fcr: Fuzzy Censored Regression

In MohsinFuzzy/FuzzReg: Fuzzy Regression

Description

It gives the estimates of Fuzzy Censored Regression by using the iterative based method of Newton Raphson.

Usage

fcr(Y,X,lower=c(L1,L2,L3),upper=c(U1,U2,U3),type)

Arguments

y	is an output fuzzy number
x	is an input fuzzy number
lower	is a set contains lower end censored observations of left, centre and right of triangular fuzzy number.Default lower limit is zero "0".
upper	is a set contains Upper end censored observations of left, centre and right of triangular fuzzy number.Default upper limit is infinity "Inf".
type	is model to be used (1 or 2)
1	"Fuzzy output, Fuzzy input and Fuzzy Parameters"
2	"Fuzzy output, Crisp input and Fuzzy Parameters"

Author(s)

Mohsin Shahzad

Examples

If given Triangular Fuzzy NUmber

library("VGAM")

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)
Y<-cbind(y_left,y_centre,y_right)

fcr(Y,X,lower=c(2.0,3.5,5.0),upper=c(8.5,9.0,10.0),type=1)

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