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.
oplr(x, y, h = 0)Returns a fuzzylm object that includes the model coefficients, limits
for data predictions from the model and the input data.
matrix with the independent variables observations. The first column is related to the intercept, so it consists of ones. Missing values not allowed.
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.
a scalar value in interval [0,1], specifying the h-level.
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.
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.
fuzzylm
data(fuzzydat)
fuzzylm(y ~ x, fuzzydat$hun, "oplr", , , "yl")
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