DTWBI (version 1.1)

compute.fb: 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.

Examples

# NOT RUN {
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)
# }