Formulas in T

Formulas provide a declarative way to specify statistical models, inspired by R.

Syntax

response ~ predictor

The ~ operator creates a Formula object that can be passed to modeling functions.

Examples

-- Simple linear regression
model = lm(data = df, formula = y ~ x)

-- Future: Multiple regression
model = lm(data = df, formula = y ~ x1 + x2 + x3)

Supported Functions

• lm() - Linear regression

lm() - Linear Regression

Fit a linear model using least squares.

Signature

lm(data: DataFrame, formula: Formula, ...) -> Dict

Arguments

• data: DataFrame containing the variables
• formula: Formula specifying the model (e.g., y ~ x)

Returns

Dictionary containing: - formula: The model formula - intercept: Estimated intercept - slope: Estimated slope (coefficient) - r_squared: R² statistic - residuals: Vector of residuals - n: Number of observations - response: Name of response variable - predictor: Name of predictor variable

Examples

data = read_csv("mtcars.csv")
model = lm(data = data, formula = mpg ~ hp)
print(model.r_squared)

Future Extensions

• Intercept control: y ~ x + 1 vs y ~ x - 1
• Interactions: y ~ x1 * x2
• Transformations: y ~ log(x)