AutovarCore

AutovarCore generates networks given a data frame. AutovarCore is a simplified/efficient version of Autovar.

To install, type the following:

install.packages('devtools')
devtools::install_github('roqua/autovarCore')

For more information on Autovar, see autovar.nl.

Should I use Autovar or AutovarCore?

You should use Autovar if you

• Prefer a slightly better model fit over a model with less outlier dummies (less outlier dummies means that the model explains more of the measurements).
• Are okay with Autovar sometimes returning NULL because it could not find any models that passed all residual tests.
• Need VAR models with more than one lag or with zero lags.
• Need models with automatically determined restrictions.
• Need debugging information such as a full list of all evaluated models.
• Want detailed summary information such as a plot of contemporaneous correlations or Granger causalities.
• Need named dummy variables for interpretation (e.g., “morning”, “afternoon”, “Monday”, “Tuesday” instead of “day_part_1”, “day_part_1”, “day_3”, “day_4”)

You should use AutovarCore if you

• Prefer a model with less outlier dummies over a model with a slightly better model fit (less outlier dummies means that the model explains more of the measurements).
• Always want a list of best models even if those do not pass all residual tests at the default p-level (this is indicated by the 'bucket' property, see ?autovar for details).
• Are not interested in any models except for models with lag 1 and models with lag 2 where the second lag is autoregressive only.
• May have missing data (i.e., NA values). Autovar also has a function “impute_dataframe” to impute values, but AutovarCore does this automatically (if needed).
• Need more flexibility as to which residual tests should constitute model validity (e.g., portmanteau, portmanteau_squared, skewness, kurtosis, joint_sktest). Autovar uses a fixed set of residual tests.
• Deem performance to be an issue and prefer memory-efficient and fast code.

Example use

library('autovarCore')

# 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.)
library('foreign')

# This example data set can be downloaded from
# https://autovar.nl/datasets/aug_pp5_da.sav
suppressWarnings(dfile <- 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]])

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 Networks
• compete
  Returns the winning model
• day_dummies
  Calculate day 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
• rcpp_hello_world
  Simple function using Rcpp
• 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
• 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