compute.fb {DTWBI} R Documentation

## Fractional Bias (FB)

### 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)
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)


[Package DTWBI version 1.1 Index]