| na_interpolation {imputeTS} | R Documentation |
Missing Value Imputation by Interpolation
Description
Uses either linear, spline or stineman interpolation to replace missing values.
Usage
na_interpolation(x, option = "linear", 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. |
... |
Additional parameters to be passed through to approx or spline interpolation functions |
Details
Missing values get replaced by values of approx, spline or stinterp interpolation.
The na_interpolation function also supports the use of additional parameters from the respective underlying interpolation functions. While usually not really needed, it is useful to know that this advanced use is in principle possible. These additional parameters are not specified explicitly in the na_interpolation function documentation. Take a look into the documentation of the stinterp, approx and spline functions to get an overview about these additional parameters.
An example for such a parameter is the 'method' argument of spline, which can be used to
further specify the type of spline to be used. Possible values are "fmm", "natural",
"periodic", "monoH.FC" and "hyman" (as can be seen in the spline
documentation). The respective function call using this additional parameter would
look like this:
na_interpolation(x, option ="spline", method ="natural")
Like in this example other additional detail parameters (gained from approx, spline, stinterp documentation) can be used by just including them in the na_interpolation function call. As already mentioned, these advanced possibilities for settings parameters are only helpful for specific use cases. For regular use the standard parameters provided directly in the na_interpolation documentation should be more than enough.
Value
Vector (vector) or Time Series (ts)
object (dependent on given input at parameter x)
Author(s)
Steffen Moritz, Ron Hause
References
Johannesson, Tomas, et al. (2015). "Package stinepack".
See Also
na_kalman, na_locf,
na_ma, na_mean,
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 linear interpolation
na_interpolation(x)
# Example 2: Perform spline interpolation
na_interpolation(x, option = "spline")
# Example 3: Perform stine interpolation
na_interpolation(x, option = "stine")
# Example 4: Perform linear interpolation, with additional parameter pass through from spline()
# Take a look at the 'Details' section of the na_interpolation documentation
# for more information about advanced parameter pass through options
na_interpolation(x, option ="spline", method ="natural")
# Example 5: Same as example 1, just written with pipe operator
x %>% na_interpolation()
# Example 6: Same as example 2, just written with pipe operator
x %>% na_interpolation(option = "spline")