ModelBuildMultivariateVAR: R6 class ModelBuildMultivariateVAR
In josephsdavid/tswgewrapped: Helpful wrappers for 'tswge', 'vars' and 'nnfor' time series packages
Description
R6 class ModelBuildMultivariateVAR
R6 class ModelBuildMultivariateVAR
Value
A dataframe containing the following columns
'Model': Name of the model
'Selection': The selection criteria used for K value (AIC or BIC)
'Trend': The trend argument used in the VARselect and VAR functions
'SlidingASE': Whether Sliding ASE will be used for this model
'Init_K': The K value recommended by the VARselect function
'Final_K': The adjusted K value to take into account the smaller batch size (only when using sliding_ase)
Methods
Public methods
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Method new()
Initialize an object to compare several Univatiate Time Series Models
Usage
ModelBuildMultivariateVAR$new(
data = NA,
var_interest = NA,
mdl_list,
alpha = 0.05,
verbose = 0,
...
)
Arguments
dataThe dataframe containing the time series realizations (data should not contain time index)
var_interestThe output variable of interest (dependent variable)
mdl_listA names list of all models (see format below)
alphaSignificance level to use for filtering of variables from the recommendations (Default = 0.05)
verboseHow much to print during the model building and other processes (Default = 0)
...Additional parameers to feed to VARSelect (if applicable) and VAR –> Most notably "exogen"
Returns
A new 'ModelCompareMultivariateVAR' object.
Method get_data()
Returns the time series realization
Usage
ModelBuildMultivariateVAR$get_data()
Returns
The Time Series Realization
Method get_var_interest()
Returns the dependent variable name
Usage
ModelBuildMultivariateVAR$get_var_interest()
Returns
The dependent variable name
Method get_data_var_interest()
Returns the dependent variable data only
Usage
ModelBuildMultivariateVAR$get_data_var_interest()
Returns
The dependent variable data only
Method set_verbose()
Adjust the verbosity level
Usage
ModelBuildMultivariateVAR$set_verbose(verbose = 0)
Arguments
verbose0 = Minimal Printing only (usualy limited to step being performed)
1 = Basic printing of model builds, etc.
2 = Reserved for debugging mode. May slow down the run due to excessive printing, especially when using batches
Method set_alpha()
Set the significance level to use for filtering of variables from the recommendations
Usage
ModelBuildMultivariateVAR$set_alpha(alpha = 0.05)
Arguments
alphaSignificance level to use (Default = 0.05)
Method summarize_build()
Returns the VAR model Build Summary
Usage
ModelBuildMultivariateVAR$summarize_build()
Method get_recommendations()
Returns a dataframe with recommended variables to use
for each VAR model along with its corresponding lag value
Usage
ModelBuildMultivariateVAR$get_recommendations()
Returns
A data frame with the recommendations
(1) Number of significant variables
(2) The names of the significant variables to use
(3) Lag value to use for the model
Method build_recommended_models()
Builds the models with the recommended lags and variables
Usage
ModelBuildMultivariateVAR$build_recommended_models()
Method get_final_models()
Returns a final models
Usage
ModelBuildMultivariateVAR$get_final_models(subset = "a", mdl_names = NA)
Arguments
subsetThe subset of models to get.
'a': All models (Default)
'u': Only User Defined Models
'r': Only the recommended models
mdl_namesVector of model names to get. This honors the subset variable.
Returns
A named list of models
Method add_models()
Add models to the existing object
Usage
ModelBuildMultivariateVAR$add_models(mdl_list, alpha = NA, ...)
Arguments
mdl_listThe list of new models to add
alphaSignificance level to use for filtering of variables from the recommendations (Default = 0.05)
...Additional parameers to feed to VARSelect (if applicable) and VAR –> Most notably "exogen"
Method remove_models()
Remove models from the object
Usage
ModelBuildMultivariateVAR$remove_models(mdl_names)
Arguments
mdl_namesA vector of the model names to remove.
Method keep_models()
Keep only the provided models
Usage
ModelBuildMultivariateVAR$keep_models(mdl_names)
Arguments
mdl_namesA vector of the model names to keep.
Method clone()
The objects of this class are cloneable with this method.
