3.486 0.000490 ***
DesignationSSSI   2.540e-01  2.183e-01   1.164 0.244573    
DesignationSANG   2.533e-01  1.643e-01   1.542 0.123089    
DesignationpSANG  5.618e-01  1.838e-01   3.056 0.002241 ** 
HabitatGrassland -7.616e-01  1.641e-01  -4.641 3.46e-06 ***
HabitatHeathland -4.467e-01  1.535e-01  -2.909 0.003624 ** 
HabitatMixed     -3.555e-02  1.204e-01  -0.295 0.767751    
HabitatWetland   -3.569e-01  1.696e-01  -2.104 0.035348 *  
HabitatWoodland  -3.283e-01  2.642e-01  -1.242 0.214065    
Type_DayWeekend   4.860e-01  7.802e-02   6.229 4.69e-10 ***
SeasonSummer      1.580e-01  1.353e-01   1.168 0.242926    
SeasonAutumn      3.756e-01  1.520e-01   2.471 0.013482 *  
SeasonWinter      1.332e-01  1.275e-01   1.045 0.295982    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.7571) family taken to be 1)

Null deviance: 1174.5  on 885  degrees of freedom
Residual deviance: 1049.3  on 869  degrees of freedom
(114 observations deleted due to missingness)
AIC: 8674

Number of Fisher Scoring iterations: 1


          Theta:  0.7571 
      Std. Err.:  0.0332 

 2 x log-likelihood:  -8637.9990 

由于估计值和显着性值的差异,我对这些结果有点吃惊。Weight1(数值预测器)具有非常低的估计并且非常显着,而 DesignationSSSI(分类预测器)具有更高的估计并且不显着。我知道,对于分类预测变量,它指的是基础类别,而对于 Weight1,它代表 Weight1 的单位增加。

有人对此有任何意见/建议吗?这些结果是否有意义?或者模型语法(等)中的某些内容可能指定错误?

非常感谢您的帮助!

达米亚诺

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