# compute.fb: Fractional Bias (FB) In FSMUMI: Imputation of Time Series Based on Fuzzy Logic

## Description

Calculate the FB between 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 FB value of two vectors univariate signals. This indicator indicates whether predicted values are underestimated or overestimated compared to true values. A perfect imputation model has FB = 0. An imputation model is acceptable when its FB value is less than or equal to 0.3 (FB <= 0.3). The two vectors Y and X are the same length, on the contrary an error will be displayed. In both input vectors, eventual NA will be exluded with a warning diplayed.

## Author(s)

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```data(dataFSMUMI) X <- dataFSMUMI[, 1] ; Y <- dataFSMUMI[, 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) ```

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