{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Associazione tra Variabili\n", "\n", "Abbiamo finora analizzato campioni \"univariati\", ovvero composti di una sola variabile. Più precisamente, anche quando diverse variabili erano disponibili, abbiamo analizzato le variabili ad una ad una. In pratica, capita spesso che le osservazioni sotto analisi siano costituite da più variabili. In questi casi è utile considerare dei metodi per descrivere e visualizzare i dati in maniera \"multivariata\", in modo da studiare le interazioni tra i vari fattori che descrivono il fenomeno in analisi. \n", "\n", "Utilizzeremo come esempio nuovamente il dataset Titanic:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
....................................
88702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
89011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", "

891 rows × 11 columns

\n", "
" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0 3 \n", "2 1 1 \n", "3 1 3 \n", "4 1 1 \n", "5 0 3 \n", "... ... ... \n", "887 0 2 \n", "888 1 1 \n", "889 0 3 \n", "890 1 1 \n", "891 0 3 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", "5 Allen, Mr. William Henry male 35.0 \n", "... ... ... ... \n", "887 Montvila, Rev. Juozas male 27.0 \n", "888 Graham, Miss. Margaret Edith female 19.0 \n", "889 Johnston, Miss. Catherine Helen \"Carrie\" female NaN \n", "890 Behr, Mr. Karl Howell male 26.0 \n", "891 Dooley, Mr. Patrick male 32.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S \n", "2 1 0 PC 17599 71.2833 C85 C \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", "4 1 0 113803 53.1000 C123 S \n", "5 0 0 373450 8.0500 NaN S \n", "... ... ... ... ... ... ... \n", "887 0 0 211536 13.0000 NaN S \n", "888 0 0 112053 30.0000 B42 S \n", "889 1 2 W./C. 6607 23.4500 NaN S \n", "890 0 0 111369 30.0000 C148 C \n", "891 0 0 370376 7.7500 NaN Q \n", "\n", "[891 rows x 11 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "titanic = pd.read_csv('https://raw.githubusercontent.com/agconti/kaggle-titanic/master/data/train.csv',\n", " index_col='PassengerId')\n", "titanic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dato il dataset sopra, potremmo chiederci se delle variabili influenzano i valori di altre. Ad esempio, trovarsi in prima, seconda o terza classe (variabile `Pclass`) influenza in qualche modo la probabilità di sopravvivere (variabile `Survived`)?, o ancora, l'età (variabile `Age`) o il prezzo pagato (`Fare`) influenza in qualche modo la probabilità di salvarsi (`Survived`)?\n", "\n", "In questa lezione, vedremo diversi modi per riassumere le distribuzioni di due variabili e verificare eventuali associazioni (o correlazioni) tra le variabili." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tabelle di contingenza e probabilità\n", "\n", "Se entrambe le variabili che stiamo studiando sono discrete, possiamo enumerare tutte le possibili combinazioni di valori e riassumerle in una **tabella di contingenza** che indica i valori di una variabile sulle righe e quelli dell'altra variabile sulle colonne. Ogni cella indicherà il numero di volte in cui osserviamo una data coppia di valori.\n", "\n", "La tabella di contingenza per le variabili `Sex` e `Pclass` avrà questo aspetto:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pclass123
Sex
female9476144
male122108347
\n", "
" ], "text/plain": [ "Pclass 1 2 3\n", "Sex \n", "female 94 76 144\n", "male 122 108 347" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La tabella sopra indica ad esempio che $94$ passeggeri in classe $1$ erano di sesso femminile. Una tabella di contingenza è spesso mostrata con dei valori a margine che mostrano le somme di righe e colonne:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pclass123All
Sex
female9476144314
male122108347577
All216184491891
\n", "
" ], "text/plain": [ "Pclass 1 2 3 All\n", "Sex \n", "female 94 76 144 314\n", "male 122 108 347 577\n", "All 216 184 491 891" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass'],margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si noti che i valori \"All\" indicano le frequenze assolute delle due variabili, mentre il valore in basso a destra indica la numerosità del campione." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A partire dalla tabella di contingenza, è possibile ragionare in termini dei vari concetti che abbiamo visto quando abbiamo parlato di probabilità nel caso di variabili discrete, e in particolare di:\n", "* Joint Probability Distributions (o distribuzioni di frequenze/probabilità congiunte)\n", "* Marginal Probability Distributions (o distribuzioni di frequenze/probabilità marginali)\n", "* Conditional Probability Distributions (o distribuzioni di frequenze/probabilità condizionali)\n", "\n", "Vediamo qualche esempio:\n", "\n", "### Joint Probability Distributions" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pclass123
