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

In fuzzyreg: Fuzzy Linear Regression

Fuzzy Linear Regression Using the Possibilistic Linear Regression with Omission Method

Description

The function calculates fuzzy regression coeficients using the possibilistic linear regression with an outlier omission approach method (OPLR) developed by Hung and Yang (2006) that combines the least squares approach (fitting of a central tendency) with the possibilistic approach (fitting of spreads) when approximating an observed linear dependence by a fuzzy linear model.

Usage

oplr(x, y, h = 0)

Arguments

x	matrix with the independent variables observations. The first column is related to the intercept, so it consists of ones. Missing values not allowed.
y	two column matrix of the 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.

Details

The function input expects symmetric fuzzy response and crisp predictors. The prediction returns symmetric triangular fuzzy number coefficients. The OPLR method can detect one outlier in the data that is farther than 1.5 * IQR from either quartile.

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 = "oplr".

References

Hung, W.-L. and Yang, M.-S. (2006) An omission approach for detecting outliers in fuzzy regression models. Fuzzy Sets and Systems 157: 3109-3122.

See Also

fuzzylm

Examples

data(fuzzydat)
fuzzylm(y ~ x, fuzzydat$hun, "oplr", , , "yl")

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