Description Usage Arguments Details Value Note Author(s) See Also Examples
This function calculates the M-estimator of scale with loss function given in M
for a matrix of trapezoidal fuzzy numbers F
. For computing the M-estimator, a method called “iterative reweighting” is used. The employed metric in the M-equation can be the 1-norm distance, the mid/spr distance or the (\varphi,θ)-wabl/ldev/rdev distance. The function first checks if the input matrix F
is given in the correct form (tested by checkingTra
).
1 | M.estimate(F, M, est_initial, delta, epsilon, type, a = 1, b = 1, theta = 1/3)
|
F |
matrix of dimension |
M |
name of the loss function. It can be “Huber”, “Tukey” or “Cauchy”. |
est_initial |
initial scale estimate. |
delta |
number in (0,1). It is present in the M-equation. |
epsilon |
number >0. It is the tolerance allowed in the algorithm. |
type |
number 1, 2 or 3: if |
a |
number >0, by default |
b |
number >0, by default |
theta |
number >0, by default |
See examples
The function returns the value of the M-estimator of scale, which is a real number.
In case you find (almost surely existing) bugs or have recommendations for improving the functions comments are welcome to the above mentioned mail addresses.
Asun Lubiano <lubiano@uniovi.es>, Sara de la Rosa de Saa <rosasara@uniovi.es>
checkingTra
, Rho1Tra
, DthetaphiTra
, DwablphiTra
1 2 3 4 5 6 7 | # Example 1:
F=SimulCASE1(100)
U=Median1norm(F)
est_initial=MDD(F,U,1)
delta=0.5
epsilon=10^(-5)
M.estimate(F,"Huber",est_initial,delta,epsilon,1)
|
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