plr: Fuzzy Linear Regression Using the Possibilistic Linear...

In fuzzyreg: Fuzzy Linear Regression

Fuzzy Linear Regression Using the Possibilistic Linear Regression Method

Description

The function calculates fuzzy regression coeficients using the possibilistic linear regression method (PLR) developed by Tanaka et al. (1989). Specifically, the min problem is implemented in this function.

Usage

plr(x, y, h = 0)

Arguments

x	matrix of n independent variable observations. The first column is related to the intercept, so it consists of ones. Missing values not allowed.
y	two column matrix of dependent variable values and the respective spread. Method assumes symmetric triangular fuzzy input, so the second spread (if present) is ignored. Missing values not allowed.
h	a scalar value in interval [0,1), specifying the h-level, which is the minimum degree of membership for each prediction in the model.

Details

The function input expects the response in form of a symmetric fuzzy number and the predictors as crisp numbers. The prediction returns symmetric triangular fuzzy number coefficients.

The h-level is a degree of fitting chosen by the decision maker.

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 = "plr".

References

Tanaka H., Hayashi I. and Watada J. (1989) Possibilistic linear regression analysis for fuzzy data. European Journal of Operational Research 40: 389-396.

See Also

fuzzylm

Examples

data(fuzzydat)
fuzzylm(y ~ x, fuzzydat$tan, "plr", , , "yl", "yr")

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