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

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

# # Interactions and ANOVA

# Note: This script is based heavily on Jonathan Taylor's class notes
# https://web.stanford.edu/class/stats191/notebooks/Interactions.html
#
# Download and format data:

from urllib.request import urlopen
import numpy as np

np.set_printoptions(precision=4, suppress=True)

import pandas as pd

pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols
from statsmodels.graphics.api import interaction_plot, abline_plot
from statsmodels.stats.anova import anova_lm

try:
    salary_table = pd.read_csv("salary.table")
except:  # recent pandas can read URL without urlopen
    url = "http://stats191.stanford.edu/data/salary.table"
    fh = urlopen(url)
    salary_table = pd.read_table(fh)
    salary_table.to_csv("salary.table")

E = salary_table.E
M = salary_table.M
X = salary_table.X
S = salary_table.S

# Take a look at the data:

plt.figure(figsize=(6, 6))
symbols = ["D", "^"]
colors = ["r", "g", "blue"]
factor_groups = salary_table.groupby(["E", "M"])
for values, group in factor_groups:
    i, j = values
    plt.scatter(group["X"],
                group["S"],
                marker=symbols[j],
                color=colors[i - 1],
                s=144)
plt.xlabel("Experience")
plt.ylabel("Salary")

# Fit a linear model:

formula = "S ~ C(E) + C(M) + X"
lm = ols(formula, salary_table).fit()
print(lm.summary())

# Have a look at the created design matrix:

lm.model.exog[:5]

# Or since we initially passed in a DataFrame, we have a DataFrame
# available in

lm.model.data.orig_exog[:5]

# We keep a reference to the original untouched data in

lm.model.data.frame[:5]

# Influence statistics

infl = lm.get_influence()
print(infl.summary_table())

# or get a dataframe

df_infl = infl.summary_frame()

df_infl[:5]

# Now plot the residuals within the groups separately:

resid = lm.resid
plt.figure(figsize=(6, 6))
for values, group in factor_groups:
    i, j = values
    group_num = i * 2 + j - 1  # for plotting purposes
    x = [group_num] * len(group)
    plt.scatter(
        x,
        resid[group.index],
        marker=symbols[j],
        color=colors[i - 1],
        s=144,
        edgecolors="black",
    )
plt.xlabel("Group")
plt.ylabel("Residuals")

# Now we will test some interactions using anova or f_test

interX_lm = ols("S ~ C(E) * X + C(M)", salary_table).fit()
print(interX_lm.summary())

# Do an ANOVA check

from statsmodels.stats.api import anova_lm

table1 = anova_lm(lm, interX_lm)
print(table1)

interM_lm = ols("S ~ X + C(E)*C(M)", data=salary_table).fit()
print(interM_lm.summary())

table2 = anova_lm(lm, interM_lm)
print(table2)

# The design matrix as a DataFrame

interM_lm.model.data.orig_exog[:5]

# The design matrix as an ndarray

interM_lm.model.exog
interM_lm.model.exog_names

infl = interM_lm.get_influence()
resid = infl.resid_studentized_internal
plt.figure(figsize=(6, 6))
for values, group in factor_groups:
    i, j = values
    idx = group.index
    plt.scatter(
        X[idx],
        resid[idx],
        marker=symbols[j],
        color=colors[i - 1],
        s=144,
        edgecolors="black",
    )
plt.xlabel("X")
plt.ylabel("standardized resids")

# Looks like one observation is an outlier.

drop_idx = abs(resid).argmax()
print(drop_idx)  # zero-based index
idx = salary_table.index.drop(drop_idx)

lm32 = ols("S ~ C(E) + X + C(M)", data=salary_table, subset=idx).fit()

print(lm32.summary())
print("\n")

interX_lm32 = ols("S ~ C(E) * X + C(M)", data=salary_table, subset=idx).fit()

print(interX_lm32.summary())
print("\n")

table3 = anova_lm(lm32, interX_lm32)
print(table3)
print("\n")

interM_lm32 = ols("S ~ X + C(E) * C(M)", data=salary_table, subset=idx).fit()

table4 = anova_lm(lm32, interM_lm32)
print(table4)
print("\n")

