na_mean {imputeTS} | R Documentation |
Missing Value Imputation by Mean Value
Description
Missing value replacement by mean values. Different means like median, mean, mode possible.
Usage
na_mean(x, option = "mean", maxgap = Inf)
Arguments
x |
Numeric Vector ( |
option |
Algorithm to be used. Accepts the following input:
|
maxgap |
Maximum number of successive NAs to still perform imputation on. Default setting is to replace all NAs without restrictions. With this option set, consecutive NAs runs, that are longer than 'maxgap' will be left NA. This option mostly makes sense if you want to treat long runs of NA afterwards separately. |
Details
Missing values get replaced by overall mean values. The function calculates the mean, median, mode, harmonic or geometric mean over all the non-NA values and replaces all NAs with this value. Option 'mode' replaces NAs with the most frequent value in the time series. If two or more values occur equally frequent, the function imputes the lower value. Due to their calculation formula geometric and harmonic mean are not well defined for negative values or zero values in the input series.
In general using the mean for imputation imputation is mostly a suboptimal choice and should be handled with great caution.
Value
Vector (vector
) or Time Series (ts
)
object (dependent on given input at parameter x)
Author(s)
Steffen Moritz
See Also
na_interpolation
,
na_kalman
, na_locf
,
na_ma
,
na_random
, na_replace
,
na_seadec
, na_seasplit
Examples
# Prerequisite: Create Time series with missing values
x <- ts(c(2, 3, 4, 5, 6, NA, 7, 8))
# Example 1: Perform imputation with the overall mean
na_mean(x)
# Example 2: Perform imputation with overall median
na_mean(x, option = "median")
# Example 3: Same as example 1, just written with pipe operator
x %>% na_mean()