使用 R (MICE) 轮询多级逻辑模型的乘法估算数据集的问题 - 缺失系数
Problems with pooling Multiply Imuputed datasets, of multilevel logistic models, using R (MICE) - missing coefficent
我在使用 R 中的 MICE 包时遇到问题，特别是在汇集估算数据集方面。
我是 运行 多级二项式逻辑回归，Level1 - 主题（参与者对 10 个关于不同主题的问题的回答，例如黑暗，白天）嵌套在 Level2 - 个体中。
模型使用R2MLwiN创建，公式为
> fit1 <-runMLwiN( c(probit(T_Darkness, cons), probit(T_Day, cons), probit(T_Light, cons), probit(T_Night, cons), probit(T_Rain, cons), probit(T_Rainbows, cons), probit(T_Snow, cons), probit(T_Storms, cons), probit(T_Waterfalls, cons), probit(T_Waves, cons)) ~ 1, D=c("Mixed", "Binomial", "Binomial","Binomial","Binomial", "Binomial", "Binomial", "Binomial", "Binomial", "Binomial" ,"Binomial"), estoptions = list(EstM = 0), data=data)
很遗憾，所有 Level1（主题）回复中都缺少数据。
我一直在使用 mice 包 ([CRAN][1]) 来乘法估算缺失值。
我可以使用公式 > fitMI <- (with(MI.Data, runMLwiN( c(probit(T_Darkness, cons), probit(T_Day, cons), probit(T_Light, cons), probit(T_Night, cons), probit(T_Rain, cons), probit(T_Rainbows, cons), probit(T_Snow, cons), probit(T_Storms, cons), probit(T_Waterfalls, cons), probit(T_Waves, cons)) ~ 1, D=c("Mixed", "Binomial", "Binomial","Binomial","Binomial", "Binomial", "Binomial", "Binomial", "Binomial", "Binomial" ,"Binomial"), estoptions = list(EstM = 0), data=data)))
将模型拟合到估算的数据集
但是，当我将分析与调用代码 > pool(fitMI) 合并时，它失败了，错误为：
Error in pool(with(tempData, runMLwiN(c(probit(T_Darkness, cons), probit(T_Day, :
Object has no coef() method.
我不确定为什么说没有系数，因为对各个 MI 数据集的分析提供了固定部分（系数）和随机部分（协方差）
对于出现问题的任何帮助将不胜感激。
我应该警告你，这是我第一次尝试使用 R 和多级建模。
我也知道有一个 MlwiN 包 ([REALCOM][2]) 可以做到这一点，但我没有使用 MLwiN 软件的背景。
谢谢
强尼
更新 - R 可重现示例
使用的库
library(R2MLwiN)
library(mice)
数据子集
`
T_Darkness <- c(0, 1, 0, 0, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, NA, 1, 0, NA,NA, 1, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, NA, 1, 0)
T_Day <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, NA, NA, 0)
T_Light <- c(0, 0, NA, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0)
T_Night <- c(0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,NA, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0)
T_Rain <- c(1, 0, 0, 1, 1, 0, 0, NA, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, NA, 1, NA, 0, 0, 0, 0, 1, NA, 1, 0, 0, 0, 0, 1, NA, 0, 0)
T_Rainbows <- c(1, 1, 1, 1, 0, 1, 0, 1, 0, 1, NA, 1, 1, 0, 0, 1, 0, NA, 0, 1, 0, NA, 0, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, NA, 1, 0, 1, NA, 0, 0, 1, 0, 1, 1, 1, 0, 1)
T_Snow <- c(0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, NA, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 1, NA, 1, 0, 1, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0)