Usage
ModelBuildMultivariateVAR$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
josephsdavid/tswgewrapped documentation built on July 31, 2020, 9:36 a.m.
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Description
R6 class ModelBuildMultivariateVAR
R6 class ModelBuildMultivariateVAR
Value
A dataframe containing the following columns 'Model': Name of the model 'Selection': The selection criteria used for K value (AIC or BIC) 'Trend': The trend argument used in the VARselect and VAR functions 'SlidingASE': Whether Sliding ASE will be used for this model 'Init_K': The K value recommended by the VARselect function 'Final_K': The adjusted K value to take into account the smaller batch size (only when using sliding_ase)
Methods
Public methods
Method new()
Initialize an object to compare several Univatiate Time Series Models
Usage
ModelBuildMultivariateVAR$new( data = NA, var_interest = NA, mdl_list, alpha = 0.05, verbose = 0, ... )
Arguments
dataThe dataframe containing the time series realizations (data should not contain time index)
var_interestThe output variable of interest (dependent variable)
mdl_listA names list of all models (see format below)
alphaSignificance level to use for filtering of variables from the recommendations (Default = 0.05)
verboseHow much to print during the model building and other processes (Default = 0)
...Additional parameers to feed to VARSelect (if applicable) and VAR –> Most notably "exogen"
Returns
A new 'ModelCompareMultivariateVAR' object.
Method get_data()
Returns the time series realization
Usage
ModelBuildMultivariateVAR$get_data()
Returns
The Time Series Realization
Method get_var_interest()
Returns the dependent variable name
Usage
ModelBuildMultivariateVAR$get_var_interest()
Returns
The dependent variable name
Method get_data_var_interest()
Returns the dependent variable data only
Usage
ModelBuildMultivariateVAR$get_data_var_interest()
Returns
The dependent variable data only
Method set_verbose()
Adjust the verbosity level
Usage
ModelBuildMultivariateVAR$set_verbose(verbose = 0)
Arguments
verbose0 = Minimal Printing only (usualy limited to step being performed) 1 = Basic printing of model builds, etc. 2 = Reserved for debugging mode. May slow down the run due to excessive printing, especially when using batches
Method set_alpha()
Set the significance level to use for filtering of variables from the recommendations
Usage
ModelBuildMultivariateVAR$set_alpha(alpha = 0.05)
Arguments
alphaSignificance level to use (Default = 0.05)
Method summarize_build()
Returns the VAR model Build Summary
Usage
ModelBuildMultivariateVAR$summarize_build()
Method get_recommendations()
Returns a dataframe with recommended variables to use for each VAR model along with its corresponding lag value
Usage
ModelBuildMultivariateVAR$get_recommendations()
Returns
A data frame with the recommendations (1) Number of significant variables (2) The names of the significant variables to use (3) Lag value to use for the model
Method build_recommended_models()
Builds the models with the recommended lags and variables
Usage
ModelBuildMultivariateVAR$build_recommended_models()
Method get_final_models()
Returns a final models
Usage
ModelBuildMultivariateVAR$get_final_models(subset = "a", mdl_names = NA)
Arguments
subsetThe subset of models to get. 'a': All models (Default) 'u': Only User Defined Models 'r': Only the recommended models
mdl_namesVector of model names to get. This honors the subset variable.
Returns
A named list of models
Method add_models()
Add models to the existing object
Usage
ModelBuildMultivariateVAR$add_models(mdl_list, alpha = NA, ...)
Arguments
mdl_listThe list of new models to add
alphaSignificance level to use for filtering of variables from the recommendations (Default = 0.05)
...Additional parameers to feed to VARSelect (if applicable) and VAR –> Most notably "exogen"
Method remove_models()
Remove models from the object
Usage
ModelBuildMultivariateVAR$remove_models(mdl_names)
Arguments
mdl_namesA vector of the model names to remove.
Method keep_models()
Keep only the provided models
Usage
ModelBuildMultivariateVAR$keep_models(mdl_names)
Arguments
mdl_namesA vector of the model names to keep.
Method clone()
The objects of this class are cloneable with this method.
Usage
ModelBuildMultivariateVAR$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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