kssa_plot {kssa}

kssa_plot function

Description

Function to plot the results of kssa for easy interpretation

Usage

kssa_plot(results, type, metric)

Arguments

results

An object with results produced with function kssa

type

A character value with the type of plot to show. It can be "summary" or "complete".

metric

A character with the performance metric to be plotted. It can be "rmse", "mase," "cor", or "smape"

• "rmse" - Root Mean Squared Error (default choice)

• "mase" - Mean Absolute Scaled Error

• "smape" - Symmetric Mean Absolute Percentage Error

• "cor" - Pearson correlation coefficient

For further details on these metrics please check package Metrics

Value

A plot of kssa results in which imputation methods are ordered from lower to higher (left to right) error.

Examples

# Example 1: Plot the results from comparing all imputation methods

library("kssa")
library("imputeTS")

# Create 20% random missing data in tsAirgapComplete time series from imputeTS
airgap_na <- missMethods::delete_MCAR(as.data.frame(tsAirgapComplete), 0.2)

# Convert to time series object
airgap_na_ts <- ts(airgap_na, start = c(1959, 1), end = c(1997, 12), frequency = 12)

# Apply the kssa algorithm with 5 segments,
# 10 iterations, 20% of missing data, and
# compare among all available methods in the package.
# Remember that percentmd must match with
# the real percentage of missing data in the input time series

results_kssa <- kssa(airgap_na_ts,
  start_methods = "all",
  actual_methods = "all",
  segments = 5,
  iterations = 10,
  percentmd = 0.2
)

kssa_plot(results_kssa, type = "complete", metric = "rmse")

# Conclusion: Since kssa_plot is ordered from lower to
# higher error (left to right), method 'linear_i' is the best to
# impute missing data in airgap_na_ts. Notice that method 'locf' is the worst

# To obtain imputations with the best method, or any method of preference
# please use function get_imputations




# Example 2: Plot the results when only applying locf and linear interpolation

library("kssa")
library("imputeTS")

# Create 20% random missing data in tsAirgapComplete time series from imputeTS
airgap_na <- missMethods::delete_MCAR(as.data.frame(tsAirgapComplete), 0.2)

# Convert to time series object
airgap_na_ts <- ts(airgap_na, start = c(1959, 1), end = c(1997, 12), frequency = 12)

# Apply the kssa algorithm with 5 segments,
# 10 iterations, 20% of missing data, and compare among all
# applied methods (locf and linear interpolation).
# Remember that percentmd must match with
# the real percentage of missing data in the input time series

results_kssa <- kssa(airgap_na_ts,
  start_methods = c("linear_i", "locf"),
  actual_methods = c("linear_i", "locf"),
  segments = 5,
  iterations = 10,
  percentmd = 0.2
)

kssa_plot(results_kssa, type = "complete", metric = "rmse")