scicloj.metamorph.ml.r-model-matrix — org.scicloj/metamorph.ml 1.7.0

R-style formula-based feature engineering and linear regression.

This namespace provides tools to leverage R's powerful formula syntax for feature engineering and linear modeling within Clojure. R formulas enable expressive specification of interactions, transformations, and categorical expansions without manual column manipulation.

Key Functions:

• r-model-matrix: Convert dataset + R formula to design matrix
• lm: Simplified linear regression using R formulas

Implementation Backends: The namespace supports multiple R execution backends:

• :ocpu Remote R via OpenCPU (cloud.opencpu.org) - no local R needed
• :renjin Java-based R implementation (https://renjin.org/)
• :clojisr Local R via clojisr (requires R installation)

Model Matrix Capabilities: R formulas handle:

• Basic features: y ~ x1 + x2
• Interactions: y ~ x1 * x2 (expands to x1 + x2 + x1:x2)
• Polynomial terms: y ~ x + I(x^2)
• Categorical encoding: Automatic dummy variable creation
• Intercept control: y ~ x - 1 (remove intercept)
• Exclusions: y ~ . - x3 (all columns except x3)

Linear Regression (lm): Combines formula-based feature engineering with OLS regression training. Returns a ready-to-use trained model for predictions.

Notes:

• OpenCPU backend is convenient but requires internet connectivity
• Renjin is standalone but may have some R incompatibilities
• clojisr requires a local R installation but offers full R compatibility
• Returned model matrices exclude row names and intercept columns by default

See also: scicloj.metamorph.ml.design-matrix for Clojure-native feature engineering

lm clj

(lm ds formula target-var formula-impl)

Train a linear model using an R-style formula.

This function combines R formula-based feature engineering with ordinary least squares (OLS) regression. It creates a design matrix from the input dataset using the specified R formula, then trains a linear model on the resulting features.

Parameters:

• ds A tech.ml.dataset dataset containing the input data with all variables referenced in the formula and target variable.

• formula A string containing the R formula (e.g., "y ~ x1 + x2 * x3"). The formula is interpreted by the R backend.

• target-var A keyword or string naming the target variable for regression. This variable must be present in the input dataset.

• formula-impl An implementation keyword for formula evaluation:

  • :ocpu Uses OpenCPU (cloud.opencpu.org), no local R needed
  • :renjin Uses Renjin, a Java implementation of R
  • :clojisr Uses clojisr with local R installation

Requires setup of dependencies of teh engine, see: r-model-matrix

Returns: A trained linear model (OLS from fastmath) ready for predictions. The model excludes the intercept column and row names from the design matrix by default.

model clj

r-model-matrix clj

(r-model-matrix dataset r-formula impl)

Compute a model matrix from a dataset and an R-style formula.

Parameters:

• ds A tech.ml.dataset dataset representing the input data.

• r-formula A string containing the R formula to use for model matrix construction. The formua is interpreted by R itself, so should be full compatible

• impl An implementation keyword, either

  • :ocpu Uses an online service https://www.opencpu.org/api.html (server: cloud.opencpu.org)
  • :renjine Uses https://renjin.org/
  • :clojisr Uses https://github.com/scicloj/clojisr, which requires a local R installation

Each implementation requires dependencies to be added:

• :ocpu : [opencpu-clj/opencpu-clj "0.3.1"]
• :renjin : [org.renjin/renjin-script-engine "3.5-beta76"]
• :clojisr : [scicloj/clojisr "1.1.0"]

Returns a dataset containing the constructed design matrix. If ds contains target columns, they are added to the returned dataset.

Dispatches to the appropriate backend implementation.

Returns a map with

• :model-matrix-dataset having the TMD containing the design matrix specified by r-formula
• :attributes the (R) attributes of the model.matrix object