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

Description Usage Arguments Details Author(s) Examples

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

1	compute.sim(Y, X)

`Y`	vector of imputed values
`X`	vector of true values

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.

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

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)