bfrl: Fuzzy Linear Regression Using the Boskovitch Fuzzy Regression Line Method
Description
The function calculates fuzzy regression coeficients using the Boskovitch fuzzy
regression line method (BFRL) developed by Tanaka et al. (1989). Specifically, the
min problem is implemented in this function.
Usage
bfrl(x, y)
Value
Returns a fuzzylm object that includes the model coefficients, limits
for data predictions from the model and the input data.
Arguments
x
matrix with two colums, representing one independent variable observations.
The first column is
related to the intercept, so it consists of ones. Missing values not allowed.
y
three column matrix of dependent variable values and the respective spread.
Method assumes non-symmetric triangular fuzzy input. Missing values not allowed.
Details
The function input expects the response in form of a non-symmetric fuzzy
number and the predictors as crisp numbers. The prediction returns
non-symmetric triangular fuzzy numbers. The intercept is a non-symmetric triangular
fuzzy number and the slope is a crisp number that is returned as a triangular fuzzy
number with spreads equal to zero.
References
Skrabanek, P., Marek, J. and Pozdilkova, A. (2021) Boscovich Fuzzy Regression
Line. Mathematics 9: 685.