检查残差并可视化零膨胀泊松 r
examining residuals and visualizing zero-inflated poission r
我是 运行 CPUE 数据的零膨胀模型。该数据具有零 inflation 的证据,我已通过 Vuong 测试(在下面的代码中)确认了这一点。根据 AIC,完整模型 (zint) 优于空模型。我现在想要:
- 检查完整模型的残差以确定模型拟合(由于缺乏来自同事、互联网和 R 书籍的信息而遇到问题)
- 如果模型拟合似乎没问题,可视化模型的输出(使用偏移变量时如何制定实参数值的方程)
我已经向该部门的一些统计人员寻求帮助(他们以前从未这样做过,并将我发送到相同的 google 搜索站点),在统计部门之外(每个人都太忙了)和 Whosebug 提要。
我希望有代码或书籍指南(在线免费提供),其中包含处理可视化 ZIP 和使用偏移变量时模型拟合的代码。
yc=read.csv("CPUE_ycs_trawl_withcobb_BLS.csv",header=TRUE)
yc=yc[which(yc$countinyear<150),]
yc$fyear=as.factor(yc$year_cap)
yc$flocation=as.factor(yc$location)
hist(yc$countinyear,20)
yc$logoffset=log(yc$numtrawlyr)
###Run Zero-inflated poisson with offset for CPUE####
null <- formula(yc$countinyear ~ 1| 1)
znull <- zeroinfl(null, offset=logoffset,dist = "poisson",link = "logit",
data = yc)
int <- formula(yc$countinyear ~ assnage * spawncob| assnage * spawncob)
zint <- zeroinfl(int, offset=logoffset,dist = "poisson",link = "logit", data
= yc)
AIC(znull,zint)
g1=glm(countinyear ~ assnage * spawncob,
offset=logoffset,data=yc,family=poisson)
summary(g1)
####Vuong test to see if ZIP is even needed##
vuong(g1,zint)
##########DATASET###########
countinyear 是第 1 列
##########DATASET###########
count assnage spawncob logoffset
56 0 0.32110173 2.833213
44 1 0.33712 2.833213
60 2 0.34053264 2.833213
0 4 0.19381496 2.833213
1 3 0.30819333 2.833213
33 0 0.32110173 2.833213
40 1 0.33712 2.833213
25 2 0.34053264 2.833213
0 3 0.30819333 2.833213
2 4 0.19381496 2.833213
6 0 0.32110173 2.833213
13 1 0.33712 2.833213
7 2 0.34053264 2.833213
0 3 0.30819333 2.833213
0 4 0.19381496 NA
5 0 0.32110173 2.833213
31 1 0.33712 2.833213
73 2 0.34053264 2.833213
0 3 0.30819333 2.833213
1 4 0.19381496 2.833213
0 0 0.32110173 2.833213
7 1 0.33712 2.833213
75 2 0.34053264 2.833213
3 3 0.30819333 2.833213
0 4 0.19381496 2.833213
19 0 0.32110173 2.833213
13 1 0.33712 2.833213
18 2 0.34053264 2.833213
0 3 0.30819333 2.833213
2 4 0.19381496 2.833213
11 0 0.32110173 2.833213
14 1 0.33712 2.833213
32 2 0.34053264 2.833213
1 3 0.30819333 2.833213
1 4 0.19381496 2.833213
12 0 0.32110173 2.833213
3 1 0.33712 2.833213
9 2 0.34053264 2.833213
2 3 0.30819333 2.833213
0 4 0.19381496 2.833213
5 0 0.32110173 2.833213
15 1 0.33712 2.833213
22 2 0.34053264 2.833213
5 3 0.30819333 2.833213
1 4 0.19381496 2.833213
1 0 0.32110173 2.833213
16 1 0.33712 2.833213
33 2 0.34053264 2.833213
4 3 0.30819333 2.833213
2 4 0.19381496 2.833213
6 0 0.32110173 2.833213
17 1 0.33712 2.833213
26 2 0.34053264 2.833213
1 3 0.30819333 2.833213
0 4 0.19381496 2.833213
16 0 0.32110173 2.833213
16 1 0.33712 2.833213
11 2 0.34053264 2.833213
1 3 0.30819333 2.833213
1 4 0.19381496 2.833213
2 0 0.32110173 2.833213
8 1 0.33712 2.833213
18 2 0.34053264 2.833213
0 3 0.30819333 2.833213
0 4 0.19381496 2.833213
2 0 0.32110173 2.833213
27 1 0.33712 2.833213
49 2 0.34053264 2.833213
1 3 0.30819333 2.833213
0 4 0.19381496 2.833213
1 0 0.32110173 2.833213
6 1 0.33712 2.833213
36 2 0.34053264 2.833213
17 3 0.30819333 2.833213
0 4 0.19381496 2.833213
10 0 0.32110173 2.833213
21 1 0.33712 2.833213
78 2 0.34053264 2.833213
32 3 0.30819333 2.833213
0 4 0.19381496 2.833213
0 0 0.32110173 2.833213
8 1 0.33712 2.833213
14 2 0.34053264 2.833213
7 3 0.30819333 2.833213
0 4 0.19381496 2.833213
0 1 0.13648433 2.833213
6 1 0.23952033 2.833213
12 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
30 0 0.13648433 2.833213
30 1 0.23952033 2.833213
25 2 0.32110173 2.833213
30 3 0.33712 2.833213
30 4 0.34053264 2.833213
68 0 0.13648433 2.833213
68 1 0.23952033 2.833213