T_Storms <- c(0, 0, 0, 1, 1, 1, 0, 1, 0, 1, NA, 0, 0, 0, 0, 1, 0, NA, 0, 0, 1, 0, 0, NA, 1, 1, NA, 0, 0, NA, 0, 1, 0, NA, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, NA, 1, 0, NA, 0, 0, 0, 1, 1, 0, 1, NA, NA, 1)
T_Waterfalls <- c(0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 1, 0, NA, 1, 0, 1, 0, 0, 0, NA, 0, 0, 0, NA, NA, 0)
T_Waves <- c(0, 1, 0, 1, 1, 0, 1, NA, 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, 0, 0, 1, 0, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, NA, 1, 0, 0, 0, 1, 0, 0, NA, 0, 1, 0, 0, 0, 0, 0, 1, 1, NA, 1, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0)
data <- data.frame (T_Darkness, T_Day, T_Light, T_Night, T_Rain, T_Rainbows, T_Snow, T_Storms, T_Waterfalls, T_Waves)
data$cons <- 1
`
使用具有
的小鼠估算的数据
MI.Data <- mice(data,m=5,maxit=50,meth='pmm',seed=500)
这似乎是由于未正确找到 R2MLwiN 中的某些模型提取方法，这应该已在最近发布的 0.8-2 版本的软件包中得到修复。 运行 你的例子给出了以下结果：
> pool(fitMI)
Call: pool(object = fitMI)
Pooled coefficients:
FP_Intercept_T_Darkness FP_Intercept_T_Day FP_Intercept_T_Light FP_Intercept_T_Night FP_Intercept_T_Rain
-0.9687210917 -1.0720602274 -0.9584792256 -1.1816471815 -0.7082406878
FP_Intercept_T_Rainbows FP_Intercept_T_Snow FP_Intercept_T_Storms FP_Intercept_T_Waterfalls FP_Intercept_T_Waves
-0.0455361903 -0.7537600398 -0.3883027434 -1.2365225554 -0.6423609257
RP1_var_bcons_1 RP1_cov_bcons_1_bcons_2 RP1_var_bcons_2 RP1_cov_bcons_1_bcons_3 RP1_cov_bcons_2_bcons_3
1.0000000000 0.0508168936 1.0000000000 0.2744663656 0.1625871509
RP1_var_bcons_3 RP1_cov_bcons_1_bcons_4 RP1_cov_bcons_2_bcons_4 RP1_cov_bcons_3_bcons_4 RP1_var_bcons_4
1.0000000000 0.0013987361 0.0576194786 0.0201622359 1.0000000000
RP1_cov_bcons_1_bcons_5 RP1_cov_bcons_2_bcons_5 RP1_cov_bcons_3_bcons_5 RP1_cov_bcons_4_bcons_5 RP1_var_bcons_5
-0.0220604800 0.1620389074 0.0956511647 -0.0242812764 1.0000000000
RP1_cov_bcons_1_bcons_6 RP1_cov_bcons_2_bcons_6 RP1_cov_bcons_3_bcons_6 RP1_cov_bcons_4_bcons_6 RP1_cov_bcons_5_bcons_6
0.2644620836 0.0555731133 0.1911445856 0.2584619522 0.1523280591
RP1_var_bcons_6 RP1_cov_bcons_1_bcons_7 RP1_cov_bcons_2_bcons_7 RP1_cov_bcons_3_bcons_7 RP1_cov_bcons_4_bcons_7
1.0000000000 0.1877118051 0.0872156173 0.2800109982 0.1433261335
RP1_cov_bcons_5_bcons_7 RP1_cov_bcons_6_bcons_7 RP1_var_bcons_7 RP1_cov_bcons_1_bcons_8 RP1_cov_bcons_2_bcons_8
-0.0006230903 0.1582182944 1.0000000000 -0.0749104023 0.1435756236
RP1_cov_bcons_3_bcons_8 RP1_cov_bcons_4_bcons_8 RP1_cov_bcons_5_bcons_8 RP1_cov_bcons_6_bcons_8 RP1_cov_bcons_7_bcons_8
0.0537744537 0.2291038185 0.2553031743 0.2716509402 0.1914017051
RP1_var_bcons_8 RP1_cov_bcons_1_bcons_9 RP1_cov_bcons_2_bcons_9 RP1_cov_bcons_3_bcons_9 RP1_cov_bcons_4_bcons_9
