#!/usr/bin/env python
# coding: utf-8

# DO NOT EDIT
# Autogenerated from the notebook regression_plots.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT

# # Regression Plots

from statsmodels.compat import lzip
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import ols

plt.rc("figure", figsize=(16, 8))
plt.rc("font", size=14)

# ## Duncan's Prestige Dataset

# ### Load the Data

# We can use a utility function to load any R dataset available from the
# great <a href="https://vincentarelbundock.github.io/Rdatasets/">Rdatasets
# package</a>.

prestige = sm.datasets.get_rdataset("Duncan", "carData", cache=True).data

prestige.head()

prestige_model = ols("prestige ~ income + education", data=prestige).fit()

print(prestige_model.summary())

# ### Influence plots

# Influence plots show the (externally) studentized residuals vs. the
# leverage of each observation as measured by the hat matrix.
#
# Externally studentized residuals are residuals that are scaled by their
# standard deviation where
#
# $$var(\hat{\epsilon}_i)=\hat{\sigma}^2_i(1-h_{ii})$$
#
# with
#
# $$\hat{\sigma}^2_i=\frac{1}{n - p - 1 \;\;}\sum_{j}^{n}\;\;\;\forall
# \;\;\; j \neq i$$
#
# $n$ is the number of observations and $p$ is the number of regressors.
# $h_{ii}$ is the $i$-th diagonal element of the hat matrix
#
# $$H=X(X^{\;\prime}X)^{-1}X^{\;\prime}$$
#
# The influence of each point can be visualized by the criterion keyword
# argument. Options are Cook's distance and DFFITS, two measures of
# influence.

fig = sm.graphics.influence_plot(prestige_model, criterion="cooks")
fig.tight_layout(pad=1.0)

# As you can see there are a few worrisome observations. Both contractor
# and reporter have low leverage but a large residual. <br />
# RR.engineer has small residual and large leverage. Conductor and
# minister have both high leverage and large residuals, and, <br />
# therefore, large influence.

# ### Partial Regression Plots (Duncan)

# Since we are doing multivariate regressions, we cannot just look at
# individual bivariate plots to discern relationships. <br />
# Instead, we want to look at the relationship of the dependent variable
# and independent variables conditional on the other <br />
# independent variables. We can do this through using partial regression
# plots, otherwise known as added variable plots. <br />
#
# In a partial regression plot, to discern the relationship between the
# response variable and the $k$-th variable, we compute <br />
# the residuals by regressing the response variable versus the independent
# variables excluding $X_k$. We can denote this by <br />
# $X_{\sim k}$. We then compute the residuals by regressing $X_k$ on
# $X_{\sim k}$. The partial regression plot is the plot <br />
# of the former versus the latter residuals. <br />
#
# The notable points of this plot are that the fitted line has slope
# $\beta_k$ and intercept zero. The residuals of this plot <br />
# are the same as those of the least squares fit of the original model
# with full $X$. You can discern the effects of the <br />
# individual data values on the estimation of a coefficient easily. If
# obs_labels is True, then these points are annotated <br />
# with their observation label. You can also see the violation of
# underlying assumptions such as homoskedasticity and <br />
# linearity.

fig = sm.graphics.plot_partregress("prestige",
                                   "income", ["income", "education"],
                                   data=prestige)
fig.tight_layout(pad=1.0)

fig = sm.graphics.plot_partregress("prestige",
                                   "income", ["education"],
                                   data=prestige)
fig.tight_layout(pad=1.0)

# As you can see the partial regression plot confirms the influence of
# conductor, minister, and RR.engineer on the partial relationship between
# income and prestige. The cases greatly decrease the effect of income on
# prestige. Dropping these cases confirms this.

subset = ~prestige.index.isin(["conductor", "RR.engineer", "minister"])
prestige_model2 = ols("prestige ~ income + education",
                      data=prestige,
                      subset=subset).fit()
print(prestige_model2.summary())

# For a quick check of all the regressors, you can use
# plot_partregress_grid. These plots will not label the <br />
# points, but you can use them to identify problems and then use
# plot_partregress to get more information.

fig = sm.graphics.plot_partregress_grid(prestige_model)
fig.tight_layout(pad=1.0)

# ### Component-Component plus Residual (CCPR) Plots

# The CCPR plot provides a way to judge the effect of one regressor on the
# <br />
# response variable by taking into account the effects of the other  <br
# />
# independent variables. The partial residuals plot is defined as  <br />
# $\text{Residuals} + B_iX_i \text{ }\text{ }$   versus $X_i$. The
# component adds $B_iX_i$ versus  <br />
# $X_i$ to show where the fitted line would lie. Care should be taken if
# $X_i$  <br />
# is highly correlated with any of the other independent variables. If
# this  <br />
# is the case, the variance evident in the plot will be an underestimate
# of  <br />
# the true variance.

fig = sm.graphics.plot_ccpr(prestige_model, "education")
fig.tight_layout(pad=1.0)