55 2 0.32110173 2.833213
68 3 0.33712 2.833213
68 4 0.34053264 2.833213
0 0 0.13648433 2.833213
12 1 0.23952033 2.833213
26 2 0.32110173 2.833213
2 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.13648433 2.833213
17 1 0.23952033 2.833213
36 2 0.32110173 2.833213
1 3 0.33712 2.833213
4 4 0.34053264 2.833213
1 0 0.13648433 2.833213
1 1 0.23952033 2.833213
4 2 0.32110173 2.833213
4 3 0.33712 2.833213
0 4 0.34053264 2.833213
3 0 0.13648433 2.833213
3 1 0.23952033 2.833213
3 2 0.32110173 2.833213
3 3 0.33712 2.833213
3 4 0.34053264 2.833213
0 0 0.13648433 2.833213
29 1 0.23952033 2.833213
33 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
10 1 0.23952033 2.833213
7 2 0.32110173 2.833213
1 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
6 1 0.23952033 2.833213
18 2 0.32110173 2.833213
1 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
18 1 0.23952033 2.833213
37 2 0.32110173 2.833213
1 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.13648433 2.833213
13 1 0.23952033 2.833213
26 2 0.32110173 2.833213
8 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
0 1 0.23952033 2.833213
1 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
1 1 0.23952033 2.833213
5 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
29 1 0.23952033 2.833213
15 2 0.32110173 2.833213
2 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
19 1 0.23952033 2.833213
25 2 0.32110173 2.833213
3 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.13648433 2.833213
24 1 0.23952033 2.833213
40 2 0.32110173 2.833213
6 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.03678637 2.772589
28 1 0.07414634 2.772589
28 2 0.13648433 2.772589
3 3 0.23952033 2.772589
2 4 0.32110173 2.772589
0 0 0.03678637 2.772589
3 1 0.07414634 2.772589
2 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
4 0 0.03678637 2.772589
14 1 0.07414634 2.772589
6 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
6 1 0.07414634 2.772589
3 2 0.13648433 2.772589
2 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
8 1 0.07414634 2.772589
2 2 0.13648433 2.772589
4 3 0.23952033 2.772589
1 4 0.32110173 2.772589
1 0 0.03678637 2.772589
12 1 0.07414634 2.772589
23 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
24 1 0.07414634 2.772589
56 2 0.13648433 2.772589
7 3 0.23952033 2.772589
4 4 0.32110173 2.772589
0 0 0.03678637 2.772589
22 1 0.07414634 2.772589
45 2 0.13648433 2.772589
3 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
2 1 0.07414634 2.772589
18 2 0.13648433 2.772589
1 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
5 1 0.07414634 2.772589
18 2 0.13648433 2.772589
5 3 0.23952033 2.772589
1 4 0.32110173 2.772589
0 0 0.03678637 2.772589
9 1 0.07414634 2.772589
25 2 0.13648433 2.772589
6 3 0.23952033 2.772589
1 4 0.32110173 2.772589
0 0 0.03678637 2.772589
1 1 0.07414634 2.772589
3 2 0.13648433 2.772589
1 3 0.23952033 2.772589
1 4 0.32110173 2.772589
0 0 0.03678637 2.772589
3 1 0.07414634 2.772589
16 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
7 1 0.07414634 2.772589
21 2 0.13648433 2.772589
8 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
5 1 0.07414634 2.772589
22 2 0.13648433 2.772589
6 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
11 1 0.07414634 2.772589
22 2 0.13648433 2.772589
6 3 0.23952033 2.772589
0 4 0.32110173 2.772589
1 0 0.11532605 2.564949
7 1 0.05628636 2.564949
11 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
4 1 0.05628636 2.564949
4 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
0 1 0.05628636 2.564949
