bsts_test: Test a BSTS_Model - Highest Function level from bstsTest...
In lissahyacinth/bstsTest: Rapidly Iterate & Test BSTS Models

Description Usage Arguments

Test a BSTS_Model - Highest Function level from bstsTest package

bsts_test(df = df, validate_n = 20, date_variable, response, na_fill = 0,
  nseasons = 7, trend_type, data_frequency, niter = 100, target_variable,
  group_variable, model.options = BstsOptions(), rebag_vars = FALSE,
  rebag_mean_vars = FALSE, inclusion_probability = 0.1)

`df`	Dataframe
`validate_n`	N Rows of the df will be used for cross valdiation
`date_variable`	Name of date variable column - will not accept lazy
`response`	Response variable name - will not accept lazy
`na_fill`	- Fill for NA rows - defaults to 0
`nseasons`	- Seasonality of data - defaults to 7 - also takes "auto" to detect seasons automatically
`trend_type`	- If using auto for nseasons, specify what type of seasonality exists [multiplicative/additive]
`data_frequency`	- If using auto for nseasons, specify the frequency of the data.
`niter`	- Iterations to run BSTS
`target_variable`	- Name of target variable column
`group_variable`	- Name of column to group by
`model.options`	- Any extra BSTS Options to add in using BstsOptions()
`rebag_vars`	- T/F - Sum all variables into a new predictor.
`rebag_mean_vars`	- T/F Take the mean of all variables into a new prediction.
`inclusion_probability`	Floor for including variables in output - doesn't affect model right now.