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Mixture autoregressive models

Objects from the Class

A virtual Class: no objects can be created from it.

Derived classes add distribution properties, e.g. use class "MixARGaussian" for MixAR models with Gaussian error components.

Slots

prob:

the mixing probabilities, "numeric".

order:

the AR orders, "numeric".

shift:

intercept terms, "numeric".

scale:

scaling factor, "numeric".

arcoef:

autoregressive coefficients, an object from class "raggedCoef" containing one row for each mixture component.

Methods

fit_mixAR

signature(x = "ANY", model = "MixAR", init = "list"): ...

fit_mixAR

signature(x = "ANY", model = "MixAR", init = "missing"): ...

fit_mixAR

signature(x = "ANY", model = "MixAR", init = "MixAR"): ...

fit_mixAR

signature(x = "ANY", model = "MixAR", init = "numeric"): ...

fit_mixAR

signature(x = "ANY", model = "MixARGaussian", init = "MixAR"): ...

get_edist

signature(model = "MixAR"): ...

initialize

signature(.Object = "MixAR"): ...

lik_params

signature(model = "MixAR"): ...

make_fcond_lik

signature(model = "MixAR", ts = "numeric"): ...

mix_ek

signature(model = "MixAR", x = "numeric", index = "numeric", xcond = "missing", scale = "missing"): ...

mix_ek

signature(model = "MixAR", x = "numeric", index = "numeric", xcond = "missing", scale = "logical"): ...

mix_ek

signature(model = "MixAR", x = "numeric", index = "missing", xcond = "numeric", scale = "missing"): ...

mix_ek

signature(model = "MixAR", x = "numeric", index = "missing", xcond = "numeric", scale = "logical"): ...

mix_hatk

signature(model = "MixAR", x = "numeric", index = "numeric", xcond = "missing"): ...

mix_ncomp

signature(x = "MixAR"): ...

mixAR

signature(template = "MixAR"): ...

noise_dist

signature(model = "MixAR"): ...

noise_params

signature(model = "MixAR"): ...

noise_rand

signature(model = "MixAR"): ...

parameters

signature(model = "MixAR"): ...

row_lengths

signature(x = "MixAR"): ...

Author

Georgi N. Boshnakov

See also

codemixAR, classes "MixARGaussian", "MixARgen"

Examples

## some models from subclasses of (virtual) class "MixAR"
names(exampleModels)
#>  [1] "WL_ibm"     "WL_A"       "WL_B"       "WL_I"       "WL_II"     
#>  [6] "WL_ibm_gen" "WL_ibm_t3v" "WL_ibm_tf"  "WL_At"      "WL_Bt_1"   
#> [11] "WL_Bt_2"    "WL_Bt_3"    "WL_Ct_1"    "WL_Ct_2"    "WL_Ct_3"   
exampleModels$WL_A
#> An object of class "MixARGaussian"
#> Number of components: 2 
#>        prob shift scale order ar_1
#> Comp_1 0.5    0     5     1   0.5 
#> Comp_2 0.5    0     1     1   1.1 
#> 
#> Distributions of the error components:
#> 	standard Gaussian
#> 
exampleModels$WL_At
#> An object of class "MixARgen"
#> Number of components: 2 
#>        prob shift scale order ar_1
#> Comp_1 0.5    0     1     1   -0.5
#> Comp_2 0.5    0     2     1    1.1
#> 
#> Distributions of the error components:
#> 	Component 1: Student t with 4 df
#> 	Component 2: Student t with 8 df
#> 

## modify an existing model, here change the mixture weights
mixAR(exampleModels$WL_A, coef = list((prob = c(0.4, 0.6))))
#> An object of class "MixARGaussian"
#> Number of components: 2 
#>        prob shift scale order ar_1
#> Comp_1 0.5    0     5     1   0.5 
#> Comp_2 0.5    0     1     1   1.1 
#> 
#> Distributions of the error components:
#> 	standard Gaussian
#>