fls: Fuzzy Linear Regression using the Fuzzy Least Squares Method

In fuzzyreg: Fuzzy Linear Regression

Fuzzy Linear Regression using the Fuzzy Least Squares Method

Description

The function calculates fuzzy regression coeficients using the fuzzy least squares (FLS) method proposed by Diamond (1988) for non-symmetric triangular fuzzy numbers.

Usage

fls(x, y)

Arguments

x	two column matrix with the second column 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 FLS method for the fuzzy linear regression fits a simple model.

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 = "fls".

References

Diamond, P. (1988) Fuzzy least squares. Information Sciences 46(3): 141-157.

See Also

fuzzylm

Examples

   data(fuzzydat)
   x <- fuzzydat$dia[, 1, drop = FALSE]
   x <- cbind(rep(1, nrow(x)), x)
   y <- fuzzydat$dia[, c(2,3,3)]
   fls(x = x, y = y)

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