# compute.sim: Similarity In DTWBI: Imputation of Time Series Based on Dynamic Time Warping

## Description

Estimates the percentage of similarity of two univariate signals Y (imputed values) and X (true values).

## Usage

 1 compute.sim(Y, X) 

## Arguments

 Y vector of imputed values X vector of true values

## Details

This function returns the value of similarity of two vectors corresponding to univariate signals. A higher similarity (Similarity \in [0, 1]) highlights a more accurate method for completing missing values in univariate datasets. Both vectors Y and X must be of equal length, on the contrary an error will be displayed. In both input vectors, eventual NA will be excluded with a warning diplayed.

## Author(s)

Camille Dezecache, Hong T. T. Phan, Emilie Poisson-Caillault

## Examples

  1 2 3 4 5 6 7 8 9 10 data(dataDTWBI) X <- dataDTWBI[, 1] ; Y <- dataDTWBI[, 2] compute.sim(Y,X) # By definition, if true values is a constant vector # and one or more imputed values are equal to the true values, # similarity = 1. X <- rep(2, 10) Y <- X compute.sim(Y,X) 

DTWBI documentation built on May 2, 2019, 1:59 a.m.