Question

我在R中使用MICE包时出现问题，特别是汇集了推算数据集。

我正在运行多级二项式逻辑回归，其中Level1 - 主题（参与者对不同主题的10个问题的响应，例如Darkness，Day）嵌套在Level2 - 个体中。

使用R2MLwiN创建模型，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)

不幸的是，所有Level1（主题）响应中都缺少数据。我一直在使用mice包（[CRAN] [1]）来乘以错误的值。

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)))

将模型拟合到插补数据集

但是，当我使用调用代码> pool(fitMI)汇总分析时，它会失败，并显示错误：

Error in pool(with(tempData, runMLwiN(c(probit(T_Darkness, cons), probit(T_Day, : Object has no coef() method.

我不确定为什么它说没有系数，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都将非常感激。

我应该警告你，这是我第一次尝试使用R和多级建模。另外我知道有一个MlwiN软件包（[REALCOM] [2]）可以做到这一点，但我没有使用MLwiN软件的背景。

感谢约翰尼

更新 - R可重复的示例

使用的库

库（R2MLwiN）

库（小鼠）

数据子集 `

T_Darkness＆lt; - 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，0,0,0,0,0,0,0,0,0,0,0,0,0,0 ，0,0,0,0,0,1，NA，0,0,1,0,1,0,0,0,0,0，NA，1,0）

T_Day＆lt; - c（0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0，NA，0,0,0 ，NA，0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0，NA，0,0,0 ，0，NA，0,0,1,0,0,0,0,0,0,0,0,1,0,0，NA，NA，0）

T_Light＆lt; - 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，0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ，0,0,0,0,1，NA，1,0,0,0,0,0,0,0,0,0,0，NA，0,0）

T_Night＆lt; - c（0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0 ，0，NA，0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ，0,0，NA，0，NA，0,0,1,0,0,0,0,0,0,0,0,0，NA，0,0）

T_Rain＆lt; - 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＆lt; - 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＆lt; - 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，0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 ，0,0，NA，0,0,1，NA，1,0,1,1,0,0,0,0,0，NA，0,0,0）

T_Storms＆lt; - 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，1,0,1,0,0,0,0,0,0,0,0,0 ，1,0,0,0,1,0，NA，1,0，NA，0,0,0,1,1,0,1，NA，NA，1）

T_Waterfalls＆lt; - 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，0,0,0,0,0,1,0,0,0,1,0,0，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＆lt; - 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，NA，1,0,0,0,0,0,0，NA，0,1,0 ，0,0,0,0,1,1，NA，1,1，NA，0,0,0，NA，0,0,0，NA，0,0）

数据＆lt; - data.frame（T_Darkness，T_Day，T_Light，T_Night，T_Rain，T_Rainbows，T_Snow，T_Storms，T_Waterfalls，T_Waves）

数据$ cons＆lt; - 1

`

使用

鼠标估算数据

MI.Data＆lt; - mice（data，m = 5，maxit = 50，meth ='pmm'，seed = 500）

Answer 1

这似乎是由于R2MLwiN中的某些模型提取方法未正确找到，应该已在最近发布的0.8-2版本的软件包中修复。用这个运行你的例子给我以下结果：

> 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

使用R（MICE） - 缺少系数

1 个答案: