compute.fb: Fractional Bias (FB)

In DTWBI: Imputation of Time Series Based on Dynamic Time Warping

Description

Estimates the Fractional Bias (FB) of two univariate signals Y (imputed values) and X (true values).

Usage

compute.fb(Y, X, verbose = F)

Arguments

Y	vector of imputed values
X	vector of true values
verbose	if TRUE, print advice about the quality of the model

Details

This function returns the value of FB of two vectors corresponding to univariate signals, indicating whether predicted values are underestimated or overestimated compared to true values. A perfect imputation model gets FB = 0. An acceptable imputation model gives FB <= 0.3. 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 exluded with a warning diplayed.

Author(s)

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

Examples

data(dataDTWBI)
X <- dataDTWBI[, 1] ; Y <- dataDTWBI[, 2]
compute.fb(Y,X)
compute.fb(Y,X, verbose = TRUE)

# If mean(X)=mean(Y)=0, it is impossible to estimate FB,
# unless both true and imputed values vectors are constant.
# By definition, in this case, FB = 0.
X <- rep(0, 10) ; Y <- rep(0, 10)
compute.fb(Y,X)

# If true and imputed values are not zero and are opposed, FB = Inf.
X <- rep(runif(1), 10)
Y <- -X
compute.fb(Y,X)

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