Load custom data from inst/extdata or a user-specified path

Description

This function loads custom data from the inst/extdata directory of the package or a designated path provided by the user.

Usage

load_custom_data(
  data = "crudeoildata.csv",
  custom_path = NULL,
  exclude_first_column = FALSE
)

Arguments

Format

A data frame with 55 weekly observations and 19 columns, including headers. The first column represents the date in the format MM/DD/YYYY, while all other columns display weekly percentage changes with five-decimal precision.

Value

A data frame containing the loaded data.

Source

Example dataset compiled from the following public sources: - Crude oil supply/demand metrics (FieldProduction, RefinerNetInput, OperableCapacity, Imports, StocksExcludingSPR): https://www.eia.gov - API gravity and Rig Count: Extracted from industry reports - S&P 500 Index (SPX): https://fred.stlouisfed.org/series/SP500 - CFTC positioning data (NonCommercialLong, CommercialShort, OpenInterest, etc.): https://www.cftc.gov (NYMEX short format) - TotalLong and TotalShort = NonCommercial (L/S) + Spread (L/S) + Commercial (L/S)

Examples

## Not run: 
# Load custom data with default options
df <- load_custom_data()

# Load data from a custom file path and exclude the first column (e.g., a Date column)
df <- load_custom_data(
  data = "crudeoildata.csv",
  custom_path = "path/to/custom/crudeoildata.csv",
  exclude_first_column = TRUE
)

# Load default custom data and exclude the first column
df <- load_custom_data(exclude_first_column = TRUE)

## End(Not run)

olr: Optimal Linear Regression

Description

The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictors—regardless of relevance—potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective model—based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome.

Usage

olr(dataset, responseName = NULL, predictorNames = NULL, adjr2 = TRUE)

olrmodels(dataset, responseName = NULL, predictorNames = NULL)

olrformulas(dataset, responseName = NULL, predictorNames = NULL)

olrformulasorder(dataset, responseName = NULL, predictorNames = NULL)

adjr2list(dataset, responseName = NULL, predictorNames = NULL)

r2list(dataset, responseName = NULL, predictorNames = NULL)

Arguments

Details

Complementary functions below follow the format: function(dataset, responseName = NULL, predictorNames = NULL)

olrmodels: Returns the list of all evaluated models. Use summary(olrmodels(dataset, responseName, predictorNames)[, x]) to inspect a specific model, where x is the model index.

olrformulas: Returns the list of all regression formulas generated by olr(), each representing a unique combination of specified predictor variables regressed on the dependent variable, in the order created.

olrformulasorder: Returns the same set of regression formulas as olrformulas, but sorted alphabetically by variable names within each formula. This helps users more easily locate or compare specific combinations of predictors.

adjr2list: Returns adjusted R-squared values for all models.

r2list: Returns R-squared values for all models.

Tip: To avoid errors from non-numeric columns (e.g., dates), remove them using dataset <- dataset[, -1]. Or use load_custom_data(..., exclude_first_column = TRUE).

When responseName and predictorNames are NULL, the function will treat the first column of the dataset as the response variable and all remaining columns as predictors. If the first column contains non-numeric or irrelevant data (e.g., a Date column), you must exclude it manually: dataset <- crudeoildata[, -1].

Otherwise, you can utilize load_custom_data(data = "crudeoildata.csv", custom_path = NULL, exclude_first_column = TRUE), a custom function that allows you to load the data (crudeoildata) automatically without the first column.

Value

Returns the best-fitting linear model object based on either adjusted R-squared (default) or R-squared. Call summary() on the result to view full regression statistics.

Examples

# Please allow time for rendering after clicking "Run Examples"
crudeoildata <- read.csv(system.file("extdata", "crudeoildata.csv", package = "olr"))
dataset <- crudeoildata[, -1]

responseName <- 'CrudeOil'
predictorNames <- c('RigCount', 'API', 'FieldProduction', 'RefinerNetInput',
  'OperableCapacity', 'Imports', 'StocksExcludingSPR', 'NonCommercialLong',
  'NonCommercialShort', 'CommercialLong', 'CommercialShort', 'OpenInterest')

olr(dataset, responseName, predictorNames, adjr2 = TRUE)