plrls | R Documentation |
The function calculates fuzzy regression coeficients using the possibilistic linear regression with least squares method developed by Lee and Tanaka (1999) 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.
plrls(x, y, h = 0, k1 = 1, k2 = 1, epsilon = 1e-05)
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 |
one column matrix of dependent variable values, missing values not allowed. |
h |
a scalar value in interval |
k1 |
weight coefficient for the centeral tendency. |
k2 |
weight coefficient for the spreads. |
epsilon |
small positive number that supports search for the optimal solution. |
The function input expects crisp numbers of both the explanatory and response variables, and the prediction returns non-symmetric triangular fuzzy number coefficients.
The h-level is a degree of fitting chosen by the decision maker.
Returns a fuzzylm
object that includes the model coefficients, limits
for data predictions from the model and the input data.
Preferred use is through the fuzzylm
wrapper function with argument
method = "plrls"
.
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.
fuzzylm
x <- matrix(c(rep(1, 15), rep(1:3, each = 5)), ncol = 2)
y <- matrix(c(rnorm(5, 1), rnorm(5, 2), rnorm(5, 3)), ncol = 1)
plrls(x = x, y = y)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.