5 2 0.03678637 2.564949
0 3 0.07414634 2.564949
1 4 0.13648433 2.564949
0 0 0.11532605 2.564949
3 1 0.05628636 2.564949
4 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
3 1 0.05628636 2.564949
0 2 0.03678637 2.564949
1 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
1 1 0.05628636 2.564949
0 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
6 1 0.05628636 2.564949
9 2 0.03678637 2.564949
3 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
3 1 0.05628636 2.564949
4 2 0.03678637 2.564949
3 3 0.07414634 2.564949
1 4 0.13648433 2.564949
0 0 0.11532605 2.564949
1 1 0.05628636 2.564949
3 2 0.03678637 2.564949
4 3 0.07414634 2.564949
0 4 0.13648433 2.564949
1 0 0.11532605 2.564949
3 1 0.05628636 2.564949
10 2 0.03678637 2.564949
2 3 0.07414634 2.564949
1 4 0.13648433 2.564949
0 0 0.11532605 2.564949
0 1 0.05628636 2.564949
3 2 0.03678637 2.564949
3 3 0.07414634 2.564949
1 4 0.13648433 2.564949
0 0 0.11532605 2.564949
24 1 0.05628636 2.564949
43 2 0.03678637 2.564949
11 3 0.07414634 2.564949
3 4 0.13648433 2.564949
0 0 0.11532605 2.564949
3 1 0.05628636 2.564949
19 2 0.03678637 2.564949
14 3 0.07414634 2.564949
2 4 0.13648433 2.564949
0 0 0.09016875 NA
25 1 0.14227471 2.833213
2 2 0.11532605 2.833213
0 3 0.05628636 2.833213
0 4 0.03678637 2.833213
0 0 0.09016875 2.833213
14 1 0.14227471 2.833213
0 2 0.11532605 2.833213
0 3 0.05628636 2.833213
0 4 0.03678637 2.833213
0 0 0.09016875 2.833213
12 1 0.14227471 2.833213
4 2 0.11532605 2.833213
0 3 0.05628636 2.833213
0 4 0.03678637 2.833213
1 0 0.09016875 2.833213
42 1 0.14227471 2.833213
20 2 0.11532605 2.833213
1 3 0.05628636 2.833213
2 4 0.03678637 2.833213
0 0 0.09016875 2.833213
48 1 0.14227471 2.833213
40 2 0.11532605 2.833213
1 3 0.05628636 2.833213
0 4 0.03678637 2.833213
10 0 0.09016875 2.833213
23 2 0.11532605 2.833213
0 3 0.05628636 2.833213
2 4 0.03678637 2.833213
2 0 0.09016875 2.833213
89 1 0.14227471 2.833213
5 2 0.11532605 2.833213
1 3 0.05628636 2.833213
6 4 0.03678637 2.833213
0 0 0.09016875 2.833213
27 1 0.14227471 2.833213
9 2 0.11532605 2.833213
3 3 0.05628636 2.833213
2 4 0.03678637 2.833213
1 0 0.09016875 2.833213
6 1 0.14227471 2.833213
0 2 0.11532605 2.833213
1 3 0.05628636 2.833213
0 4 0.03678637 2.833213
0 0 0.09016875 2.833213
65 1 0.14227471 2.833213
35 2 0.11532605 2.833213
1 3 0.05628636 2.833213
2 4 0.03678637 2.833213
0 0 0.09016875 2.833213
29 1 0.14227471 2.833213
26 2 0.11532605 2.833213
3 3 0.05628636 2.833213
1 4 0.03678637 2.833213
4 0 0.09016875 2.833213
105 1 0.14227471 2.833213
5 2 0.11532605 2.833213
0 3 0.05628636 2.833213
1 4 0.03678637 2.833213
4 0 0.09016875 2.833213
107 1 0.14227471 2.833213
5 2 0.11532605 2.833213
0 3 0.05628636 2.833213
0 4 0.03678637 2.833213
0 0 0.09016875 2.833213
17 1 0.14227471 2.833213
1 2 0.11532605 2.833213
0 3 0.05628636 2.833213
0 4 0.03678637 2.833213
3 0 0.09016875 2.833213
106 1 0.14227471 2.833213
1 2 0.11532605 2.833213
1 3 0.05628636 2.833213
0 4 0.03678637 2.833213
0 0 0.09016875 2.833213
21 1 0.14227471 2.833213
14 2 0.11532605 2.833213
5 3 0.05628636 2.833213
1 4 0.03678637 2.833213
0 0 0.09016875 2.833213
35 1 0.14227471 2.833213
12 2 0.11532605 2.833213
8 3 0.05628636 2.833213
2 4 0.03678637 2.833213
4 0 0.13510174 1.791759
1 1 0.10188844 1.791759
4 2 0.09016875 1.791759
0 3 0.14227471 1.791759
0 4 0.11532605 1.791759
3 0 0.13510174 1.791759
16 1 0.10188844 1.791759
11 2 0.09016875 1.791759
0 3 0.14227471 1.791759
0 4 0.11532605 1.791759
4 0 0.13510174 1.791759
20 1 0.10188844 1.791759
7 2 0.09016875 1.791759
