# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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.