fls {fuzzyreg}

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)