0 3 0.14227471 1.791759
0 4 0.11532605 1.791759
0 0 0.13510174 1.791759
3 1 0.10188844 1.791759
1 2 0.09016875 1.791759
1 3 0.14227471 1.791759
1 4 0.11532605 1.791759
0 0 0.13510174 1.791759
2 1 0.10188844 1.791759
8 2 0.09016875 1.791759
2 3 0.14227471 1.791759
1 4 0.11532605 1.791759
0 0 0.13510174 1.791759
1 1 0.10188844 1.791759
40 2 0.09016875 1.791759
8 3 0.14227471 1.791759
0 4 0.11532605 1.791759
0 0 0.33638851 2.70805
0 1 0.20354567 2.70805
18 2 0.13510174 2.70805
2 3 0.10188844 2.70805
0 4 0.09016875 2.70805
0 0 0.33638851 2.70805
0 1 0.20354567 2.70805
1 2 0.13510174 2.70805
0 3 0.10188844 2.70805
0 4 0.09016875 2.70805
0 0 0.33638851 2.70805
1 1 0.20354567 2.70805
1 2 0.13510174 2.70805
0 3 0.10188844 2.70805
0 4 0.09016875 2.70805
0 0 0.33638851 2.70805
13 1 0.20354567 2.70805
23 2 0.13510174 2.70805
1 3 0.10188844 2.70805
13 4 0.09016875 2.70805
0 0 0.33638851 2.70805
1 1 0.20354567 2.70805
8 2 0.13510174 2.70805
3 3 0.10188844 2.70805
4 4 0.09016875 2.70805
0 0 0.33638851 2.70805
2 1 0.20354567 2.70805
9 2 0.13510174 2.70805
2 3 0.10188844 2.70805
0 4 0.09016875 2.70805
26 0 0.33638851 2.70805
2 1 0.20354567 2.70805
2 2 0.13510174 2.70805
0 3 0.10188844 2.70805
0 4 0.09016875 2.70805
57 0 0.33638851 2.70805
4 1 0.20354567 2.70805
6 2 0.13510174 2.70805
0 3 0.10188844 2.70805
1 4 0.09016875 2.70805
26 0 0.33638851 2.70805
3 1 0.20354567 2.70805
10 2 0.13510174 2.70805
2 3 0.10188844 2.70805
3 4 0.09016875 2.70805
9 0 0.33638851 2.70805
1 1 0.20354567 2.70805
3 2 0.13510174 2.70805
2 3 0.10188844 2.70805
0 4 0.09016875 2.70805
0 0 0.33638851 2.70805
0 1 0.20354567 2.70805
6 2 0.13510174 2.70805
1 3 0.10188844 2.70805
0 4 0.09016875 2.70805
0 0 0.33638851 2.70805
4 1 0.20354567 2.70805
16 2 0.13510174 2.70805
12 3 0.10188844 2.70805
3 4 0.09016875 2.70805
0 0 0.33638851 2.70805
7 1 0.20354567 2.70805
19 2 0.13510174 2.70805
8 3 0.10188844 2.70805
2 4 0.09016875 2.70805
14 0 0.33638851 2.70805
8 1 0.20354567 2.70805
33 2 0.13510174 2.70805
25 3 0.10188844 2.70805
4 4 0.09016875 2.70805
5 1 0.20354567 2.70805
15 2 0.13510174 2.70805
20 3 0.10188844 2.70805
10 4 0.09016875 2.70805
8 0 0.17597738 2.833213
28 1 0.25832942 2.833213
6 2 0.33638851 2.833213
0 3 0.20354567 2.833213
0 4 0.13510174 2.833213
4 0 0.17597738 2.833213
15 1 0.25832942 2.833213
27 2 0.33638851 2.833213
0 3 0.20354567 2.833213
0 4 0.13510174 2.833213
5 0 0.17597738 2.833213
13 1 0.25832942 2.833213
6 2 0.33638851 2.833213
0 3 0.20354567 2.833213
1 4 0.13510174 2.833213
0 0 0.17597738 2.833213
1 1 0.25832942 2.833213
0 2 0.33638851 2.833213
0 3 0.20354567 2.833213
2 4 0.13510174 2.833213
0 0 0.17597738 2.833213
6 1 0.25832942 2.833213
22 2 0.33638851 2.833213
0 3 0.20354567 2.833213
0 4 0.13510174 2.833213
0 0 0.17597738 2.833213
0 1 0.25832942 2.833213
11 2 0.33638851 2.833213
1 3 0.20354567 2.833213
2 4 0.13510174 2.833213
2 0 0.17597738 2.833213
0 1 0.25832942 2.833213
7 2 0.33638851 2.833213
3 3 0.20354567 2.833213
0 4 0.13510174 2.833213
1 0 0.17597738 2.833213
14 1 0.25832942 2.833213
23 2 0.33638851 2.833213
0 3 0.20354567 2.833213
1 4 0.13510174 2.833213
0 0 0.17597738 2.833213
0 1 0.25832942 2.833213
5 2 0.33638851 2.833213
2 3 0.20354567 2.833213
0 4 0.13510174 2.833213
0 0 0.17597738 2.833213
3 1 0.25832942 2.833213
6 2 0.33638851 2.833213
0 3 0.20354567 2.833213
1 4 0.13510174 2.833213
0 0 0.17597738 2.833213
0 1 0.25832942 2.833213
2 2 0.33638851 2.833213
0 3 0.20354567 2.833213
4 4 0.13510174 2.833213
2 0 0.17597738 2.833213
39 1 0.25832942 2.833213
18 2 0.33638851 2.833213
