Análises pós-hoc após ANOVA

Projeto experimental em Python

James Chapman

Curriculum Manager, DataCamp

Quando usar testes pós-hoc

Após um ANOVA significativo
Para explorar diferenças par a par entre grupos

Principais métodos pós-hoc

HSD de Tukey (Honest Significant Difference)
- Robusto para múltiplas comparações
- Ideal para comparar todos os grupos
Correção de Bonferroni
- Ajusta p-values para controlar erros Tipo I
- Ideal para comparações específicas

John Tukey

Carlo Emilio Bonferroni

¹ https://www.amphilsoc.org/item-detail/photograph-john-wilder-tukey ² https://en.wikipedia.org/wiki/Carlo_Emilio_Bonferroni

O dataset: campanhas de marketing

ad_campaigns

              Ad_Campaign  Click_Through_Rate
1300    Seasonal Discount        2.1659547732
1661          New Arrival        2.9409657365
2762       Loyalty Reward        3.2476777154
571     Seasonal Discount        3.3382186561
775     Seasonal Discount        1.7148876401

Organizando dados com tabelas dinâmicas

pivot_table = ad_campaigns.pivot_table(values='Click_Through_Rate',
                                       index='Ad_Campaign',
                                       aggfunc="mean")
print(pivot_table)

                   Click_Through_Rate
Ad_Campaign                          
Loyalty Reward               2.792716
New Arrival                  3.013843
Seasonal Discount            2.518917

Executando ANOVA

from scipy.stats import f_oneway
campaign_types = ['Seasonal Discount', 'New Arrival', 'Loyalty Reward']

groups = [ad_campaigns[ad_campaigns['Ad_Campaign'] == campaign]['Click_Through_Rate']
              for campaign in campaign_types]


f_stat, p_val = f_oneway(*groups)
print(p_val)

4.484124496940693e-134

Teste HSD de Tukey

from statsmodels.stats.multicomp import pairwise_tukeyhsd

tukey_results = pairwise_tukeyhsd(ad_campaigns['Click_Through_Rate'],

                                  ad_campaigns['Ad_Campaign'],

                                  alpha=0.05)

print(tukey_results)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05          
===========================================================================
        group1             group2  meandiff  p-adj   lower    upper  reject
<hr />---------------------------------------------------------------------
Loyalty Reward        New Arrival    0.2211   0.0    0.176   0.2663    True
Loyalty Reward  Seasonal Discount   -0.2738   0.0  -0.3189  -0.2287    True
   New Arrival  Seasonal Discount   -0.4949   0.0  -0.5401  -0.4498    True
<hr />---------------------------------------------------------------------

Configuração da correção de Bonferroni

from scipy.stats import ttest_ind
from statsmodels.sandbox.stats.multicomp import multipletests

p_values = []


comparisons = [('Seasonal Discount', 'New Arrival'),
               ('Seasonal Discount', 'Loyalty Reward'), 
               ('New Arrival', 'Loyalty Reward')]


for comp in comparisons:
    group1 = ad_campaigns[ad_campaigns['Ad_Campaign'] == comp[0]]['Click_Through_Rate']
    group2 = ad_campaigns[ad_campaigns['Ad_Campaign'] == comp[1]]['Click_Through_Rate']

    t_stat, p_val = ttest_ind(group1, group2)

    p_values.append(p_val)

Aplicando a correção de Bonferroni

p_adjusted = multipletests(p_values, alpha=0.05, method='bonferroni')

print(f"Adjusted P-values: {p_adjusted[1]}")

Adjusted P-values: [5.33634403e-133 2.17627991e-043 5.62590083e-029]

Vamos praticar!

Projeto experimental em Python