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Boilerplate Workflow

Source: R/boilerplate-lm.R
ts_auto_lm.Rd

This is a boilerplate function to create automatically the following:

  • recipe

  • model specification

  • workflow

  • calibration tibble and plot

Usage

ts_auto_lm(
  .data,
  .date_col,
  .value_col,
  .formula,
  .rsamp_obj,
  .prefix = "ts_lm",
  .bootstrap_final = FALSE
)

Arguments

.data

The data being passed to the function. The time-series object.

.date_col

The column that holds the datetime.

.value_col

The column that has the value

.formula

The formula that is passed to the recipe like value ~ .

.rsamp_obj

The rsample splits object

.prefix

Default is ts_lm

.bootstrap_final

Not yet implemented.

Value

A list

Details

This uses parsnip::linear_reg() and sets the engine to lm

See also

https://parsnip.tidymodels.org/reference/linear_reg.html

Other Boiler_Plate: ts_auto_arima(), ts_auto_arima_xgboost(), ts_auto_croston(), ts_auto_exp_smoothing(), ts_auto_glmnet(), ts_auto_mars(), ts_auto_nnetar(), ts_auto_prophet_boost(), ts_auto_prophet_reg(), ts_auto_smooth_es(), ts_auto_svm_poly(), ts_auto_svm_rbf(), ts_auto_theta(), ts_auto_xgboost()

Author

Steven P. Sanderson II, MPH

Examples

# \donttest{
library(dplyr)
library(timetk)
library(modeltime)

data <- AirPassengers %>%
  ts_to_tbl() %>%
  select(-index)

splits <- time_series_split(
  data
  , date_col
  , assess = 12
  , skip = 3
  , cumulative = TRUE
)

ts_lm <- ts_auto_lm(
  .data = data,
  .date_col = date_col,
  .value_col = value,
  .rsamp_obj = splits,
  .formula = value ~ .,
)
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `.nested.col = purrr::map2(...)`.
#> Caused by warning in `predict.lm()`:
#> ! prediction from rank-deficient fit; consider predict(., rankdeficient="NA")
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `.nested.col = purrr::map2(...)`.
#> Caused by warning in `predict.lm()`:
#> ! prediction from rank-deficient fit; consider predict(., rankdeficient="NA")

ts_lm$recipe_info
#> $recipe_call
#> recipe(.data = data, .date_col = date_col, .value_col = value, 
#>     .formula = value ~ ., .rsamp_obj = splits)
#> 
#> $recipe_syntax
#> [1] "ts_lm_recipe <-"                                                                                                    
#> [2] "\n  recipe(.data = data, .date_col = date_col, .value_col = value, .formula = value ~ \n    ., .rsamp_obj = splits)"
#> 
#> $rec_obj
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 1
#> 
#> ── Operations 
#> • Timeseries signature features from: date_col
#> • Novel factor level assignment for: recipes::all_nominal_predictors()
#> • Variable mutation for: tidyselect::vars_select_helpers$where(is.character)
#> • Variable mutation for: as.numeric(^date_col)
#> • Dummy variables from: recipes::all_nominal()
#> • Sparse, unbalanced variable filter on: recipes::all_predictors(), ...
#> • Centering and scaling for: recipes::all_numeric_predictors(), ...
#> • Linear combination filter on: recipes::all_numeric_predictors()
#> 
# }