#  Replot the residuals

resid = interM_lm32.get_influence().summary_frame()["standard_resid"]

plt.figure(figsize=(6, 6))
resid = resid.reindex(X.index)
for values, group in factor_groups:
    i, j = values
    idx = group.index
    plt.scatter(
        X.loc[idx],
        resid.loc[idx],
        marker=symbols[j],
        color=colors[i - 1],
        s=144,
        edgecolors="black",
    )
plt.xlabel("X[~[32]]")
plt.ylabel("standardized resids")

#  Plot the fitted values

lm_final = ols("S ~ X + C(E)*C(M)", data=salary_table.drop([drop_idx])).fit()
mf = lm_final.model.data.orig_exog
lstyle = ["-", "--"]

plt.figure(figsize=(6, 6))
for values, group in factor_groups:
    i, j = values
    idx = group.index
    plt.scatter(
        X[idx],
        S[idx],
        marker=symbols[j],
        color=colors[i - 1],
        s=144,
        edgecolors="black",
    )
    # drop NA because there is no idx 32 in the final model
    fv = lm_final.fittedvalues.reindex(idx).dropna()
    x = mf.X.reindex(idx).dropna()
    plt.plot(x, fv, ls=lstyle[j], color=colors[i - 1])
plt.xlabel("Experience")
plt.ylabel("Salary")

# From our first look at the data, the difference between Master's and PhD
# in the management group is different than in the non-management group.
# This is an interaction between the two qualitative variables management,M
# and education,E. We can visualize this by first removing the effect of
# experience, then plotting the means within each of the 6 groups using
# interaction.plot.

U = S - X * interX_lm32.params["X"]

plt.figure(figsize=(6, 6))
interaction_plot(E,
                 M,
                 U,
                 colors=["red", "blue"],
                 markers=["^", "D"],
                 markersize=10,
                 ax=plt.gca())

# ## Minority Employment Data

try:
    jobtest_table = pd.read_table("jobtest.table")
except:  # do not have data already
    url = "http://stats191.stanford.edu/data/jobtest.table"
    jobtest_table = pd.read_table(url)

factor_group = jobtest_table.groupby(["MINORITY"])

fig, ax = plt.subplots(figsize=(6, 6))
colors = ["purple", "green"]
markers = ["o", "v"]
for factor, group in factor_group:
    ax.scatter(
        group["TEST"],
        group["JPERF"],
        color=colors[factor],
        marker=markers[factor],
        s=12**2,
    )
ax.set_xlabel("TEST")
ax.set_ylabel("JPERF")

min_lm = ols("JPERF ~ TEST", data=jobtest_table).fit()
print(min_lm.summary())

fig, ax = plt.subplots(figsize=(6, 6))
for factor, group in factor_group:
    ax.scatter(
        group["TEST"],
        group["JPERF"],
        color=colors[factor],
        marker=markers[factor],
        s=12**2,
    )

ax.set_xlabel("TEST")
ax.set_ylabel("JPERF")
fig = abline_plot(model_results=min_lm, ax=ax)

min_lm2 = ols("JPERF ~ TEST + TEST:MINORITY", data=jobtest_table).fit()

print(min_lm2.summary())

fig, ax = plt.subplots(figsize=(6, 6))
for factor, group in factor_group:
    ax.scatter(
        group["TEST"],
        group["JPERF"],
        color=colors[factor],
        marker=markers[factor],
        s=12**2,
    )

fig = abline_plot(
    intercept=min_lm2.params["Intercept"],
    slope=min_lm2.params["TEST"],
    ax=ax,
    color="purple",
)
fig = abline_plot(
    intercept=min_lm2.params["Intercept"],
    slope=min_lm2.params["TEST"] + min_lm2.params["TEST:MINORITY"],
    ax=ax,
    color="green",
)

min_lm3 = ols("JPERF ~ TEST + MINORITY", data=jobtest_table).fit()
print(min_lm3.summary())

fig, ax = plt.subplots(figsize=(6, 6))
for factor, group in factor_group:
    ax.scatter(
        group["TEST"],
        group["JPERF"],
        color=colors[factor],
        marker=markers[factor],
        s=12**2,
    )