7 3 0.20354567 2.833213
0 4 0.13510174 2.833213
3 0 0.17597738 2.833213
25 1 0.25832942 2.833213
9 2 0.33638851 2.833213
3 3 0.20354567 2.833213
0 4 0.13510174 2.833213
4 0 0.17597738 2.833213
7 1 0.25832942 2.833213
1 2 0.33638851 2.833213
1 3 0.20354567 2.833213
0 4 0.13510174 2.833213
0 0 0.17597738 2.833213
1 1 0.25832942 2.833213
6 2 0.33638851 2.833213
1 3 0.20354567 2.833213
0 4 0.13510174 2.833213
2 0 0.17597738 2.833213
15 1 0.25832942 2.833213
49 2 0.33638851 2.833213
19 3 0.20354567 2.833213
2 4 0.13510174 2.833213
0 0 0.17597738 2.833213
0 1 0.25832942 2.833213
1 2 0.33638851 2.833213
0 3 0.20354567 2.833213
0 4 0.13510174 2.833213
3 0 0.17485677 2.302585
50 1 0.17597738 2.302585
25 2 0.25832942 2.302585
0 3 0.33638851 2.302585
0 4 0.20354567 2.302585
1 0 0.17485677 2.302585
7 1 0.17597738 2.302585
8 2 0.25832942 2.302585
0 3 0.33638851 2.302585
0 4 0.20354567 2.302585
3 0 0.17485677 2.302585
16 1 0.17597738 2.302585
63 2 0.25832942 2.302585
3 3 0.33638851 2.302585
0 4 0.20354567 2.302585
1 0 0.17485677 2.302585
34 1 0.17597738 2.302585
12 3 0.33638851 2.302585
4 4 0.20354567 2.302585
0 0 0.17485677 2.302585
29 1 0.17597738 2.302585
16 2 0.25832942 2.302585
0 3 0.33638851 2.302585
0 4 0.20354567 2.302585
0 0 0.17485677 2.302585
30 1 0.17597738 2.302585
13 2 0.25832942 2.302585
0 3 0.33638851 2.302585
2 4 0.20354567 2.302585
0 0 0.17485677 2.302585
15 1 0.17597738 2.302585
10 2 0.25832942 2.302585
0 3 0.33638851 2.302585
1 4 0.20354567 2.302585
4 0 0.17485677 2.302585
50 1 0.17597738 2.302585
32 2 0.25832942 2.302585
6 3 0.33638851 2.302585
8 4 0.20354567 2.302585
0 0 0.17485677 2.302585
32 1 0.17597738 2.302585
29 2 0.25832942 2.302585
4 3 0.33638851 2.302585
8 4 0.20354567 2.302585
0 0 0.17485677 2.302585
2 1 0.17597738 2.302585
2 2 0.25832942 2.302585
2 3 0.33638851 2.302585
3 4 0.20354567 2.302585
为了可视化概率回归模型的拟合优度,"standard" 残差(例如,泊松或偏差)通常没有那么丰富的信息,因为它们主要捕获均值而非整个分布的建模。有时使用的一种替代方法是(随机化的)分位数残差。在没有随机化的情况下,它们被定义为 qnorm(pdist(y))
,其中 pdist()
是拟合分布函数(这里是 ZIP 模型),y
是观察值,qnorm()
是分位数函数标准正态分布。如果模型适合,残差的分布应该是标准正态的,并且可以在 Q-Q 图中检查。在离散分布的情况下(如此处),需要随机化来打破数据的离散性质。有关详细信息,请参见 Dunn & Smyth(1996 年,Journal of Computational and Graphical Statistics、5、236-244)。在 R 中,您可以使用 R-Forge 中的 countreg
包(希望很快也能在 CRAN 上使用)。
检查数据边缘分布的另一种方法是所谓的根图。它直观地比较了计数的观察频率和拟合频率,0、1,...它通常比随机分位数残差的 Q-Q 图更能显示过零 and/or 过度分散的问题。有关更多信息,请参阅我们的论文 Kleiber & Zeileis (2016, The American Statistician, 70(3), 296–303, doi:10.1080/00031305.2016.1173590)详情。
将这些快速应用于您的回归模型表明零膨胀 泊松 并未解释响应中的过度分散。 (计数达到或超过 100 时,基于泊松的分布几乎从不适合。)此外,零 inflation 模型不太适合,因为 assnage
= 1 和 = 2 零很少,不需要零 inflation。这导致零 inflation 部分的相应系数趋向 -Inf
并具有非常大的标准误差(如二元回归中的准分离)。因此,由两部分组成的障碍模型更适合并且可能更容易解释。最后,由于两个 assnage
组不同,我将编码 assnage
作为一个因素(我不清楚你是否已经这样做了)。
因此,为了分析您的数据,我使用 yc
作为您 post 中提供的,并确保:
yc$assnage <- factor(yc$assnage)
为了初步探索 assnage
的影响,我绘制了 count
是否为正数(左:零障碍)和正数 count
对数-规模(右:计数)。
plot(factor(count > 0, levels = c(FALSE, TRUE), labels = c("=0", ">0")) ~ assnage,
data = yc, ylab = "count", main = "Zero hurdle")
plot(count ~ assnage, data = yc, subset = count > 0,
log = "y", main = "Count (positive)")
然后,我使用 R-Forge 的 countreg
包安装 ZIP、ZINB 和 hurdle NB 模型。这也包含 zeroinfl()
和 hurdle()
函数的更新版本。
install.packages("countreg", repos = "http://R-Forge.R-project.org")
library("countreg")
zip <- zeroinfl(count ~ assnage * spawncob, offset = logoffset,
data = yc, dist = "poisson")
zinb <- zeroinfl(count ~ assnage * spawncob, offset = logoffset,
data = yc, dist = "negbin")
hnb <- hurdle(count ~ assnage * spawncob, offset = logoffset, data = yc,
