如何将predict函数与来自mice（）的汇总结果一起使用？

嗨，我刚开始在学校里使用R作为模块的一部分。我有一个缺少数据的数据集，我用mice（）来插补缺少的数据。我现在正尝试使用predict函数和我的汇总结果。但是，我发现了以下错误：

使用方法中的错误（“预测”）： 没有适用于“c（'mipo'，'data.frame'）类对象的“predict”方法

我已经在下面列出了我的全部代码，如果你们能帮助一个新手，我将不胜感激。谢谢

```{r}
library(magrittr)
library(dplyr)
train = read.csv("Train_Data.csv", na.strings=c("","NA"))
test = read.csv("Test_Data.csv", na.strings=c("","NA"))
cols <- c("naCardiac", "naFoodNutrition", "naGenitourinary", "naGastrointestinal", "naMusculoskeletal", "naNeurological", "naPeripheralVascular", "naPain", "naRespiratory", "naSkin")
train %<>%
       mutate_each_(funs(factor(.)),cols)
test %<>%
       mutate_each_(funs(factor(.)),cols)
str(train)
str(test)
```

```{r}
library(mice)
md.pattern(train)
```

```{r}
miTrain = mice(train, m = 5, maxit = 50, meth = "pmm")
```

```{r}
model = with(miTrain, lm(LOS ~ Age + Gender + Race + Temperature + RespirationRate + HeartRate + SystolicBP + DiastolicBP + MeanArterialBP + CVP + Braden + SpO2 + FiO2 + PO2_POCT + Haemoglobin + NumWBC + Haematocrit + NumPlatelets + ProthrombinTime + SerumAlbumin + SerumChloride + SerumPotassium + SerumSodium + SerumLactate + TotalBilirubin + ArterialpH + ArterialpO2 + ArterialpCO2 + ArterialSaO2 + Creatinine + Urea + GCS + naCardiac + GCS + naCardiac + naFoodNutrition + naGenitourinary + naGastrointestinal + naMusculoskeletal + naNeurological + naPeripheralVascular + naPain + naRespiratory + naSkin))
model
summary(model)
```

```{r}
modelResults = pool(model)
modelResults
```

```{r}
pred = predict(modelResults, newdata = test)
PredTest = data.frame(test$PatientID, modelResults)
str(PredTest)
summary(PredTest)
```

`{r}
图书馆（magrittr）
图书馆（dplyr）
train=read.csv（“train_Data.csv”，na.strings=c（“，”na”））
test=read.csv（“test_Data.csv”，na.strings=c（“，”na”））
cols实现这一点的一个稍微有点老套的方法可能是采用由
fit（）
创建的拟合模型之一，并用最终合并的估计值替换存储的系数。我还没有做过详细的测试，但它似乎正在处理这个简单的示例：

library(mice)

imp <- mice(nhanes, maxit = 2, m = 2)
fit <- with(data = imp, exp = lm(bmi ~ hyp + chl))
pooled <- pool(fit)

# Copy one of the fitted lm models fit to
#   one of the imputed datasets
pooled_lm = fit$analyses[[1]]
# Replace the fitted coefficients with the pooled
#   estimates (need to check they are replaced in
#   the correct order)
pooled_lm$coefficients = summary(pooled)$estimate

# Predict - predictions seem to match the
#   pooled coefficients rather than the original
#   lm that was copied
predict(fit$analyses[[1]], newdata = nhanes)
predict(pooled_lm, newdata = nhanes)

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