Quick start with Fitting Fuzzy Linear Regression Models in R

require(fuzzyreg)

## Loading required package: fuzzyreg

## Warning: package 'fuzzyreg' was built under R version 4.1.3

data(fuzzydat)
fuzzydat$lee

##     x  y
## 1   1 21
## 2   2 20
## 3   3 30
## 4   4 26
## 5   5 31
## 6   6 37
## 7   7 35
## 8   8 40
## 9   9 49
## 10 10 45
## 11 11 53
## 12 12 35
## 13 13 52
## 14 14 60
## 15 15 62

 f = fuzzylm(y ~ x, data = fuzzydat$lee)
print(f)

## 
## Fuzzy linear model using the PLRLS method
## 
## Call:
## fuzzylm(formula = y ~ x, data = fuzzydat$lee)
## 
## Coefficients in form of non-symmetric triangular fuzzy numbers:
## 
##                center left.spread right.spread
## (Intercept) 17.761911   0.7619108    2.7380892
## x            2.746428   1.2464280    0.4202387

 plot(f, res = 20, col = "lightblue", main = "PLRLS")

 summary(f)

## 
## Central tendency of the fuzzy regression model:
## y = 17.7619 + 2.7464 * x
## 
## Lower boundary of the model support interval:
## y = 17 + 1.5 * x
## 
## Upper boundary of the model support interval:
## y = 20.5 + 3.1666 * x
## 
## The total error of fit: 126248409
## The mean squared distance between response and prediction: 262.1