dist = "negbin")
ZIP明显不妥,栏NB略好于ZINB
BIC(zip, zinb, hnb)
## df BIC
## zip 20 7700.085
## zinb 21 3574.720
## hnb 21 3556.693
如果您检查 summary(zinb)
,您还会看到零 inflation 部分中的某些系数约为 10(对于虚拟变量),标准误差大一到两个数量级。这实质上意味着相应组中的零 inflation 概率变为零,因为负二项分布已经具有足够多的零响应概率权重(assnage
第 1 组和第 2 组)。
为了可视化 ZIP 模型不适合而 HNB 适当地捕获响应,我们现在可以使用根图。
rootogram(zip, main = "ZIP", ylim = c(-5, 15), max = 50)
rootogram(hnb, main = "HNB", ylim = c(-5, 15), max = 50)
ZIP 的波浪状图案清楚地显示了模型未正确捕获的数据过度分散。相比之下,障碍相当合适。
作为最后的检查,我们还可以查看障碍模型中分位数残差的 Q-Q 图。这些看起来相当正常,与模型没有任何可疑的偏差。
qqrplot(hnb, main = "HNB")
由于残差是随机的,您可以重新运行 代码几次以了解变化情况。 qqrplot()
也有一些论点,让您可以在单个图中探索这种变化。
我是 运行 CPUE 数据的零膨胀模型。该数据具有零 inflation 的证据,我已通过 Vuong 测试(在下面的代码中)确认了这一点。根据 AIC,完整模型 (zint) 优于空模型。我现在想要:
- 检查完整模型的残差以确定模型拟合(由于缺乏来自同事、互联网和 R 书籍的信息而遇到问题)
- 如果模型拟合似乎没问题,可视化模型的输出(使用偏移变量时如何制定实参数值的方程)
我已经向该部门的一些统计人员寻求帮助(他们以前从未这样做过,并将我发送到相同的 google 搜索站点),在统计部门之外(每个人都太忙了)和 Whosebug 提要。
我希望有代码或书籍指南(在线免费提供),其中包含处理可视化 ZIP 和使用偏移变量时模型拟合的代码。
yc=read.csv("CPUE_ycs_trawl_withcobb_BLS.csv",header=TRUE)
yc=yc[which(yc$countinyear<150),]
yc$fyear=as.factor(yc$year_cap)
yc$flocation=as.factor(yc$location)
hist(yc$countinyear,20)
yc$logoffset=log(yc$numtrawlyr)
###Run Zero-inflated poisson with offset for CPUE####
null <- formula(yc$countinyear ~ 1| 1)
znull <- zeroinfl(null, offset=logoffset,dist = "poisson",link = "logit",
data = yc)
int <- formula(yc$countinyear ~ assnage * spawncob| assnage * spawncob)
zint <- zeroinfl(int, offset=logoffset,dist = "poisson",link = "logit", data
= yc)
AIC(znull,zint)
g1=glm(countinyear ~ assnage * spawncob,
offset=logoffset,data=yc,family=poisson)
summary(g1)
####Vuong test to see if ZIP is even needed##
vuong(g1,zint)
##########DATASET###########
countinyear 是第 1 列
##########DATASET###########
count assnage spawncob logoffset
56 0 0.32110173 2.833213
44 1 0.33712 2.833213
60 2 0.34053264 2.833213
0 4 0.19381496 2.833213
1 3 0.30819333 2.833213
33 0 0.32110173 2.833213
40 1 0.33712 2.833213
25 2 0.34053264 2.833213
0 3 0.30819333 2.833213
2 4 0.19381496 2.833213
6 0 0.32110173 2.833213
13 1 0.33712 2.833213
7 2 0.34053264 2.833213
0 3 0.30819333 2.833213
0 4 0.19381496 NA
5 0 0.32110173 2.833213
31 1 0.33712 2.833213
73 2 0.34053264 2.833213
0 3 0.30819333 2.833213
1 4 0.19381496 2.833213
0 0 0.32110173 2.833213
7 1 0.33712 2.833213
75 2 0.34053264 2.833213
3 3 0.30819333 2.833213
0 4 0.19381496 2.833213
19 0 0.32110173 2.833213
13 1 0.33712 2.833213
18 2 0.34053264 2.833213
0 3 0.30819333 2.833213
2 4 0.19381496 2.833213
11 0 0.32110173 2.833213
14 1 0.33712 2.833213
32 2 0.34053264 2.833213
1 3 0.30819333 2.833213
1 4 0.19381496 2.833213
12 0 0.32110173 2.833213
3 1 0.33712 2.833213
9 2 0.34053264 2.833213
2 3 0.30819333 2.833213
0 4 0.19381496 2.833213
5 0 0.32110173 2.833213
15 1 0.33712 2.833213
22 2 0.34053264 2.833213
5 3 0.30819333 2.833213
1 4 0.19381496 2.833213
1 0 0.32110173 2.833213
16 1 0.33712 2.833213
33 2 0.34053264 2.833213
4 3 0.30819333 2.833213
2 4 0.19381496 2.833213
6 0 0.32110173 2.833213
17 1 0.33712 2.833213
26 2 0.34053264 2.833213
1 3 0.30819333 2.833213
0 4 0.19381496 2.833213
16 0 0.32110173 2.833213
16 1 0.33712 2.833213
11 2 0.34053264 2.833213
1 3 0.30819333 2.833213
1 4 0.19381496 2.833213
2 0 0.32110173 2.833213
8 1 0.33712 2.833213
18 2 0.34053264 2.833213
0 3 0.30819333 2.833213
0 4 0.19381496 2.833213
2 0 0.32110173 2.833213
27 1 0.33712 2.833213
49 2 0.34053264 2.833213
1 3 0.30819333 2.833213
0 4 0.19381496 2.833213
1 0 0.32110173 2.833213
6 1 0.33712 2.833213
36 2 0.34053264 2.833213
