r - 如何使用分类值估算连续值

Question

我在连续向量中有缺失值

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

我有邮政编码

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

我想使用邮政编码来估算 y。我努力了

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

但是我收到错误消息，表明它不能做交叉产品（由于我的 rstudio 会话挂起，所以无法发布特定消息）。

关于如何用鼠标或其他包做到这一点的任何想法？

---

编辑：dput部分数据

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"98036", "98038", "98046", "98053", "98072", "98075", "98101", 
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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

假设您有一个带有zipcodeand的数据框，有两种方法可以做到这一点y：

您可以使用以下起始数据创建这样的数据框：

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)
  )

我建议下次搜索一下；这个问题之前可能已经回答过，比如这里：

如何用R中的子集替换NA（用plyr估算？）

Answer 2

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

这是一个对分类变量使用虚拟编码方案的示例。

# 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)

虚拟编码是在使用时自动完成的model matrix()，这会创建一系列指示变量来表示Z.

使用虚拟编码数据，mice将做你想做的事。