# na.interpolation

0th

Percentile

##### Missing Value Imputation by Interpolation

Uses either linear, spline or stineman interpolation to replace missing values.

##### Usage
na.interpolation(x, option = "linear", ...)
##### Arguments
x

Numeric Vector (vector) or Time Series (ts) object in which missing values shall be replaced

option

Algorithm to be used. Accepts the following input:

• "linear" - for linear interpolation using approx

• "spline" - for spline interpolation using spline

• "stine" - for Stineman interpolation using stinterp

...

Additional parameters to be passed through to approx or spline interpolation functions

##### Details

Missing values get replaced by values of a approx, spline or stinterp interpolation.

##### Value

Vector (vector) or Time Series (ts) object (dependent on given input at parameter x)

##### References

Johannesson, Tomas, et al. (2015). "Package stinepack".

na.kalman, na.locf, na.ma, na.mean, na.random, na.replace, na.seadec, na.seasplit

##### Aliases
• na.interpolation
##### Examples
# NOT RUN {
#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: Same as example 1, just written with pipe operator
x %>% na.interpolation

#Example 5: Same as example 2, just written with pipe operator
x %>% na.interpolation(option = "spline")

# }

Documentation reproduced from package imputeTS, version 2.7, License: GPL-3

### Community examples

Looks like there are no examples yet.