Model estimates and diagnostics

Fit a model once, then read it from several angles: a coefficient forest, the predicted effect of a predictor, an estimation plot of group differences, and a panel of regression diagnostics.

import statsmodels.formula.api as smf

import depictr as dp

cy = dp.crop_yield()
# Q() quotes "yield" because it is a Python keyword.
model = smf.ols('Q("yield") ~ fertiliser + rainfall + soil_ph + treatment', cy).fit()

A dot-and-whisker forest of the coefficients.

p = dp.coefficient_plot(model, title="Drivers of crop yield")
p

The predicted response as one predictor varies, with a confidence band.

p = dp.effects_plot(model, "fertiliser")
p

A two-panel Gardner-Altman estimation plot of the group difference.

p = dp.estimation_plot(cy, "yield", "treatment", two_panel=True, seed=1)
p

The four-panel residual-diagnostic dashboard.

p = dp.residual_diagnostics_plot(model)
p