Sex
female0.1054990.0852970.161616
male0.1369250.1212120.389450
\n", "
" ], "text/plain": [ "Pclass 1 2 3\n", "Sex \n", "female 0.105499 0.085297 0.161616\n", "male 0.136925 0.121212 0.389450" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Marginal Probability Distributions" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pclass123All
Sex
female0.1054990.0852970.1616160.352413
male0.1369250.1212120.3894500.647587
All0.2424240.2065100.5510661.000000
\n", "
" ], "text/plain": [ "Pclass 1 2 3 All\n", "Sex \n", "female 0.105499 0.085297 0.161616 0.352413\n", "male 0.136925 0.121212 0.389450 0.647587\n", "All 0.242424 0.206510 0.551066 1.000000" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=True, margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le marginali sono riportate nell'ultima riga e ultima colonna." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conditional Probability Distributions\n", "\n", "Probabilità condizionate rispetto alla variabile `Sex` (ottenute dividendo i valori della tabella di contingenza per le somme dei valori sulle righe - colonna `All`):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pclass123
Sex
female0.2993630.2420380.458599
male0.2114380.1871750.601386
All0.2424240.2065100.551066
\n", "
" ], "text/plain": [ "Pclass 1 2 3\n", "Sex \n", "female 0.299363 0.242038 0.458599\n", "male 0.211438 0.187175 0.601386\n", "All 0.242424 0.206510 0.551066" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#normalize=0 indica di condizionare rispetto alla prima variabile\n", "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=0, margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si noti che nella tabella sopra non sono riportati valori a margine delle righe, in quanto questi sarebbero tutti uguali a 1.\n", "\n", "Dalla tabella sopra, posso evincere:\n", "\n", "* $P(Pclass=1|Sex=female) = 0.290363 = \\frac{94}{314}$\n", "* $P(Pclass=2|Sex=female) = 0.242038 = \\frac{76}{314}$\n", "* $P(Pclass=3|Sex=female) = 0.458599 = \\frac{144}{314}$\n", "* $P(Pclass=1|Sex=male) = 0.211438 = \\frac{122}{577}$\n", "* $P(Pclass=2|Sex=male) = 0.187175 = \\frac{108}{577}$\n", "* $P(Pclass=3|Sex=male) = 0.601386 = \\frac{347}{577}$\n", "\n", "Dalla tabella, notiamo che la distribuzione dei passeggeri cambia nelle tre classi. In particolare, tra gli uomini, la terza classe è più frequente che tra le donne. \n", "\n", "Possiamo ottenere la prospettiva complementare condizionando rispetto alla classe invece:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pclass123All
Sex
female0.4351850.4130430.2932790.352413
male0.5648150.5869570.7067210.647587
\n", "
" ], "text/plain": [ "Pclass 1 2 3 All\n", "Sex \n", "female 0.435185 0.413043 0.293279 0.352413\n", "male 0.564815 0.586957 0.706721 0.647587" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#normalize=1 indica di condizionare rispetto alla prima variabile\n", "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=1, margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si noti che nella tabella sopra non sono riportati valori a margine delle colonne, in quanto questi sarebbero tutti uguali a 1.\n", "\n", "In questo caso, ogni colonna sarà una distribuzione di probabilità. Ad esempio:\n", "\n", "* $P(Sex=female|Pclass=1) = 0.435185$\n", "* $P(Sex=male|Pclass=1) = 0.564815$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notiamo che le proporzione tra uomini e donne cambiano nelle tre classi e in particolare nella terza classe ci sono molti più uomini che donne." