plotNA.distribution: Visualize Distribution of Missing Values In imputeTS: Time Series Missing Value Imputation

Description

Visualize the distribution of missing values within a time series.

Usage

 ```1 2 3 4``` ```plotNA.distribution(x, colPoints = "steelblue", colBackgroundMV = "indianred2", main = "Distribution of NAs", xlab = "Time", ylab = "Value", pch = 20, cexPoints = 0.8, col = "black", ...) ```

Arguments

 `x` Numeric Vector (`vector`) or Time Series (`ts`) object containing NAs `colPoints` Color of the points for each observation `colBackgroundMV` Color for the background for the NA sequences `main` Main label for the plot `xlab` Label for x axis of the plot `ylab` Label for y axis of plot `pch` Plotting 'character', i.e., symbol to use. `cexPoints` character (or symbol) expansion: a numerical vector. `col` Color for the lines. `...` Additional graphical parameters that can be passed through to plot

Details

This function visualizes the distribution of missing values within a time series. Therefore, the time series is plotted and whenever a value is NA the background is colored differently. This gives a nice overview, where in the time series most of the missing values occur.

Author(s)

Steffen Moritz

`plotNA.distributionBar`, `plotNA.gapsize`, `plotNA.imputations`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```#Example 1: Visualize the missing values in x x <- ts(c(1:11, 4:9,NA,NA,NA,11:15,7:15,15:6,NA,NA,2:5,3:7)) plotNA.distribution(x) #Example 2: Visualize the missing values in tsAirgap time series plotNA.distribution(tsAirgap) #Example 3: Same as example 1, just written with pipe operator x <- ts(c(1:11, 4:9,NA,NA,NA,11:15,7:15,15:6,NA,NA,2:5,3:7)) x %>% plotNA.distribution ```