fig = abline_plot(
    intercept=min_lm3.params["Intercept"],
    slope=min_lm3.params["TEST"],
    ax=ax,
    color="purple",
)
fig = abline_plot(
    intercept=min_lm3.params["Intercept"] + min_lm3.params["MINORITY"],
    slope=min_lm3.params["TEST"],
    ax=ax,
    color="green",
)

min_lm4 = ols("JPERF ~ TEST * MINORITY", data=jobtest_table).fit()
print(min_lm4.summary())

fig, ax = plt.subplots(figsize=(8, 6))
for factor, group in factor_group:
    ax.scatter(
        group["TEST"],
        group["JPERF"],
        color=colors[factor],
        marker=markers[factor],
        s=12**2,
    )

fig = abline_plot(
    intercept=min_lm4.params["Intercept"],
    slope=min_lm4.params["TEST"],
    ax=ax,
    color="purple",
)
fig = abline_plot(
    intercept=min_lm4.params["Intercept"] + min_lm4.params["MINORITY"],
    slope=min_lm4.params["TEST"] + min_lm4.params["TEST:MINORITY"],
    ax=ax,
    color="green",
)

# is there any effect of MINORITY on slope or intercept?
table5 = anova_lm(min_lm, min_lm4)
print(table5)

# is there any effect of MINORITY on intercept
table6 = anova_lm(min_lm, min_lm3)
print(table6)

# is there any effect of MINORITY on slope
table7 = anova_lm(min_lm, min_lm2)
print(table7)

# is it just the slope or both?
table8 = anova_lm(min_lm2, min_lm4)
print(table8)

# ## One-way ANOVA

try:
    rehab_table = pd.read_csv("rehab.table")
except:
    url = "http://stats191.stanford.edu/data/rehab.csv"
    rehab_table = pd.read_table(url, delimiter=",")
    rehab_table.to_csv("rehab.table")

fig, ax = plt.subplots(figsize=(8, 6))
fig = rehab_table.boxplot("Time", "Fitness", ax=ax, grid=False)

rehab_lm = ols("Time ~ C(Fitness)", data=rehab_table).fit()
table9 = anova_lm(rehab_lm)
print(table9)

print(rehab_lm.model.data.orig_exog)

print(rehab_lm.summary())

# ## Two-way ANOVA

try:
    kidney_table = pd.read_table("./kidney.table")
except:
    url = "http://stats191.stanford.edu/data/kidney.table"
    kidney_table = pd.read_csv(url, delim_whitespace=True)

# Explore the dataset

kidney_table.head(10)

# Balanced panel

kt = kidney_table
plt.figure(figsize=(8, 6))
fig = interaction_plot(
    kt["Weight"],
    kt["Duration"],
    np.log(kt["Days"] + 1),
    colors=["red", "blue"],
    markers=["D", "^"],
    ms=10,
    ax=plt.gca(),
)

# You have things available in the calling namespace available in the
# formula evaluation namespace

kidney_lm = ols("np.log(Days+1) ~ C(Duration) * C(Weight)", data=kt).fit()

table10 = anova_lm(kidney_lm)

print(
    anova_lm(
        ols("np.log(Days+1) ~ C(Duration) + C(Weight)", data=kt).fit(),
        kidney_lm))
print(
    anova_lm(
        ols("np.log(Days+1) ~ C(Duration)", data=kt).fit(),
        ols("np.log(Days+1) ~ C(Duration) + C(Weight, Sum)", data=kt).fit(),
    ))
print(
    anova_lm(
        ols("np.log(Days+1) ~ C(Weight)", data=kt).fit(),
        ols("np.log(Days+1) ~ C(Duration) + C(Weight, Sum)", data=kt).fit(),
    ))

# ## Sum of squares
#
#  Illustrates the use of different types of sums of squares (I,II,II)
#  and how the Sum contrast can be used to produce the same output between
#  the 3.
#
#  Types I and II are equivalent under a balanced design.
#
#  Do not use Type III with non-orthogonal contrast - ie., Treatment

sum_lm = ols("np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)",
             data=kt).fit()

print(anova_lm(sum_lm))
print(anova_lm(sum_lm, typ=2))
print(anova_lm(sum_lm, typ=3))

nosum_lm = ols(
    "np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)",
    data=kt).fit()
print(anova_lm(nosum_lm))
print(anova_lm(nosum_lm, typ=2))
print(anova_lm(nosum_lm, typ=3))