1.0000000000 0.1936145425 0.2835071683 0.0144172618 0.3326070011
RP1_cov_bcons_5_bcons_9 RP1_cov_bcons_6_bcons_9 RP1_cov_bcons_7_bcons_9 RP1_cov_bcons_8_bcons_9 RP1_var_bcons_9
0.1372590512 0.2854030728 0.0750594735 0.2545967996 1.0000000000
RP1_cov_bcons_1_bcons_10 RP1_cov_bcons_2_bcons_10 RP1_cov_bcons_3_bcons_10 RP1_cov_bcons_4_bcons_10 RP1_cov_bcons_5_bcons_10
0.3137466609 0.3498021364 0.2846792042 0.1126367375 0.2416045219
RP1_cov_bcons_6_bcons_10 RP1_cov_bcons_7_bcons_10 RP1_cov_bcons_8_bcons_10 RP1_cov_bcons_9_bcons_10 RP1_var_bcons_10
0.2137401104 0.1849118918 0.2134640366 0.6101759672 1.0000000000
Fraction of information about the coefficients missing due to nonresponse:
FP_Intercept_T_Darkness FP_Intercept_T_Day FP_Intercept_T_Light FP_Intercept_T_Night FP_Intercept_T_Rain
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
FP_Intercept_T_Rainbows FP_Intercept_T_Snow FP_Intercept_T_Storms FP_Intercept_T_Waterfalls FP_Intercept_T_Waves
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_1 RP1_cov_bcons_1_bcons_2 RP1_var_bcons_2 RP1_cov_bcons_1_bcons_3 RP1_cov_bcons_2_bcons_3
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_3 RP1_cov_bcons_1_bcons_4 RP1_cov_bcons_2_bcons_4 RP1_cov_bcons_3_bcons_4 RP1_var_bcons_4
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_1_bcons_5 RP1_cov_bcons_2_bcons_5 RP1_cov_bcons_3_bcons_5 RP1_cov_bcons_4_bcons_5 RP1_var_bcons_5
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_1_bcons_6 RP1_cov_bcons_2_bcons_6 RP1_cov_bcons_3_bcons_6 RP1_cov_bcons_4_bcons_6 RP1_cov_bcons_5_bcons_6
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_6 RP1_cov_bcons_1_bcons_7 RP1_cov_bcons_2_bcons_7 RP1_cov_bcons_3_bcons_7 RP1_cov_bcons_4_bcons_7
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_5_bcons_7 RP1_cov_bcons_6_bcons_7 RP1_var_bcons_7 RP1_cov_bcons_1_bcons_8 RP1_cov_bcons_2_bcons_8
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_3_bcons_8 RP1_cov_bcons_4_bcons_8 RP1_cov_bcons_5_bcons_8 RP1_cov_bcons_6_bcons_8 RP1_cov_bcons_7_bcons_8
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_8 RP1_cov_bcons_1_bcons_9 RP1_cov_bcons_2_bcons_9 RP1_cov_bcons_3_bcons_9 RP1_cov_bcons_4_bcons_9
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_5_bcons_9 RP1_cov_bcons_6_bcons_9 RP1_cov_bcons_7_bcons_9 RP1_cov_bcons_8_bcons_9 RP1_var_bcons_9
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_1_bcons_10 RP1_cov_bcons_2_bcons_10 RP1_cov_bcons_3_bcons_10 RP1_cov_bcons_4_bcons_10 RP1_cov_bcons_5_bcons_10
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_6_bcons_10 RP1_cov_bcons_7_bcons_10 RP1_cov_bcons_8_bcons_10 RP1_cov_bcons_9_bcons_10 RP1_var_bcons_10
