Description Usage Arguments Details Author(s) Examples

Calculate the FB between two univariate signals Y (imputed values) and X (true values).

1 | ```
compute.fb(Y, X, verbose = F)
``` |

`Y` |
vector of imputed values |

`X` |
vector of true values |

`verbose` |
if TRUE, print advice about the quality of the model |

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.

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

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)
``` |

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