r-如何使用分类值估算连续值

Question

连续矢量中缺少值

y <- c(8, 7, NA, 4, 6)

我有的邮政编码

x <- factor(c("94562", "98705", "94122", "94539", "94122"))

我想用邮政编码来估算y。我试过

library(mice)
mice.impute.norm(y = y, ry = is.na(y), x = x)

但我收到一个错误，指示它无法进行交叉产品（由于我的rstudio会话挂起，无法发布特定消息）。

关于如何用鼠标或其他包装做到这一点，有什么想法吗？

---

编辑：部分数据的dput

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"98126", "98144", "98146", "98178", "98199", "98223", "98236", 
"98260", "98312", "98392", "98506", "98686"), class = "factor")), .Names = c("P", 
"Z"), row.names = c(NA, -1000L), class = "data.frame")

Answer 1

有两种方法可以做到这一点，假设您有一个带有zipcode和y:的数据帧

您可以使用创建这样一个带有起始数据的数据帧

df <- data.frame("zipcode" = x, y)

使用数据表

library(data.table)
# Create an imputation function for values that are NA
impute.mean <- function(x) replace(x, is.na(x), mean(x, na.rm = TRUE))
# Cast df as a data table
df <- as.data.table(df)
# Use the function to impute y by zipcode
df[, y := impute.mean(y), by = zipcode]

使用dplyr

library(dplyr)
# Create an imputation function for values that are NA
impute.mean <- function(x) replace(x, is.na(x), mean(x, na.rm = TRUE))
# Use dplyr to group by and run function
df <- df %>%
  group_by(zipcode) %>%
  mutate(
    y = impute.mean(y)
  )

我建议下次找一找；这个问题以前可能已经回答过了，比如这里：

如何用R中的子集均值替换NA（用plyr估算？）

Answer 2

mice中的norm方法的问题在于，它不像R中的其他函数（例如lm）那样识别因子变量。因此，如果你的目标是以分类变量为条件，你必须首先对因子变量进行某种编码。

下面是一个使用分类变量的伪编码方案的示例。

# create data set with dummy coding for Z
dat.dummy <- as.data.frame(model.matrix(~Z, data=dat))
dat.dummy$P <- dat$P
# empty imputation (provides a template for the "predictorMatrix" and
# "method" arguments)
library(mice)
imp0 <- mice(dat.dummy, m=5, maxit=0)
# get defaults
predMatrix <- imp0$predictorMatrix
impMethod <- imp0$method
# choose "norm" for P (instead of the default "pmm")
impMethod["P"] <- "norm"
# impute 5 times, using 5 iterations each
imp1 <- mice(dat.dummy, m=5, maxit=5)

当使用model matrix()时，伪编码会自动完成，这会创建一系列指示变量，表示Z中的不同级别。

使用伪编码数据，mice将执行您想要的操作。