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
我在使用 R 中的 MICE 包时遇到问题，特别是在汇集估算数据集方面。
我是 运行 多级二项式逻辑回归，Level1 - 主题（参与者对 10 个关于不同主题的问题的回答，例如黑暗，白天）嵌套在 Level2 - 个体中。
模型使用R2MLwiN创建，公式为
> fit1 <-runMLwiN( c(probit(T_Darkness, cons), probit(T_Day, cons), probit(T_Light, cons), probit(T_Night, cons), probit(T_Rain, cons), probit(T_Rainbows, cons), probit(T_Snow, cons), probit(T_Storms, cons), probit(T_Waterfalls, cons), probit(T_Waves, cons)) ~ 1, D=c("Mixed", "Binomial", "Binomial","Binomial","Binomial", "Binomial", "Binomial", "Binomial", "Binomial", "Binomial" ,"Binomial"), estoptions = list(EstM = 0), data=data)
很遗憾，所有 Level1（主题）回复中都缺少数据。
我一直在使用 mice 包 ([CRAN][1]) 来乘法估算缺失值。
我可以使用公式 > fitMI <- (with(MI.Data, runMLwiN( c(probit(T_Darkness, cons), probit(T_Day, cons), probit(T_Light, cons), probit(T_Night, cons), probit(T_Rain, cons), probit(T_Rainbows, cons), probit(T_Snow, cons), probit(T_Storms, cons), probit(T_Waterfalls, cons), probit(T_Waves, cons)) ~ 1, D=c("Mixed", "Binomial", "Binomial","Binomial","Binomial", "Binomial", "Binomial", "Binomial", "Binomial", "Binomial" ,"Binomial"), estoptions = list(EstM = 0), data=data)))
但是，当我将分析与调用代码 > pool(fitMI) 合并时，它失败了，错误为：
Error in pool(with(tempData, runMLwiN(c(probit(T_Darkness, cons), probit(T_Day, :
Object has no coef() method.
我不确定为什么说没有系数，因为对各个 MI 数据集的分析提供了固定部分（系数）和随机部分（协方差）
对于出现问题的任何帮助将不胜感激。
我应该警告你，这是我第一次尝试使用 R 和多级建模。 我也知道有一个 MlwiN 包 ([REALCOM][2]) 可以做到这一点，但我没有使用 MLwiN 软件的背景。
谢谢 强尼
更新 - R 可重现示例
使用的库
library(R2MLwiN)
library(mice)
数据子集 `
T_Darkness <- c(0, 1, 0, 0, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, NA, 1, 0, NA,NA, 1, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, NA, 1, 0)
T_Day <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, NA, NA, 0)
T_Light <- c(0, 0, NA, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0)
T_Night <- c(0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,NA, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, NA, 0, 0)
T_Rain <- c(1, 0, 0, 1, 1, 0, 0, NA, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, NA, 1, NA, 0, 0, 0, 0, 1, NA, 1, 0, 0, 0, 0, 1, NA, 0, 0)
T_Rainbows <- c(1, 1, 1, 1, 0, 1, 0, 1, 0, 1, NA, 1, 1, 0, 0, 1, 0, NA, 0, 1, 0, NA, 0, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, NA, 1, 0, 1, NA, 0, 0, 1, 0, 1, 1, 1, 0, 1)
T_Snow <- c(0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, NA, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 1, NA, 1, 0, 1, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0)
T_Storms <- c(0, 0, 0, 1, 1, 1, 0, 1, 0, 1, NA, 0, 0, 0, 0, 1, 0, NA, 0, 0, 1, 0, 0, NA, 1, 1, NA, 0, 0, NA, 0, 1, 0, NA, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, NA, 1, 0, NA, 0, 0, 0, 1, 1, 0, 1, NA, NA, 1)
