3.6.11. statsmodels.graphics.regressionplots¶
Partial Regression plot and residual plots to find misspecification
Author: Josef Perktold License: BSD-3 Created: 2011-01-23
update 2011-06-05 : start to convert example to usable functions 2011-10-27 : docstrings
3.6.11.1. Functions¶
abline_plot([intercept, slope, horiz, vert, ...]) |
Plots a line given an intercept and slope. |
add_lowess(ax[, lines_idx, frac]) |
Add Lowess line to a plot. |
influence_plot(results[, external, alpha, ...]) |
Plot of influence in regression. |
lowess(endog, exog[, frac, it, delta, ...]) |
LOWESS (Locally Weighted Scatterplot Smoothing) |
maybe_unwrap_results(results) |
Gets raw results back from wrapped results. |
plot_ccpr(results, exog_idx[, ax]) |
Plot CCPR against one regressor. |
plot_ccpr_grid(results[, exog_idx, grid, fig]) |
Generate CCPR plots against a set of regressors, plot in a grid. |
plot_fit(results, exog_idx[, y_true, ax]) |
Plot fit against one regressor. |
plot_leverage_resid2(results[, alpha, ...]) |
Plots leverage statistics vs. |
plot_partregress(endog, exog_i, exog_others) |
Plot partial regression for a single regressor. |
plot_partregress_grid(results[, exog_idx, ...]) |
Plot partial regression for a set of regressors. |
plot_regress_exog(results, exog_idx[, fig]) |
Plot regression results against one regressor. |
wls_prediction_std(res[, exog, weights, alpha]) |
calculate standard deviation and confidence interval for prediction |