autovarCore-package {autovarCore}R Documentation

Automated Vector Autoregression Models and Networks

Description

Automatically find the best vector autoregression models and networks for a given time series data set. 'AutovarCore' evaluates eight kinds of models: models with and without log transforming the data, lag 1 and lag 2 models, and models with and without weekday dummy variables. For each of these 8 model configurations, 'AutovarCore' evaluates all possible combinations for including outlier dummies (at 2.5x the standard deviation of the residuals) and retains the best model. Model evaluation includes the Eigenvalue stability test and a configurable set of residual tests. These eight models are further reduced to four models because 'AutovarCore' determines whether adding weekday dummies improves the model fit.

Details

The DESCRIPTION file:

Package: autovarCore
Type: Package
Title: Automated Vector Autoregression Models and Networks
Version: 1.0-4
Date: 2018-06-04
Authors@R: c(person("Ando","Emerencia",role = c("aut","cre"), email = "ando.emerencia@gmail.com"))
BugReports: https://github.com/roqua/autovarcore/issues
Maintainer: Ando Emerencia <ando.emerencia@gmail.com>
Description: Automatically find the best vector autoregression models and networks for a given time series data set. 'AutovarCore' evaluates eight kinds of models: models with and without log transforming the data, lag 1 and lag 2 models, and models with and without weekday dummy variables. For each of these 8 model configurations, 'AutovarCore' evaluates all possible combinations for including outlier dummies (at 2.5x the standard deviation of the residuals) and retains the best model. Model evaluation includes the Eigenvalue stability test and a configurable set of residual tests. These eight models are further reduced to four models because 'AutovarCore' determines whether adding weekday dummies improves the model fit.
License: MIT + file LICENSE
Suggests: testthat, roxygen2
Imports: Rcpp (>= 0.11.4), Amelia, jsonlite, parallel, stats, urca, vars
LinkingTo: Rcpp
RoxygenNote: 6.0.1
Author: Ando Emerencia [aut, cre]

Index of help topics:

apply_ln_transformation
                        Applies the natural logarithm to the data set
assess_joint_sktest     Tests the skewness and kurtosis of a VAR model
assess_kurtosis         Tests the kurtosis of a VAR model
assess_portmanteau      Tests the white noise assumption for a VAR
                        model using a portmanteau test on the residuals
assess_portmanteau_squared
                        Tests the homeskedasticity assumption for a VAR
                        model using a portmanteau test on the squared
                        residuals
assess_skewness         Tests the skewness of a VAR model
autovar                 Return the best VAR models found for a time
                        series data set
autovarCore-package     Automated Vector Autoregression Models and
                        Networks
coefficients_of_kurtosis
                        Kurtosis coefficients.
coefficients_of_skewness
                        Skewness coefficients.
compete                 Returns the winning model
day_dummies             Calculate weekday dummy variables
daypart_dummies         Calculate day-part dummy variables
explode_dummies         Explode dummies columns into separate dummy
                        variables
impute_datamatrix       Imputes the missing values in the input data
invalid_mask            Calculate a bit mask to identify invalid
                        outlier dummies
model_is_stable         Eigenvalue stability condition checking
model_score             Return the model fit for the given varest model
needs_trend             Determines if a trend is required for the
                        specified VAR model
outliers_column         Determine the outliers column for the given
                        column data
portmanteau_test_statistics
                        An implementation of the portmanteau test.
print_correlation_matrix
                        Print the correlation matrix of the residuals
                        of a model annotated with p-values
residual_outliers       Calculate dummy variables to mask residual
                        outliers
run_tests               Execute a series of model validity assumptions
run_var                 Calculate the VAR model and apply restrictions
select_valid_masks      Select and return valid dummy outlier masks
selected_columns        Convert an outlier_mask to a vector of column
                        indices
significance_from_pearson_coef
                        Calculate the significance of a Pearson
                        correlation coefficient
sktest_joint_p          SK test p-level
trend_columns           Construct linear and quadratic trend columns
validate_params         Validates the params given to the autovar
                        function
validate_raw_dataframe
                        Validates the dataframe given to the autovar
                        function

Please see the help of the autovar function for information on how to use this package.

Author(s)

NA

Maintainer: Ando Emerencia <ando.emerencia@gmail.com>

References

Emerencia, A. C., L. van der Krieke, E. H. Bos, P. de Jonge, N. Petkov and M. Aiello (2016), Automating Vector Autoregression on Electronic Patient Diary Data, IEEE Journal of Biomedical and Health Informatics, 20(2): 631-643, https://doi.org/10.1109/JBHI.2015.2402280

See Also

autovar

Examples

## Not run: 
# AutovarCore requires input data in data.frame format.
# If you have data in a .csv, .dta, or .sav file, use
# the 'foreign' library to load this data into R first.
# (You may need to type:
#    install.packages('foreign')
#  if you do not have the foreign library installed on
#  your system.)

# This example data set can be downloaded from
# https://autovar.nl/datasets/aug_pp5_da.sav
suppressWarnings(dfile <- foreign::read.spss('~/Downloads/aug_pp5_da.sav'))
dframe <- data.frame(Activity = dfile$Activity, Depression = dfile$Depression)

# Call autovar with the given data frame. Type:
#   ?autovar
# (after having typed "library('autovarCore')") to see
# which other options are available.
models_found <- autovar(dframe, selected_column_names = c('Activity', 'Depression'))

# Show details for the best model found
print(models_found[[1]])

## End(Not run)

[Package autovarCore version 1.0-4 Index]