T_Waterfalls <- c(0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 1, 0, NA, 1, 0, 1, 0, 0, 0, NA, 0, 0, 0, NA, NA, 0)
T_Waves <- c(0, 1, 0, 1, 1, 0, 1, NA, 0, 0, NA, 0, 0, 0, NA, 1, 0, 0, 0, 0, 1, 0, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, 0, NA, 1, 0, 0, 0, 1, 0, 0, NA, 0, 1, 0, 0, 0, 0, 0, 1, 1, NA, 1, 1, NA, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0)
data <- data.frame (T_Darkness, T_Day, T_Light, T_Night, T_Rain, T_Rainbows, T_Snow, T_Storms, T_Waterfalls, T_Waves)
data$cons <- 1
`
使用具有
的小鼠估算的数据MI.Data <- mice(data,m=5,maxit=50,meth='pmm',seed=500)
这似乎是由于未正确找到 R2MLwiN 中的某些模型提取方法，这应该已在最近发布的 0.8-2 版本的软件包中得到修复。 运行 你的例子给出了以下结果：
> pool(fitMI)
Call: pool(object = fitMI)
Pooled coefficients:
FP_Intercept_T_Darkness FP_Intercept_T_Day FP_Intercept_T_Light FP_Intercept_T_Night FP_Intercept_T_Rain
-0.9687210917 -1.0720602274 -0.9584792256 -1.1816471815 -0.7082406878
FP_Intercept_T_Rainbows FP_Intercept_T_Snow FP_Intercept_T_Storms FP_Intercept_T_Waterfalls FP_Intercept_T_Waves
-0.0455361903 -0.7537600398 -0.3883027434 -1.2365225554 -0.6423609257
RP1_var_bcons_1 RP1_cov_bcons_1_bcons_2 RP1_var_bcons_2 RP1_cov_bcons_1_bcons_3 RP1_cov_bcons_2_bcons_3
1.0000000000 0.0508168936 1.0000000000 0.2744663656 0.1625871509
RP1_var_bcons_3 RP1_cov_bcons_1_bcons_4 RP1_cov_bcons_2_bcons_4 RP1_cov_bcons_3_bcons_4 RP1_var_bcons_4
1.0000000000 0.0013987361 0.0576194786 0.0201622359 1.0000000000
RP1_cov_bcons_1_bcons_5 RP1_cov_bcons_2_bcons_5 RP1_cov_bcons_3_bcons_5 RP1_cov_bcons_4_bcons_5 RP1_var_bcons_5
-0.0220604800 0.1620389074 0.0956511647 -0.0242812764 1.0000000000
RP1_cov_bcons_1_bcons_6 RP1_cov_bcons_2_bcons_6 RP1_cov_bcons_3_bcons_6 RP1_cov_bcons_4_bcons_6 RP1_cov_bcons_5_bcons_6
0.2644620836 0.0555731133 0.1911445856 0.2584619522 0.1523280591
RP1_var_bcons_6 RP1_cov_bcons_1_bcons_7 RP1_cov_bcons_2_bcons_7 RP1_cov_bcons_3_bcons_7 RP1_cov_bcons_4_bcons_7
1.0000000000 0.1877118051 0.0872156173 0.2800109982 0.1433261335
RP1_cov_bcons_5_bcons_7 RP1_cov_bcons_6_bcons_7 RP1_var_bcons_7 RP1_cov_bcons_1_bcons_8 RP1_cov_bcons_2_bcons_8
-0.0006230903 0.1582182944 1.0000000000 -0.0749104023 0.1435756236
RP1_cov_bcons_3_bcons_8 RP1_cov_bcons_4_bcons_8 RP1_cov_bcons_5_bcons_8 RP1_cov_bcons_6_bcons_8 RP1_cov_bcons_7_bcons_8
0.0537744537 0.2291038185 0.2553031743 0.2716509402 0.1914017051
RP1_var_bcons_8 RP1_cov_bcons_1_bcons_9 RP1_cov_bcons_2_bcons_9 RP1_cov_bcons_3_bcons_9 RP1_cov_bcons_4_bcons_9
1.0000000000 0.1936145425 0.2835071683 0.0144172618 0.3326070011
