Index

_ | C | D | E | I | L | M | P | S | T | V | W

_

  • __init__() (smmargins.DiDResult method)
    • (smmargins.Expr method)
    • (smmargins.Margins method)
    • (smmargins.MarginsResult method)
    • (smmargins.Transform method)
    • (smmargins.WaldResult method)
  • _central_jacobian() (in module smmargins.utils)

C

  • ci_method (smmargins.MarginsResult attribute)
  • contrast_estimates (smmargins.WaldResult attribute)
  • contrast_matrix (smmargins.WaldResult attribute)

D

  • df (smmargins.WaldResult attribute)
  • DiDResult (class in smmargins)
  • draws (smmargins.MarginsResult attribute)

E

  • estimate (smmargins.MarginsResult attribute)
  • Exp (in module smmargins.transforms)
  • Expr (class in smmargins)

I

  • Identity (in module smmargins.transforms)

L

  • labels (smmargins.MarginsResult attribute)
  • Linear (in module smmargins.transforms)
  • Log (in module smmargins.transforms)
  • Logit (in module smmargins.transforms)

M

  • Margins (class in smmargins)
  • MarginsResult (class in smmargins)
  • module
    • smmargins

P

  • plot_comparisons() (in module smmargins)
  • plot_predictions() (in module smmargins)
  • plot_slopes() (in module smmargins)
  • Probit (in module smmargins.transforms)
  • pvalue (smmargins.WaldResult attribute)
  • pvalues (smmargins.MarginsResult attribute)

S

  • se (smmargins.MarginsResult attribute)
  • smmargins
    • module
  • stat (smmargins.WaldResult attribute)

T

  • Transform (class in smmargins)
  • tvalues (smmargins.MarginsResult attribute)

V

  • vcov (smmargins.MarginsResult attribute)

W

  • WaldResult (class in smmargins)

smmargins

Navigation

Getting started

  • Introduction

Tutorials — learning by doing

  • Tutorial 1: Getting Started with smmargins
  • Tutorial 2: Adjusted Predictions at Different Points
  • Tutorial 3: Marginal Effects
  • Tutorial 4: Inference and Standard Errors
  • Tutorial 5: Difference-in-Differences
  • Tutorial 6: Counterfactual Predictions and Plotting

How-to guides — task-focused recipes

  • How to compute robust and cluster-robust standard errors for marginal effects
  • How to use Krinsky–Robb simulation for standard errors and confidence intervals
  • How to compute bootstrap standard errors with pairs, cluster, or block resampling
  • How to compute simultaneous confidence intervals for families of margins
  • How to report marginal effects on custom scales and with user-defined transforms
  • How to compute subgroup-specific average marginal effects with the over parameter
  • How to perform joint Wald tests and pairwise comparisons on marginal effects
  • How to compute counterfactual predictions with values, Expr, and newdata
  • How to set covariate profiles with values=, Expr, and newdata=
  • How to compute elasticities and semi-elasticities for marginal effects
  • How to compute marginal effects for multinomial and ordered outcome models
  • How to plot predictions, slopes, and comparisons
  • How to verify smmargins results against R marginaleffects
  • How to choose between formula mode and raw exog mode

Reference

  • API reference
  • Demos

Explanations — theory and design

  • Mathematical motivation
  • Why Expr and the values= DSL
  • Analytic vs. Finite-Difference Jacobians
  • Why Patsy Design Matrix Rebuilding Matters
  • Prediction Scales and Link Functions
  • The Ai & Norton Difference-in-Differences Problem
  • Comparing Inference Methods: Delta vs. KR vs. Bootstrap
  • Multiple Comparison Adjustments
  • Discrete vs. Continuous Variable Detection
  • Formula Mode vs. Raw Exog Mode
  • Why Margins.contrast exists: joint covariance for two arms

Related Topics

  • Documentation overview
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