
## Interactions and ANOVA

# Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html
#
# Download and format data:

from __future__ import print_function
from statsmodels.compat import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import statsmodels.api as sm
import pandas as pd
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 reiduals 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

try:
    resid = interM_lm32.get_influence().summary_frame()['standard_resid']
except:
    resid = interM_lm32.get_influence().summary_frame()['standard_resid']

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[~[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
    plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),
            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:
    minority_table = pd.read_table('minority.table')
except:  # don't have data already
    url = 'http://stats191.stanford.edu/data/minority.table'
    minority_table = pd.read_table(url)

factor_group = minority_table.groupby(['ETHN'])

plt.figure(figsize=(6,6))
colors = ['purple', 'green']
markers = ['o', 'v']
for factor, group in factor_group:
    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
                marker=markers[factor], s=12**2)
plt.xlabel('TEST');
plt.ylabel('JPERF');

min_lm = ols('JPERF ~ TEST', data=minority_table).fit()
print(min_lm.summary())

plt.figure(figsize=(6,6));
for factor, group in factor_group:
    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
                marker=markers[factor], s=12**2)

plt.xlabel('TEST')
plt.ylabel('JPERF')
abline_plot(model_results = min_lm, ax=plt.gca());

min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',
        data=minority_table).fit()

print(min_lm2.summary())

plt.figure(figsize=(6,6));
for factor, group in factor_group:
    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
                marker=markers[factor], s=12**2)

abline_plot(intercept = min_lm2.params['Intercept'],
                 slope = min_lm2.params['TEST'], ax=plt.gca(), color='purple');
abline_plot(intercept = min_lm2.params['Intercept'],
        slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],
        ax=plt.gca(), color='green');


min_lm3 = ols('JPERF ~ TEST + ETHN', data = minority_table).fit()
print(min_lm3.summary())

plt.figure(figsize=(6,6));
for factor, group in factor_group:
    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
                marker=markers[factor], s=12**2)

abline_plot(intercept = min_lm3.params['Intercept'],
                 slope = min_lm3.params['TEST'], ax=plt.gca(), color='purple');
abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],
        slope = min_lm3.params['TEST'], ax=plt.gca(), color='green');


min_lm4 = ols('JPERF ~ TEST * ETHN', data = minority_table).fit()
print(min_lm4.summary())

plt.figure(figsize=(6,6));
for factor, group in factor_group:
    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
                marker=markers[factor], s=12**2)

abline_plot(intercept = min_lm4.params['Intercept'],
                 slope = min_lm4.params['TEST'], ax=plt.gca(), color='purple');
abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],
        slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],
        ax=plt.gca(), color='green');


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

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

# is there any effect of ETHN 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')

plt.figure(figsize=(6,6))
rehab_table.boxplot('Time', 'Fitness', ax=plt.gca())

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_table(url, delimiter=" *")


# Explore the dataset

kidney_table.groupby(['Weight', 'Duration']).size()


# Balanced panel

kt = kidney_table
plt.figure(figsize=(6,6))
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.
#
#  Don't 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))




