R中的推算后均值 [英] mean-before-after imputation in R

问题描述

我是R语言的新手.我的问题是如何使用缺失数据点前后的均值来估算缺失值?

I'm new in R. My question is how to impute missing value using mean of before and after of the missing data point?

示例；

使用每个NA上下的平均值作为推算值.

using the mean from the upper and lower of each NA as the impute value.

-第3行的平均值为38.5

-mean for row number 3 is 38.5

-第7行的平均值是32.5

-mean for row number 7 is 32.5

age
52.0
27.0
NA
23.0
39.0
32.0
NA
33.0
43.0

谢谢.

推荐答案

此处是使用zoo包中na.locf中的from解决方案的解决方案，该解决方案将每个NA替换为之前或之后的最新非NA.

Here a solution using from na.locf from zoo package which replaces each NA with the most recent non-NA prior or posterior to it.

0.5*(na.locf(x,fromlast=TRUE) + na.locf(x))
[1] 52.0 27.0 25.0 23.0 39.0 32.0 32.5 33.0 43.0

如果连续的NA不止一个，这里的优势.

the advantage here if you have more than one consecutive NA.

x <- c(52, 27, NA, 23, 39, NA, NA, 33, 43)
0.5*(na.locf(x,fromlast=TRUE) + na.locf(x))
[1] 52 27 25 23 39 36 36 33 43

编辑 不推荐使用rev自变量，因此我将其替换为fromlast

EDIT rev argument is deprecated so I replace it by fromlast

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