# compute.sim: Similarity In FSMUMI: Imputation of Time Series Based on Fuzzy Logic

## Description

Compute 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 v univariate signals. A higher similarity (Similarity \in [0, 1]) highlights a more accurate method for completing missing values. Y and X must have the same length, otherwise an error will be displayed. Input vectors do not contains NA, if not it a warning will be diplayed.

## Author(s)

Thi-Thu-Hong Phan, Andre Bigand, Emilie Poisson-Caillault

## Examples

  1 2 3 4 5 6 7 8 9 10 data(dataFSMUMI) X <- dataFSMUMI[, 1] ; Y <- dataFSMUMI[, 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(5, 100) Y <- X compute.sim(Y,X) 

FSMUMI documentation built on May 2, 2019, 12:40 p.m.