17 3 0.30819333 2.833213
0 4 0.19381496 2.833213
10 0 0.32110173 2.833213
21 1 0.33712 2.833213
78 2 0.34053264 2.833213
32 3 0.30819333 2.833213
0 4 0.19381496 2.833213
0 0 0.32110173 2.833213
8 1 0.33712 2.833213
14 2 0.34053264 2.833213
7 3 0.30819333 2.833213
0 4 0.19381496 2.833213
0 1 0.13648433 2.833213
6 1 0.23952033 2.833213
12 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
30 0 0.13648433 2.833213
30 1 0.23952033 2.833213
25 2 0.32110173 2.833213
30 3 0.33712 2.833213
30 4 0.34053264 2.833213
68 0 0.13648433 2.833213
68 1 0.23952033 2.833213
55 2 0.32110173 2.833213
68 3 0.33712 2.833213
68 4 0.34053264 2.833213
0 0 0.13648433 2.833213
12 1 0.23952033 2.833213
26 2 0.32110173 2.833213
2 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.13648433 2.833213
17 1 0.23952033 2.833213
36 2 0.32110173 2.833213
1 3 0.33712 2.833213
4 4 0.34053264 2.833213
1 0 0.13648433 2.833213
1 1 0.23952033 2.833213
4 2 0.32110173 2.833213
4 3 0.33712 2.833213
0 4 0.34053264 2.833213
3 0 0.13648433 2.833213
3 1 0.23952033 2.833213
3 2 0.32110173 2.833213
3 3 0.33712 2.833213
3 4 0.34053264 2.833213
0 0 0.13648433 2.833213
29 1 0.23952033 2.833213
33 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
10 1 0.23952033 2.833213
7 2 0.32110173 2.833213
1 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
6 1 0.23952033 2.833213
18 2 0.32110173 2.833213
1 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
18 1 0.23952033 2.833213
37 2 0.32110173 2.833213
1 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.13648433 2.833213
13 1 0.23952033 2.833213
26 2 0.32110173 2.833213
8 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
0 1 0.23952033 2.833213
1 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
1 1 0.23952033 2.833213
5 2 0.32110173 2.833213
0 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
29 1 0.23952033 2.833213
15 2 0.32110173 2.833213
2 3 0.33712 2.833213
0 4 0.34053264 2.833213
0 0 0.13648433 2.833213
19 1 0.23952033 2.833213
25 2 0.32110173 2.833213
3 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.13648433 2.833213
24 1 0.23952033 2.833213
40 2 0.32110173 2.833213
6 3 0.33712 2.833213
1 4 0.34053264 2.833213
0 0 0.03678637 2.772589
28 1 0.07414634 2.772589
28 2 0.13648433 2.772589
3 3 0.23952033 2.772589
2 4 0.32110173 2.772589
0 0 0.03678637 2.772589
3 1 0.07414634 2.772589
2 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
4 0 0.03678637 2.772589
14 1 0.07414634 2.772589
6 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
6 1 0.07414634 2.772589
3 2 0.13648433 2.772589
2 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
8 1 0.07414634 2.772589
2 2 0.13648433 2.772589
4 3 0.23952033 2.772589
1 4 0.32110173 2.772589
1 0 0.03678637 2.772589
12 1 0.07414634 2.772589
23 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
24 1 0.07414634 2.772589
56 2 0.13648433 2.772589
7 3 0.23952033 2.772589
4 4 0.32110173 2.772589
0 0 0.03678637 2.772589
22 1 0.07414634 2.772589
45 2 0.13648433 2.772589
3 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
2 1 0.07414634 2.772589
18 2 0.13648433 2.772589
1 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
5 1 0.07414634 2.772589
18 2 0.13648433 2.772589
5 3 0.23952033 2.772589
1 4 0.32110173 2.772589
0 0 0.03678637 2.772589
9 1 0.07414634 2.772589
25 2 0.13648433 2.772589
6 3 0.23952033 2.772589
1 4 0.32110173 2.772589
0 0 0.03678637 2.772589
1 1 0.07414634 2.772589
3 2 0.13648433 2.772589
1 3 0.23952033 2.772589
1 4 0.32110173 2.772589
0 0 0.03678637 2.772589
3 1 0.07414634 2.772589
