Nothing
### some further examples:
require(distrMod)
options("newDevice"=TRUE)
### Poisson Family
P <- PoisFamily(3)
# generate data
x <- r(P)(40)
MLEstimator(x,P)
#Evaluations of Maximum likelihood estimate:
#-------------------------------------------
#An object of class Estimate
#generated by call
# MLEstimator(x = x, ParamFamily = P)
#samplesize: 40
#estimate:
#
# 3.050000
# (0.276134)
#asymptotic (co)variance (multiplied with samplesize):
#[1] 3.05
#Criterion:
#negative log-likelihood
# 82.92266
MDEstimator(x,P)
#Evaluations of Minimum Kolmogorov distance estimate:
#----------------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = P)
#samplesize: 40
#estimate:
# lambda
#3.049777
#Criterion:
#Kolmogorov distance
# 0.08891945
MDEstimator(x,P, distance = CvMDist, asvar.fct = distrMod:::.CvMMDCovariance)
#Evaluations of Minimum CvM distance estimate:
#---------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = P, distance = CvMDist, asvar.fct = distrMod:::.CvMMDCovariance)
#samplesize: 40
#estimate:
# lambda
# 2.9561034
# (0.3855664)
#asymptotic (co)variance (multiplied with samplesize):
#[1] 5.946458
#Criterion:
#CvM distance
# 0.04909021
MDEstimator(x,P, distance = CvMDist, mu = Norm())
#Evaluations of Minimum CvM distance estimate:
#---------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = P, distance = CvMDist, mu = Norm())
#samplesize: 40
#estimate:
# lambda
#2.840035
#Criterion:
#CvM distance
# 0.02739709
MDEstimator(x,P, distance = TotalVarDist)
#Evaluations of Minimum total variation distance estimate:
#---------------------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = P, distance = TotalVarDist)
#samplesize: 40
#estimate:
# lambda
#2.987849
#Criterion:
#total variation distance
# 0.2543123
### Beta Family
B <- BetaFamily(2,4)
# generate data
x <- r(B)(40)
distroptions(DistrResolution = 1e-10)
MDEstimator(x, B, distance = TotalVarDist)
#Evaluations of Minimum total variation distance estimate:
#---------------------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = B, distance = TotalVarDist)
#samplesize: 40
#estimate:
# shape1 shape2
#3.843629 7.338333
#Criterion:
#total variation distance
# 0.7421599
MDEstimator(x, B)
#Evaluations of Minimum Kolmogorov distance estimate:
#----------------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = B)
#samplesize: 40
#estimate:
# shape1 shape2
#4.140942 8.612960
#Criterion:
#Kolmogorov distance
# 0.0881627
MDEstimator(x, B, distance = CvMDist, asvar.fct = distrMod:::.CvMMDCovariance)
#Evaluations of Minimum CvM distance estimate:
#---------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = B, distance = CvMDist, asvar.fct = distrMod:::.CvMMDCovariance)
#samplesize: 40
#estimate:
# shape1 shape2
# 4.362062 9.021663
# (1.175868) (2.501083)
#asymptotic (co)variance (multiplied with samplesize):
# shape1 shape2
#shape1 55.30661 105.8183
#shape2 105.81828 250.2166
#Criterion:
#CvM distance
# 0.03793965
(MLE<-MLEstimator(x, B))
#Evaluations of Maximum likelihood estimate:
#-------------------------------------------
#An object of class Estimate
#generated by call
# MLEstimator(x = x, ParamFamily = B)
#samplesize: 40
#estimate:
# shape1 shape2
# 3.8799534 8.3454158
# (0.8356662) (1.8607859)
#asymptotic (co)variance (multiplied with samplesize):
# shape1 shape2
#shape1 27.93352 56.60962
#shape2 56.60962 138.50097
#Criterion:
#negative log-likelihood
# -26.44365
confint(MLE)
#A[n] asymptotic (CLT-based) confidence interval:
# 2.5 % 97.5 %
#shape1 2.242078 5.517829
#shape2 4.698342 11.992489
#Type of estimator: Maximum likelihood estimate
#samplesize: 40
#Call by which estimate was produced:
#MLEstimator(x = x, ParamFamily = B)
### a new central distribution
my3d <- AbscontDistribution( d = function(x) exp(-abs(x)^3), withS = TRUE)
plot(my3d)
my3dF <- L2LocationScaleFamily(name = "my3dF",
centraldistribution = my3d)
plot(my3dF)
### generate some data out of the model
x <- r(my3dF)(40)*3+2
### evaluate the MLE:
MLEstimator(x,my3dF)
#Evaluations of Maximum likelihood estimate:
#-------------------------------------------
#An object of class Estimate
#generated by call
# MLEstimator(x = x, ParamFamily = my3dF)
#samplesize: 40
#estimate:
# loc scale
# 1.8536010 3.3710549
# (0.3060706) (0.3077495)
#asymptotic (co)variance (multiplied with samplesize):
# loc scale
#loc 3.747169 0.000000
#scale 0.000000 3.788389
#Criterion:
#negative log-likelihood
# 85.14204
(MDE <- MDEstimator(x = x, ParamFamily = my3dF, distance = CvMDist))
#Evaluations of Minimum CvM distance estimate:
#---------------------------------------------
#An object of class Estimate
#generated by call
# MDEstimator(x = x, ParamFamily = my3dF, distance = CvMDist)
#samplesize: 40
#estimate:
# loc scale
#1.991751 3.758789
#Criterion:
#CvM distance
# 0.03114585
#
MDE.asvar <- distrMod:::.CvMMDCovariance(my3dF,
param = ParamFamParameter(main= estimate(MDE),
.returnClsName = "ParamWithScaleFamParameter"),
expon = 2, withplot = TRUE)
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