RP1_cov_bcons_5_bcons_9 RP1_cov_bcons_6_bcons_9 RP1_cov_bcons_7_bcons_9 RP1_cov_bcons_8_bcons_9 RP1_var_bcons_9
0.1372590512 0.2854030728 0.0750594735 0.2545967996 1.0000000000
RP1_cov_bcons_1_bcons_10 RP1_cov_bcons_2_bcons_10 RP1_cov_bcons_3_bcons_10 RP1_cov_bcons_4_bcons_10 RP1_cov_bcons_5_bcons_10
0.3137466609 0.3498021364 0.2846792042 0.1126367375 0.2416045219
RP1_cov_bcons_6_bcons_10 RP1_cov_bcons_7_bcons_10 RP1_cov_bcons_8_bcons_10 RP1_cov_bcons_9_bcons_10 RP1_var_bcons_10
0.2137401104 0.1849118918 0.2134640366 0.6101759672 1.0000000000
Fraction of information about the coefficients missing due to nonresponse:
FP_Intercept_T_Darkness FP_Intercept_T_Day FP_Intercept_T_Light FP_Intercept_T_Night FP_Intercept_T_Rain
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
FP_Intercept_T_Rainbows FP_Intercept_T_Snow FP_Intercept_T_Storms FP_Intercept_T_Waterfalls FP_Intercept_T_Waves
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_1 RP1_cov_bcons_1_bcons_2 RP1_var_bcons_2 RP1_cov_bcons_1_bcons_3 RP1_cov_bcons_2_bcons_3
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_3 RP1_cov_bcons_1_bcons_4 RP1_cov_bcons_2_bcons_4 RP1_cov_bcons_3_bcons_4 RP1_var_bcons_4
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_1_bcons_5 RP1_cov_bcons_2_bcons_5 RP1_cov_bcons_3_bcons_5 RP1_cov_bcons_4_bcons_5 RP1_var_bcons_5
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_1_bcons_6 RP1_cov_bcons_2_bcons_6 RP1_cov_bcons_3_bcons_6 RP1_cov_bcons_4_bcons_6 RP1_cov_bcons_5_bcons_6
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_6 RP1_cov_bcons_1_bcons_7 RP1_cov_bcons_2_bcons_7 RP1_cov_bcons_3_bcons_7 RP1_cov_bcons_4_bcons_7
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_5_bcons_7 RP1_cov_bcons_6_bcons_7 RP1_var_bcons_7 RP1_cov_bcons_1_bcons_8 RP1_cov_bcons_2_bcons_8
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_3_bcons_8 RP1_cov_bcons_4_bcons_8 RP1_cov_bcons_5_bcons_8 RP1_cov_bcons_6_bcons_8 RP1_cov_bcons_7_bcons_8
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_var_bcons_8 RP1_cov_bcons_1_bcons_9 RP1_cov_bcons_2_bcons_9 RP1_cov_bcons_3_bcons_9 RP1_cov_bcons_4_bcons_9
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_5_bcons_9 RP1_cov_bcons_6_bcons_9 RP1_cov_bcons_7_bcons_9 RP1_cov_bcons_8_bcons_9 RP1_var_bcons_9
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_1_bcons_10 RP1_cov_bcons_2_bcons_10 RP1_cov_bcons_3_bcons_10 RP1_cov_bcons_4_bcons_10 RP1_cov_bcons_5_bcons_10
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367
RP1_cov_bcons_6_bcons_10 RP1_cov_bcons_7_bcons_10 RP1_cov_bcons_8_bcons_10 RP1_cov_bcons_9_bcons_10 RP1_var_bcons_10
0.5714367 0.5714367 0.5714367 0.5714367 0.5714367