flar: Fuzzy Linear Regression using the Fuzzy Least Absolute...

In fuzzyreg: Fuzzy Linear Regression

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)

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.

Value

Returns a fuzzylm object that includes the model coefficients, limits for data predictions from the model and the input data.

Note

Preferred use is through the fuzzylm wrapper function with argument method = "flar".

References

Zeng, W., Feng, Q. and Li, J. (2017) Fuzzy least absolute linear regression. Applied Soft Computing 52: 1009-1019.

See Also

fuzzylm

Examples

   data(fuzzydat)
   fuzzylm(y ~ x, fuzzydat$dia, "flar", , , "yl", "yl")

fuzzyreg documentation built on March 31, 2023, 9:19 p.m.