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

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

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)

Example output

sh: 1: cannot create /dev/null: Permission denied
[1] 2
[1] "non acceptable FB"
[1] 2
[1] 0
Warning message:
In compute.fb(Y, X) :
  Vectors of input and imputed values are equal and constant. By definition FB=0
[1] Inf
Warning message:
In compute.fb(Y, X) : X=-Y => FB=Inf

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