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imputation - na.approx 和 na.locf 行为不正常

问题描述

我正在尝试计算不同国家/地区时间序列的估算值。这段代码以前运行良好，但现在估算的值都是错误的……我无法弄清楚问题所在，我已经尝试了我能想到的一切。

我们的规则是：

# load library for imputation
library(zoo)

# expand table to show NAs
output_table_imp = expand(output_table, transport_mode, year, country_code)
output_table_imp = full_join(output_table_imp, output_table)

# add imputated values
output_table_imp <- output_table_imp %>%
  group_by(transport_mode, country_code) %>%
  mutate(fatalities_imp= na.approx(fatalities,na.rm=FALSE)) %>%   # linear interpolation
  mutate(fatalities_imp= na.locf.default(fatalities_imp,na.rm=FALSE)) %>% # missing values at the end of a time series (copy last non-NA value)
  mutate(fatalities_imp= na.locf(fatalities_imp,fromLast=TRUE, na.rm=FALSE)) %>% # missing values at the start of a time series (copy first non-NA value)

我的数据框由几列组成：transport_mode、country_code、year、fatities。我不确定如何在这里分享我的数据？这是一张有 3600 个观测值的大表……

这些是原始数字： 在此处输入图像描述

这些是估算值。您可以立即看到 CY、IE 和 LT 存在问题。 在此处输入图像描述

数据框如下所示： 在此处输入图像描述

标签： imputationna.approx

解决方案

Your code looks somehow overly complicated. Don't know about the zoo details - but pretty sure you could get it also to work.

With the imputeTS package you could just take your whole data. frame (it assumes each column is a separate time series) and the package performs imputation for each of this series. (unfortunately your code has no data, but I guess this would be your output_table_imp data.frame after expansion)

Just like this:

library("imputeTS")
na_interpolation(output_table_imp, option = "linear")

We also don't have to change something for NA treatment at the beginning and at the end, since your requirements are the default in the na_interpolation function.

These were your requirements:

  • Values missing at the end of a time series are given the last known value in the series.

  • Values missing at the beginning of a time series are given the first known value in the series.

Here a toy example:

# Test time series with NAs at start, middle, end
test <- c(NA,NA,1,2,3,NA,NA,6,7,8,NA,NA)

# Perform linear interpolation
na_interpolation(test, option = "linear")

#Results
> 1 1 1 2 3 4 5 6 7 8 8 8

So see, this works perfectly fine.

Works also perfectly with a data.frame (as a said, a column is interpreted as a time series):

# Create three time series and combine them into 1 data.frame
ts1 <- c(NA,NA,1,2,3,NA,NA,6,7,8,NA,NA)
ts2 <- c(NA,1,1,2,3,NA,3,6,7,8,NA,NA)
ts3 <- c(NA,3,1,2,3,NA,3,6,7,8,NA,NA)
df <- data.frame(ts1,ts2,ts3)

na_interpolation(df, option = "linear")

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