flar: Fuzzy Linear Regression using the Fuzzy Least Absolute Residual Method
Description
The function calculates fuzzy regression coeficients using the fuzzy least absolute
residual (FLAR) method proposed by Zeng et al. (2017)
for non-symmetric triangular fuzzy numbers.
Usage
flar(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 the second to last columns representing independent variable
observations. The first column is related to the intercept, so it consists of ones.
Missing values not allowed.
y
matrix of dependent variable observations. The first column contains the
central tendency, the second column the left spread and the third column the right
spread of non-symmetric triangular fuzzy numbers. Missing values not allowed.
Details
The FLAR method expects real value input for the explanatory variables, and
non-symmetric triangular fuzzy numbers for the response variable. The prediction
returns non-symmetric triangular fuzzy numbers.
References
Zeng, W., Feng, Q. and Li, J. (2017) Fuzzy least absolute linear regression.
Applied Soft Computing 52: 1009-1019.