从 R 中的 Hmisc 包中提取完整的数据帧
Extracting complete dataframe from Hmisc package in R
我使用 aregImpute 来估算缺失值，然后我使用 impute.transcan 函数尝试使用以下代码获取完整的数据集。
impute_arg <- aregImpute(~ age + job + marital + education + default +
balance + housing + loan + contact + day + month + duration + campaign +
pdays + previous + poutcome + y , data = mov.miss, n.impute = 10 , nk =0)
imputed <- impute.transcan(impute_arg, imputation=1, data=mov.miss, list.out=TRUE, pr=FALSE, check=FALSE)
y <- completed[names(imputed)]
当我使用 str(y) 时，它已经给了我一个数据框但是 NAs 因为它之前没有被估算，我的问题是如何在没有 NAs 之后获得完整的数据集插补？
str(y)
'data.frame': 4521 obs. of 17 variables:
$ age : int 30 NA 35 30 NA 35 36 39 41 43 ...
$ job : Factor w/ 12 levels "admin.","blue-collar",..: 11 8 5 5 2 5 7 10 3 8 ...
$ marital : Factor w/ 3 levels "divorced","married",..: 2 2 3 2 2 3 2 2 2 2 ...
$ education: Factor w/ 4 levels "primary","secondary",..: 1 2 3 3 2 3 NA 2 3 1 ...
$ default : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 NA 1 1 1 ...
$ balance : int NA 4789 1350 1476 0 747 307 147 NA -88 ...
$ housing : Factor w/ 2 levels "no","yes": NA 2 2 2 NA 1 2 2 2 2 ...
$ loan : Factor w/ 2 levels "no","yes": 1 2 1 2 NA 1 1 NA 1 2 ...
$ contact : Factor w/ 3 levels "cellular","telephone",..: 1 1 1 3 3 1 1 1 NA 1 ...
$ day : int 19 NA 16 3 5 23 14 6 14 NA ...
$ month : Factor w/ 12 levels "apr","aug","dec",..: 11 9 1 7 9 4 NA 9 9 1 ...
$ duration : int 79 220 185 199 226 141 341 151 57 313 ...
$ campaign : int 1 1 1 4 1 2 1 2 2 NA ...
$ pdays : int -1 339 330 NA -1 176 330 -1 -1 NA ...
$ previous : int 0 4 NA 0 NA 3 2 0 0 2 ...
$ poutcome : Factor w/ 4 levels "failure","other",..: 4 1 1 4 4 1 2 4 4 1 ...
$ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
我自己测试了你的代码，它工作正常，除了最后一行：
y <- completed[names(imputed)]
我相信上面一行中有一个类型。另外，您甚至不需要 completed 函数。
此外，如果你想从impute.transcan函数中得到一个data.frame，那么用as.data.frame包裹它：
imputed <- as.data.frame(impute.transcan(impute_arg, imputation=1, data=mov.miss, list.out=TRUE, pr=FALSE, check=FALSE))
此外，如果你需要测试你的缺失数据模式，你也可以使用mice包提供的md.pattern函数。
我使用 aregImpute 来估算缺失值，然后我使用 impute.transcan 函数尝试使用以下代码获取完整的数据集。
impute_arg <- aregImpute(~ age + job + marital + education + default +
balance + housing + loan + contact + day + month + duration + campaign +
pdays + previous + poutcome + y , data = mov.miss, n.impute = 10 , nk =0)
imputed <- impute.transcan(impute_arg, imputation=1, data=mov.miss, list.out=TRUE, pr=FALSE, check=FALSE)
y <- completed[names(imputed)]
当我使用 str(y) 时，它已经给了我一个数据框但是 NAs 因为它之前没有被估算，我的问题是如何在没有 NAs 之后获得完整的数据集插补？
str(y)
'data.frame': 4521 obs. of 17 variables:
$ age : int 30 NA 35 30 NA 35 36 39 41 43 ...
$ job : Factor w/ 12 levels "admin.","blue-collar",..: 11 8 5 5 2 5 7 10 3 8 ...
$ marital : Factor w/ 3 levels "divorced","married",..: 2 2 3 2 2 3 2 2 2 2 ...
$ education: Factor w/ 4 levels "primary","secondary",..: 1 2 3 3 2 3 NA 2 3 1 ...
$ default : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 NA 1 1 1 ...
$ balance : int NA 4789 1350 1476 0 747 307 147 NA -88 ...
$ housing : Factor w/ 2 levels "no","yes": NA 2 2 2 NA 1 2 2 2 2 ...
$ loan : Factor w/ 2 levels "no","yes": 1 2 1 2 NA 1 1 NA 1 2 ...
$ contact : Factor w/ 3 levels "cellular","telephone",..: 1 1 1 3 3 1 1 1 NA 1 ...
$ day : int 19 NA 16 3 5 23 14 6 14 NA ...
$ month : Factor w/ 12 levels "apr","aug","dec",..: 11 9 1 7 9 4 NA 9 9 1 ...
$ duration : int 79 220 185 199 226 141 341 151 57 313 ...
$ campaign : int 1 1 1 4 1 2 1 2 2 NA ...
$ pdays : int -1 339 330 NA -1 176 330 -1 -1 NA ...
$ previous : int 0 4 NA 0 NA 3 2 0 0 2 ...
$ poutcome : Factor w/ 4 levels "failure","other",..: 4 1 1 4 4 1 2 4 4 1 ...
$ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
我自己测试了你的代码，它工作正常，除了最后一行：
y <- completed[names(imputed)]
我相信上面一行中有一个类型。另外，您甚至不需要 completed 函数。
此外，如果你想从impute.transcan函数中得到一个data.frame，那么用as.data.frame包裹它：
imputed <- as.data.frame(impute.transcan(impute_arg, imputation=1, data=mov.miss, list.out=TRUE, pr=FALSE, check=FALSE))
此外，如果你需要测试你的缺失数据模式，你也可以使用mice包提供的md.pattern函数。