fci.ml {FuzzySTs}

Estimates a fuzzy confidence interval by the Likelihood method

Description

Estimates a fuzzy confidence interval by the Likelihood method

Usage

fci.ml(
  data.fuzzified,
  t,
  distribution,
  sig,
  mu = NA,
  sigma = NA,
  step = 0.05,
  margin = c(5, 5),
  breakpoints = 100,
  plot = TRUE
)

Arguments

data.fuzzified

a fuzzification matrix constructed by a call to the function FUZZ or the function GFUZZ, or a similar matrix. No NA are allowed.

t

a given numerical or fuzzy type parameter of the distribution.

distribution

a distribution chosen between "normal", "poisson", "Student" or "Logistic".

sig

a numerical value representing the significance level of the test.

mu

if the mean of the normal distribution is known, mu should be a numerical value. Otherwise, the argument mu is fixed to NA.

sigma

if the standard deviation of the normal distribution is known, sigma should be a numerical value. Otherwise, the argument sigma is fixed to NA.

step

a numerical value fixed to 0.05, defining the step of iterations on the interval [t-5; t+5].

margin

an optional numerical couple of values fixed to [5; 5], representing the range of calculations around the parameter t.

breakpoints

a positive arbitrary integer representing the number of breaks chosen to build the numerical alpha-cuts. It is fixed to 100 by default.

plot

fixed by default to "FALSE". plot="FALSE" if a plot of the fuzzy number is not required.

Value

Returns a matrix composed by 2 vectors representing the numerical left and right alpha-cuts. For this output, is.alphacuts = TRUE.

Examples

data <- matrix(c(1,2,3,2,2,1,1,3,1,2),ncol=1) 
MF111 <- TrapezoidalFuzzyNumber(0,1,1,2)
MF112 <- TrapezoidalFuzzyNumber(1,2,2,3)
MF113 <- TrapezoidalFuzzyNumber(2,3,3,4)
PA11 <- c(1,2,3)
data.fuzzified <- FUZZ(data,mi=1,si=1,PA=PA11)
Fmean <- Fuzzy.sample.mean(data.fuzzified)
fci.ml(data.fuzzified, t = Fmean, distribution = "normal", sig= 0.05, sigma = 0.62)