fqr: Fuzzy Quantile Regression

Description Usage Arguments Author(s) Examples

Description

It gives the estimates of fuzzy quantile regression using the method of Weighted Least Absolute Deviation (WLAD). It converts the input variables into Linear Programming Problem (LPP) and uses the Simplex Algorithm to solve the LPP.

Usage

1
fqr(X,y_left,y_centre,y_right,t,type)

Arguments

t

is a spesified quantile ranges from 0 to 1 i.e t=[0,1]

type

spesifies the model (1 or 2)

X

is an input fuzzy number

y_left

is an left output fuzzy number

y_centre

is an centre of output fuzzy number

y_right

is an right output fuzzy number

1

"Fuzzy output, Fuzzy input and Fuzzy Parameters"

2

"Fuzzy output, Crisp input and Fuzzy Parameters"

Author(s)

Mohsin Shahzad

Examples

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If given Triangular Fuzzy NUmber

library ("lpSolve")

x_left<-c(1.5,3.0,4.5,6.5,8.0,9.5,10.5,12.0)
x_centre<-c(2.0,3.5,5.5,7.0,8.5,10.5,11.0,12.5)
x_right<-c(2.5,4.0,6.5,7.5,9.0,11.5,11.5,13.0)

y_left<-c(3.5,5.0,6.5,6.0,8.0,7.0,10.0,9.0)
y_centre<-c(4.0,5.5,7.5,6.5,8.5,8.0,10.5,9.5)
y_right<-c(4.5,6.0,8.5,7.0,9.0,9.0,11.0,10.0)

X<-cbind(x_left,x_centre,x_right)

t<-0.5
fqr(X,y_left,y_centre,y_right,t,type=1)

MohsinFuzzy/FuzzReg documentation built on May 25, 2019, 12:24 p.m.