analyze_coefficient_plot

source

analyze_coefficient_plot(
    models,
    *,
    keep=None,
    drop=None,
    coef_labels=None,
    model_labels=None,
    alpha=0.05,
    joint=False,
    horizontal=True,
    drop_intercept=True,
    title=None,
)

Plot coefficient estimates with confidence intervals for one or more models.

Parameters

Name	Type	Description	Default
models	Any	A fitted pyfixest model, a list of fitted models, or a :class:`~expdpy.RegressionTableResult` (its `.models` are used). This lets you do `analyze_coefficient_plot(analyze_regression_table(...))` directly.	required
keep	Sequence[str] \| str \| None	Optional regular-expression patterns selecting which coefficients to show (and in which order). `keep` whitelists, `drop` blacklists; both use pyfixest’s own coefficient-selection semantics.	`None`
drop	Sequence[str] \| str \| None	Optional regular-expression patterns selecting which coefficients to show (and in which order). `keep` whitelists, `drop` blacklists; both use pyfixest’s own coefficient-selection semantics.	`None`
coef_labels	Mapping[str, str] \| None	Optional mapping from raw coefficient names to display labels.	`None`
model_labels	Sequence[str] \| None	Optional legend labels, one per model (defaults to `"Model 1"`, `"Model 2"`…).	`None`
alpha	float	Significance level for the confidence intervals (default `0.05` → 95% intervals).	`0.05`
joint	bool	If `True`, draw simultaneous (joint) confidence bands via `model.confint(joint= True)` instead of pointwise intervals.	`False`
horizontal	bool	If `True` (default), estimates run along the x-axis with coefficients listed down the y-axis (the most readable layout for many terms).	`True`
drop_intercept	bool	If `True` (default), omit the intercept term.	`True`
title	str \| None	Optional figure title.	`None`

Returns

Name	Type	Description
	CoefficientPlotResult	`df` (tidy long frame: `model`, `term`, `estimate`, `se`, `ci_lower`, `ci_upper`) and `fig` (the Plotly figure).

Examples

Basic — plot a single fitted model’s coefficients:

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
result = ex.analyze_regression_table(
    df, dvs="gini_regional", idvs=["log_gdp_pc", "log_gdp_pc_sq", "log_gdp_pc_cu"]
)
ex.analyze_coefficient_plot(result).fig

Advanced — compare several models side by side with custom labels:

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
pooled = ex.analyze_regression_table(
    df, dvs="gini_regional", idvs=["log_gdp_pc"]
)
fe = ex.analyze_regression_table(
    df, dvs="gini_regional", idvs=["log_gdp_pc"], feffects=["country", "year"]
)
ex.analyze_coefficient_plot(
    [pooled, fe],
    model_labels=["Pooled OLS", "Two-way FE"],
    keep=["log_gdp_pc"],
).fig