# As you can see the relationship between the variation in prestige
# explained by education conditional on income seems to be linear, though
# you can see there are some observations that are exerting considerable
# influence on the relationship. We can quickly look at more than one
# variable by using plot_ccpr_grid.

fig = sm.graphics.plot_ccpr_grid(prestige_model)
fig.tight_layout(pad=1.0)

# ### Single Variable Regression Diagnostics

# The plot_regress_exog function is a convenience function that gives a
# 2x2 plot containing the dependent variable and fitted values with
# confidence intervals vs. the independent variable chosen, the residuals of
# the model vs. the chosen independent variable, a partial regression plot,
# and a CCPR plot. This function can be used for quickly checking modeling
# assumptions with respect to a single regressor.

fig = sm.graphics.plot_regress_exog(prestige_model, "education")
fig.tight_layout(pad=1.0)

# ### Fit Plot

# The plot_fit function plots the fitted values versus a chosen
# independent variable. It includes prediction confidence intervals and
# optionally plots the true dependent variable.

fig = sm.graphics.plot_fit(prestige_model, "education")
fig.tight_layout(pad=1.0)

# ## Statewide Crime 2009 Dataset

# Compare the following to http://www.ats.ucla.edu/stat/stata/webbooks/reg
# /chapter4/statareg_self_assessment_answers4.htm
#
# Though the data here is not the same as in that example. You could run
# that example by uncommenting the necessary cells below.

# dta =
# pd.read_csv("http://www.stat.ufl.edu/~aa/social/csv_files/statewide-
# crime-2.csv")
# dta = dta.set_index("State", inplace=True).dropna()
# dta.rename(columns={"VR" : "crime",
#                    "MR" : "murder",
#                    "M"  : "pctmetro",
#                    "W"  : "pctwhite",
#                    "H"  : "pcths",
#                    "P"  : "poverty",
#                    "S"  : "single"
#                    }, inplace=True)
#
# crime_model = ols("murder ~ pctmetro + poverty + pcths + single",
# data=dta).fit()

dta = sm.datasets.statecrime.load_pandas().data

crime_model = ols("murder ~ urban + poverty + hs_grad + single",
                  data=dta).fit()
print(crime_model.summary())

# ### Partial Regression Plots (Crime Data)

fig = sm.graphics.plot_partregress_grid(crime_model)
fig.tight_layout(pad=1.0)

fig = sm.graphics.plot_partregress("murder",
                                   "hs_grad", ["urban", "poverty", "single"],
                                   data=dta)
fig.tight_layout(pad=1.0)

# ### Leverage-Resid<sup>2</sup> Plot

# Closely related to the influence_plot is the leverage-resid<sup>2</sup>
# plot.

fig = sm.graphics.plot_leverage_resid2(crime_model)
fig.tight_layout(pad=1.0)

# ### Influence Plot

fig = sm.graphics.influence_plot(crime_model)
fig.tight_layout(pad=1.0)

# ### Using robust regression to correct for outliers.

# Part of the problem here in recreating the Stata results is that
# M-estimators are not robust to leverage points. MM-estimators should do
# better with this examples.

from statsmodels.formula.api import rlm

rob_crime_model = rlm(
    "murder ~ urban + poverty + hs_grad + single",
    data=dta,
    M=sm.robust.norms.TukeyBiweight(3),
).fit(conv="weights")
print(rob_crime_model.summary())

# rob_crime_model = rlm("murder ~ pctmetro + poverty + pcths + single",
# data=dta, M=sm.robust.norms.TukeyBiweight()).fit(conv="weights")
# print(rob_crime_model.summary())

# There is not yet an influence diagnostics method as part of RLM, but we
# can recreate them. (This depends on the status of [issue
# #888](https://github.com/statsmodels/statsmodels/issues/808))

weights = rob_crime_model.weights
idx = weights > 0
X = rob_crime_model.model.exog[idx.values]
ww = weights[idx] / weights[idx].mean()
hat_matrix_diag = ww * (X * np.linalg.pinv(X).T).sum(1)
resid = rob_crime_model.resid
resid2 = resid**2
resid2 /= resid2.sum()
nobs = int(idx.sum())
hm = hat_matrix_diag.mean()
rm = resid2.mean()

from statsmodels.graphics import utils

fig, ax = plt.subplots(figsize=(16, 8))
ax.plot(resid2[idx], hat_matrix_diag, "o")
ax = utils.annotate_axes(
    range(nobs),
    labels=rob_crime_model.model.data.row_labels[idx],
    points=lzip(resid2[idx], hat_matrix_diag),
    offset_points=[(-5, 5)] * nobs,
    size="large",
    ax=ax,
)
ax.set_xlabel("resid2")
ax.set_ylabel("leverage")
ylim = ax.get_ylim()
ax.vlines(rm, *ylim)
xlim = ax.get_xlim()
ax.hlines(hm, *xlim)
ax.margins(0, 0)