16 2 0.13648433 2.772589
0 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
7 1 0.07414634 2.772589
21 2 0.13648433 2.772589
8 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
5 1 0.07414634 2.772589
22 2 0.13648433 2.772589
6 3 0.23952033 2.772589
0 4 0.32110173 2.772589
0 0 0.03678637 2.772589
11 1 0.07414634 2.772589
22 2 0.13648433 2.772589
6 3 0.23952033 2.772589
0 4 0.32110173 2.772589
1 0 0.11532605 2.564949
7 1 0.05628636 2.564949
11 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
4 1 0.05628636 2.564949
4 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
0 1 0.05628636 2.564949
5 2 0.03678637 2.564949
0 3 0.07414634 2.564949
1 4 0.13648433 2.564949
0 0 0.11532605 2.564949
3 1 0.05628636 2.564949
4 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
3 1 0.05628636 2.564949
0 2 0.03678637 2.564949
1 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
1 1 0.05628636 2.564949
0 2 0.03678637 2.564949
0 3 0.07414634 2.564949
0 4 0.13648433 2.564949
0 0 0.11532605 2.564949
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40 2 0.09016875 1.791759
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63 2 0.25832942 2.302585
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为了可视化概率回归模型的拟合优度,"standard" 残差(例如,泊松或偏差)通常没有那么丰富的信息,因为它们主要捕获均值而非整个分布的建模。有时使用的一种替代方法是(随机化的)分位数残差。在没有随机化的情况下,它们被定义为 qnorm(pdist(y))
,其中 pdist()
是拟合分布函数(这里是 ZIP 模型),y
是观察值,qnorm()
是分位数函数标准正态分布。如果模型适合,残差的分布应该是标准正态的,并且可以在 Q-Q 图中检查。在离散分布的情况下(如此处),需要随机化来打破数据的离散性质。有关详细信息,请参见 Dunn & Smyth(1996 年,Journal of Computational and Graphical Statistics、5、236-244)。在 R 中,您可以使用 R-Forge 中的 countreg
包(希望很快也能在 CRAN 上使用)。
检查数据边缘分布的另一种方法是所谓的根图。它直观地比较了计数的观察频率和拟合频率,0、1,...它通常比随机分位数残差的 Q-Q 图更能显示过零 and/or 过度分散的问题。有关更多信息,请参阅我们的论文 Kleiber & Zeileis (2016, The American Statistician, 70(3), 296–303, doi:10.1080/00031305.2016.1173590)详情。
将这些快速应用于您的回归模型表明零膨胀 泊松 并未解释响应中的过度分散。 (计数达到或超过 100 时,基于泊松的分布几乎从不适合。)此外,零 inflation 模型不太适合,因为 assnage
= 1 和 = 2 零很少,不需要零 inflation。这导致零 inflation 部分的相应系数趋向 -Inf
并具有非常大的标准误差(如二元回归中的准分离)。因此,由两部分组成的障碍模型更适合并且可能更容易解释。最后,由于两个 assnage
组不同,我将编码 assnage
作为一个因素(我不清楚你是否已经这样做了)。
因此,为了分析您的数据,我使用 yc
作为您 post 中提供的,并确保:
yc$assnage <- factor(yc$assnage)
为了初步探索 assnage
的影响,我绘制了 count
是否为正数(左:零障碍)和正数 count
对数-规模(右:计数)。
plot(factor(count > 0, levels = c(FALSE, TRUE), labels = c("=0", ">0")) ~ assnage,
data = yc, ylab = "count", main = "Zero hurdle")
plot(count ~ assnage, data = yc, subset = count > 0,
log = "y", main = "Count (positive)")
然后,我使用 R-Forge 的 countreg
包安装 ZIP、ZINB 和 hurdle NB 模型。这也包含 zeroinfl()
和 hurdle()
函数的更新版本。
install.packages("countreg", repos = "http://R-Forge.R-project.org")
library("countreg")
zip <- zeroinfl(count ~ assnage * spawncob, offset = logoffset,
data = yc, dist = "poisson")
zinb <- zeroinfl(count ~ assnage * spawncob, offset = logoffset,
data = yc, dist = "negbin")
hnb <- hurdle(count ~ assnage * spawncob, offset = logoffset, data = yc,
dist = "negbin")
ZIP明显不妥,栏NB略好于ZINB
BIC(zip, zinb, hnb)
## df BIC
## zip 20 7700.085
## zinb 21 3574.720
## hnb 21 3556.693
如果您检查 summary(zinb)
,您还会看到零 inflation 部分中的某些系数约为 10(对于虚拟变量),标准误差大一到两个数量级。这实质上意味着相应组中的零 inflation 概率变为零,因为负二项分布已经具有足够多的零响应概率权重(assnage
第 1 组和第 2 组)。
为了可视化 ZIP 模型不适合而 HNB 适当地捕获响应,我们现在可以使用根图。
rootogram(zip, main = "ZIP", ylim = c(-5, 15), max = 50)
rootogram(hnb, main = "HNB", ylim = c(-5, 15), max = 50)
ZIP 的波浪状图案清楚地显示了模型未正确捕获的数据过度分散。相比之下,障碍相当合适。
作为最后的检查,我们还可以查看障碍模型中分位数残差的 Q-Q 图。这些看起来相当正常,与模型没有任何可疑的偏差。
qqrplot(hnb, main = "HNB")
由于残差是随机的,您可以重新运行 代码几次以了解变化情况。 qqrplot()
也有一些论点,让您可以在单个图中探索这种变化。