1 User guide

9.3

Racket

1 User guide🔗ℹ

To get started we will walk through an example of estimating a model from data and unpacking the results.

There are several worked Examples if you prefer to jump to the specific model type you are interested in.

You can also evaluate the examples in this guide and their source code which you can view "glmnet/examples" in the main repository on GitHub.

To find the documentation for a specific definition or procedure, see the Reference.

1.1 Getting started

1.1.1 Installing

1.1.2 A First Regression

1.1.2.1 Plotting the fit and path

1.1.2.1.1 Predicted versus actual values

1.1.2.1.2 Residuals versus fitted values

1.1.2.1.3 The lasso coefficient path

1.1.3 Fitting from named columns

1.1.4 Choosing a model

1.1.5 Predicting

1.1.6 What is not there yet

1.2 Concepts

1.2.1 Data layout

1.2.1.1 Named data

1.2.2 The penalty: α and λ

1.2.3 Model families

1.2.4 Results

1.2.5 Regularization paths

1.2.6 Predictions and coefficients

1.2.6.1 What is predicted

1.2.6.2 Predicting along a path

1.2.7 Choosing λ by cross-validation

1.2.7.1 Reading lambda-min and lambda-1se

1.2.7.2 Predicting at the chosen λ

1.2.7.3 Folds

1.2.7.4 Measures of error

1.2.8 Standardization and the intercept

1.2.9 Convergence and errors

1.2.10 The native library

1.3 Data

1.3.1 Example datasets

1.3.2 Tables

1.3.3 Lists and vectors

1.3.4 CSV files

1.3.5 Matrices from math/matrix

1.3.6 Polars dataframes

1.4 Formulas and named data

1.4.1 Tables

1.4.2 Formulas

1.4.3 A Gaussian fit

1.4.4 The formula algebra

1.4.4.1 Interactions and crossing

1.4.4.2 Powers, and R’s x^2

1.4.4.3 Removing terms

1.4.4.4 The intercept

1.4.4.5 Infix formulas

1.4.4.6 The response on the right-hand side

1.4.4.7 Predicting from new data

1.4.4.8 From R’s syntax

1.4.5 Transforms

1.4.5.1 Squares, and R’s I()

1.4.5.2 Names in a transform

1.4.5.3 Values and new data

1.4.5.4 Transforms as data

1.4.5.5 From R’s transforms

1.4.6 Categorical predictors

1.4.6.1 Which columns are factors

1.4.6.2 Levels and baselines

1.4.6.3 Treatment contrasts

1.4.6.4 Factors in interactions

1.4.6.5 Transforms that make factors

1.4.6.6 New data and levels

1.4.6.7 Categorical responses

1.4.6.8 From R’s factors

1.4.7 A binomial fit

1.4.8 A Cox fit

1.4.9 Multinomial and multi-response fits

1.4.10 Plots

1.4.11 Matching R

1.5 Plots

1.5.1 Coefficient paths

1.5.1.1 Lasso and elastic net

1.5.1.2 The x axis

1.5.1.3 Labels

1.5.1.4 Several classes or responses

1.5.2 Cross-validation curves

1.5.3 Combining with other plots

1.5.4 Writing to a file

1.6.1.3 Coefficients and predictions

1.6.1.4 Cross-validation

1.6.1.5 At lambda-min

1.6.1.6 Matching R

1.6.2 Ordinary least squares (λ = 0)

1.6.2.1 The data

1.6.2.2 Fitting

1.6.2.3 Fitted values

1.6.2.4 When to use it

1.6.3 Ridge regression (L2, α = 0)

1.6.3.1 The data

1.6.3.2 Fitting

1.6.3.3 The shrinkage path

1.6.3.4 When to use it

1.6.4 Lasso (L1, α = 1)

1.6.4.1 The data

1.6.4.2 Fitting

1.6.4.3 The selection path

1.6.4.4 Choosing λ by cross-validation

1.6.4.5 When to use it

1.6.5 Elastic net (0 < α < 1)

1.6.5.1 Fitting

1.6.5.2 Between ridge and lasso

1.6.5.3 Correlated predictors

1.6.5.4 Choosing α and λ

1.6.5.5 When to use it

1.6.6 Binomial logistic regression (classification)

1.6.6.1 The data

1.6.6.2 Fitting

1.6.6.3 Predicting

1.6.6.4 Separation and the penalty

1.6.6.5 When to use it

1.6.7 Multinomial classification (K classes)

1.6.7.5 When to use it

1.6.8 Cox proportional hazards (survival)

1.6.8.1 The data

1.6.8.2 Fitting

1.6.8.3 Ranking by risk

1.6.8.4 Varying λ

1.6.8.5 Censoring and events

1.6.8.6 When to use it

1.6.9 Poisson regression (counts)

1.6.9.5 When to use it

1.6.10 Multi-response Gaussian (grouped)

1.6.10.1 The data

1.6.10.2 Fitting

1.6.10.3 Predicting

1.6.10.4 Grouped versus separate fits

1.6.10.5 When to use it

1.6.11 Formula interactions

1.6.11.1 The data

1.6.11.2 Crossing weight and horsepower

1.6.11.3 Every pair

1.6.11.4 Choosing λ by cross-validation

1.6.11.5 Matching R

1.6.12 Formula transforms

1.6.13 Formula factors

1.6.13.1 The data

1.6.13.2 A factor

1.6.13.3 A slope for each cylinder count

1.6.13.4 New cars

1.6.13.5 Species as classes

1.6.13.6 Matching R

1.6.14 Data sources

1.6.14.1 A table

1.6.14.2 Lists and vectors

1.6.14.3 A CSV file

1.6.14.4 A matrix

1.6.14.5 A Polars dataframe

1.6.14.6 Formulas, and back

1.1	Getting started
1.2	Concepts
1.3	Data
1.4	Formulas and named data
1.5	Plots
1.6	Examples