Class "MixAR" — mixture autoregressive models
MixAR-class.RdMixture 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"): ...
See also
mixAR,
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
#>