R 二元logistic随机斜率模型的可视化
我使用以下数据运行了二元逻辑随机斜率模型：R 二元logistic随机斜率模型的可视化,r,ggplot2,mlogit,R,Ggplot2,Mlogit,我使用以下数据运行了二元逻辑随机斜率模型： serve country conscription sex education income religion immigrant proud trusting outgoing age 1 Yes ALG 1 male 3 5 Very important 0 1 2 2 -15.7403
serve country conscription sex education income religion immigrant proud trusting outgoing age
1 Yes ALG 1 male 3 5 Very important 0 1 2 2 -15.7403361
2 Yes ALG 1 female 3 6 Rather important 0 2 4 2 -12.7403361
3 Yes ALG 1 female 3 6 Very important 0 1 3 3 -10.7403361
4 Yes ALG 1 female 3 5 Very important 0 1 3 4 -8.7403361
5 Yes ALG 1 female 2 7 Very important 0 1 4 4 -1.7403361
6 Yes ALG 1 male 4 5 Very important 0 1 3 4 -0.7403361
7 Yes ALG 1 male 3 7 Very important 0 1 2 2 4.2596639
8 Yes ALG 1 female 2 2 Rather important 0 1 3 4 7.2596639
9 Yes ALG 1 male 1 5 Rather important 0 1 3 2 22.2596639
11 Yes ALG 1 female 4 5 Very important 0 1 3 1 -13.7403361
Generalized linear mixed model fit by maximum likelihood (Adaptive Gauss-Hermite Quadrature, nAGQ = 0) ['glmerMod']
Family: binomial ( logit )
Formula: serve ~ age + sex + income + religion + proud + trusting + outgoing + conscription + (1 + proud | country)
Data: WVS.2
Control: glmerControl(optimizer = "bobyqa")
AIC BIC logLik deviance df.resid
26133.6 26359.2 -13038.8 26077.6 23283
Scaled residuals:
Min 1Q Median 3Q Max
-4.5789 -0.9022 0.4386 0.6850 3.5584
Random effects:
Groups Name Variance Std.Dev. Corr
country (Intercept) 0.70584 0.8401
proud2 0.05847 0.2418 -0.31
proud3 0.18141 0.4259 -0.37 0.79
proud4 0.75998 0.8718 0.14 0.58 0.81
Number of obs: 23311, groups: country, 20
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.139479 0.248397 0.562 0.57444
age -0.006126 0.001319 -4.645 3.40e-06 ***
sexmale 0.652698 0.030950 21.089 < 2e-16 ***
income -0.006549 0.007549 -0.867 0.38569
religionRather important 0.146834 0.053087 2.766 0.00568 **
religionVery important 0.299748 0.051477 5.823 5.78e-09 ***
proud2 -0.178368 0.066784 -2.671 0.00757 **
proud3 -0.340180 0.117835 -2.887 0.00389 **
proud4 -0.346386 0.245852 -1.409 0.15886
trusting2 0.105620 0.057906 1.824 0.06815 .
trusting3 0.173238 0.058896 2.941 0.00327 **
trusting4 0.338042 0.057763 5.852 4.85e-09 ***
trusting5 0.281655 0.063626 4.427 9.57e-06 ***
outgoing2 -0.170605 0.065585 -2.601 0.00929 **
outgoing3 -0.110182 0.065934 -1.671 0.09470 .
outgoing4 0.117553 0.063268 1.858 0.06317 .
outgoing5 0.218266 0.067077 3.254 0.00114 **
conscription1 0.023910 0.338071 0.071 0.94362
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
广义线性混合模型最大似然拟合（自适应高斯-厄米特求积，nAGQ=0）['glmerMod']
家庭：二项式（logit）
公式：服役~年龄+性别+收入+宗教+骄傲+信任+外出+征兵+（1+骄傲|国家）
数据：WVS.2
控件：glmerControl（optimizer=“bobyqa”）
AIC BIC logLik偏差df.resid
26133.6 26359.2 -13038.8 26077.6 23283
标度残差：
最小1季度中值3季度最大值
-4.5789 -0.9022 0.4386 0.6850 3.5584
随机效应：
组名为Variance Std.Dev。科尔
国家（截距）0.70584 0.8401
proud2 0.05847 0.2418-0.31
proud3 0.18141 0.4259-0.37 0.79
proud4 0.75998 0.8718 0.14 0.58 0.81
OB数量：23311，组：国家，20
固定效果：
估计标准误差z值Pr（>z）
（截距）0.139479 0.248397 0.562 0.57444
年龄-0.006126 0.001319-4.645 3.40e-06***
性别男性0.652698 0.030950 21.089<2e-16***
收入-0.006549 0.007549-0.867 0.38569
宗教领袖重要人物0.146834 0.053087 2.766 0.00568**
宗教重要0.299748 0.051477 5.823 5.78e-09***
proud2-0.178368 0.066784-2.671 0.00757**
proud3-0.340180 0.117835-2.887 0.00389**
proud4-0.346386 0.245852-1.409 0.15886
信任2 0.105620 0.057906 1.824 0.06815。
信任3 0.173238 0.058896 2.941 0.00327**
信任4 0.338042 0.057763 5.852 4.85e-09***
信任5 0.281655 0.063626 4.427 9.57e-06***
支出2-0.170605 0.065585-2.601 0.00929**
支出3-0.110182 0.065934-1.671 0.09470。
支出4 0.117553 0.063268 1.858 0.06317。
支出5 0.218266 0.067077 3.254 0.00114**
征兵1 0.023910 0.338071 0.071 0.94362
---
签名。代码：0'***'0.001'***'0.01'*'0.05'.'0.1''1
df1 <- dplyr::select(WVS.2, country, proud, serve)
df1$age <- 0
df1$sex <- 0
df1$income <- 0
df1$religion <- 0
df1$trusting <- 0
df1$outgoing <- 0
df1$conscription <- 0
df1