fuzzylm {fuzzyreg}

Fuzzy Linear Regression

Description

A wrapper function that calculates fuzzy regression coeficients using a chosen method.

Usage

fuzzylm(
  formula,
  data,
  method = "plrls",
  fuzzy.left.x = NULL,
  fuzzy.right.x = NULL,
  fuzzy.left.y = NULL,
  fuzzy.right.y = NULL,
  silent = FALSE,
  ...
)

Arguments

formula

a model formula.

data

a data.frame, containing the variables used in formula.

method

method for fitting of the fuzzy linear model.

fuzzy.left.x

character string vector specifying column name(s) with the left spread of the fuzzy independent variable(s).

fuzzy.right.x

character string vector specifying column name(s) with the right spread of the fuzzy independent variable(s).

fuzzy.left.y

character string vector specifying column name(s) with the left spread of the fuzzy dependent variable.

fuzzy.right.y

character string vector specifying column name(s) with the right spread of the fuzzy dependent variable.

silent

logical whether warnings should be printed.

...

additional parameters used by specific methods, check functions moflr, oplr, plr, and plrls for full list of optional method-specific arguments.

Details

The implemented methods include plrls (Lee and Tanaka 1999) and bfrl (Skrabanek et al. 2021) for fitting the fuzzy linear regression from the crisp input data, and fls (Diamond 1988), oplr (Hung and Yang 2006), moflr (Nasrabadi et al. 2005) and plr (Tanaka et al. 1989) methods for triangular fuzzy numbers.

Value

Returns a fuzzylm object that includes the model coefficients, limits for data predictions from the model and the input data.

References

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

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.

Lee, H. and Tanaka, H. (1999) Fuzzy approximations with non-symmetric fuzzy parameters in fuzzy regression analysis. Journal of the Operations Research Society Japan 42: 98-112.

Nasrabadi, M. M., Nasrabadi, E. and Nasrabady, A. R. (2005) Fuzzy linear regression analysis: a multi-objective programming approach. Applied Mathematics and Computation 163: 245-251.

Skrabanek, P., Marek, J. and Pozdilkova, A. (2021) Boscovich Fuzzy Regression Line. Mathematics 9: 685.

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

Zeng, W., Feng, Q. and Li, J. (2017) Fuzzy least absolute linear regression. Applied Soft Computing 52: 1009-1019.

See Also

plot, predict, summary

Examples

data(fuzzydat)
fuzzylm(y ~ x, data = fuzzydat$lee, method = "plrls")
## Not run: 
# returns an error due to the incorrect number of spreads
fuzzylm(y ~ x, data = fuzzydat$dia, method = "fls", fuzzy.left.y = "yl")

## End(Not run)
# use the same column name for the left and right spread, when the method requests 
# non-symmetric fuzzy numbers, but the data specify symmetric fuzzy numbers 
fuzzylm(y ~ x, data = fuzzydat$dia, method = "fls", fuzzy.left.y = "yl", fuzzy.right.y = "yl")