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rappresentazioni Grafiche\n", "Possiamo facilmente ottenere rappresentazioni grafiche delle relazioni tra due variabili mediante grafici a barre, direttamente dalle tabelle di contingenza. Ad esempio, il seguente grafico confronta le frequenze congiunte assolute:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass']).plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Da qui notiamo che molti passeggeri sono uomini e in terza classe. In questi casi, può essere a volte utile uno stacked plot:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass']).plot.bar(stacked=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "remove-input" ] }, "source": [ "È spesso utile visualizzare le distribuzioni condizionate. Ad esempio, il grafico che segue ci permette di confrontare le distribuzioni dei passeggeri nelle tre classi, suddividendo in due gruppi sulla base del sesso:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=0).plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Anche in questi casi possiamo usare uno stacked plot:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=0).plot.bar(stacked=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analogamente, possiamo ottenere i grafici delle frequenze condizionate rispetto alle classi:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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LW4Mj4mlJH5A0ICK2tuqrAqoAKisrvSxj1g0Mnf5El+5vY58u3d0hJcsReg0wXFKFpF7AJKA6f4CkYZKUe/xBoBewrdjFmplZ29o9Qo+IZknTgKVAGTA/ItZJmprrnwv8OXCVpCbgTeCyvA9JzcysC2RZciEilgBLWrXNzXt8J3BncUszM7OO8JmiZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIjIFuqQJktZLqpM0vUD/lZLW5H6ekXRq8Us1M7P9aTfQJZUBc4ALgVHA5ZJGtRr2K+C8iBgD3AFUFbtQMzPbvyxH6GOBuojYEBG7gcXAxPwBEfFMRLya23wOKC9umWZm1p4sgT4I2Jy3XZ9ra8ungR8eTFFmZtZxPTKMUYG2KDhQ+igtgf6RNvqnAFMAhgwZkrFEMzPLIssRej0wOG+7HNjSepCkMcD9wMSI2FZoooioiojKiKgcOHDggdRrZmZtyBLoNcBwSRWSegGTgOr8AZKGAI8A/yci/qf4ZZqZWXvaXXKJiGZJ04ClQBkwPyLWSZqa658L3Ab0B+6VBNAcEZWdV7aZmbWWZQ2diFgCLGnVNjfv8bXAtcUtzczMOsJnipqZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZolwoJuZJcKBbmaWCAe6mVkiHOhmZonIFOiSJkhaL6lO0vQC/SMkPSvpLUk3Fb9MMzNrT4/2BkgqA+YA44B6oEZSdUS8kDfsFeCzwCc7o0gzM2tfliP0sUBdRGyIiN3AYmBi/oCI+H1E1ABNnVCjmZllkCXQBwGb87brc21mZnYIyRLoKtAWB7IzSVMk1UqqbWhoOJApzMysDVkCvR4YnLddDmw5kJ1FRFVEVEZE5cCBAw9kCjMza0OWQK8BhkuqkNQLmARUd25ZZmbWUe1+yyUimiVNA5YCZcD8iFgnaWquf66k9wG1wNHAXkmfB0ZFxI7OK93MzPK1G+gAEbEEWNKqbW7e49/SshRjZmYl4jNFzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwSkSnQJU2QtF5SnaTpBfol6du5/jWSPlj8Us3MbH/aDXRJZcAc4EJgFHC5pFGthl0IDM/9TAH+uch1mplZO7IcoY8F6iJiQ0TsBhYDE1uNmQh8J1o8Bxwr6fgi12pmZvvRI8OYQcDmvO164MwMYwYBL+cPkjSFliN4gEZJ6ztU7WFEMADY2mU7/Jq6bFfdgd+/w1c3eO/e31ZHlkAvVG0cwBgiogqoyrDPw56k2oioLHUddmD8/h2+uvN7l2XJpR4YnLddDmw5gDFmZtaJsgR6DTBcUoWkXsAkoLrVmGrgqty3XT4MbI+Il1tPZGZmnafdJZeIaJY0DVgKlAHzI2KdpKm5/rnAEuDjQB3wBnB155V82OgWS0sJ8/t3+Oq2750i9lnqNjOzw5DPFDUzS4QD3cwsEQ50M7NEONCt25M0QtKfSDqqVfuEUtVk2UkaK+mM3ONRkv6vpI+Xuq5S8IeinUzS1RHxr6WuwwqT9FngRuBF4DTgcxHxWK7vZxHhC80dwiR9lZZrSfUAfkzLWezLgD8FlkbEzNJV1/Uc6J1M0qaIGFLqOqwwST8HzoqIRklDgYeAhRHxLUkrI+L00lZo+5N7/04DegO/BcojYoek9wDLI2JMKevrallO/bd2SFrTVhfw3q6sxTqsLCIaASJio6TzgYckvZ/Cl7SwQ0tzROwB3pD0UkTsAIiINyXtLXFtXc6BXhzvBcYDr7ZqF/BM15djHfBbSadFxCqA3JH6RcB84JSSVmZZ7JbUNyLeAD70dqOkYwAHuh2Qx4Gj3g6FfJKWdXk11hFXAc35DRHRTMulLP6lNCVZB5wbEW8BRER+gPcE/ro0JZWO19DNzBLhry2amSXCgW5mlggHuiVL0h5JqyStlfSgpL77GXu7pJu6sj6zYnOgW8rejIjTIuJkYDcwtdQFmXUmB7p1Fz8FhgFIukrSGkmrJS1sPVDSdZJqcv0Pv31kL+nS3NH+aklP59pGS3o+95/AGknDu/RVmeXxt1wsWZIaI+IoST2Ah4EfAU8DjwDnRMRWScdFxCuSbgcaI2KWpP4RsS03x98Bv4uI2bmzEidExG8kHRsRr0maDTwXEYtyd/Qqi4g3S/KCrdvzEbql7D2SVgG1wCZgHnAB8FBEbAWIiFcKPO9kST/NBfiVwOhc+38DCyRdR8vduwCeBb4i6UvA+x3mVko+schS9mZEnJbfIElAe/+WLgA+GRGrJU0GzgeIiKmSzgT+DFiVO8P0u5KW59qWSro2Iv5fcV+GWTY+Qrfu5r+Av5TUH0DScQXG9ANeltSTliN0cmM/EBHLI+I2YCswWNIfARsi4tu03Cy9W10Myg4tPkK3biV3g/OZwE8k7QFWApNbDZsBLAd+DfycloAHuDv3oado+cOwGpgO/JWkJlqu9vf1Tn8RZm3wh6JmZonwkouZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpaI/wUZJp691xKdkwAAAABJRU5ErkJggg==", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#normalize=1 indica di condizionare rispetto alla prima variabile\n", "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=1).T.plot.bar()\n", "#normalize=1 indica di condizionare rispetto alla prima variabile\n", "pd.crosstab(titanic['Sex'], titanic['Pclass'], normalize=1).T.plot.bar(stacked=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Misure di Associazione tra due Variabili Discrete\n", "\n", "Vediamo adesso delle misure che ci permettono di stimare se due variabili sono o meno \"associate\", ovvero fino a che punto i valori di una variabile influenzano quelli dell'altra. Abbiamo visto che le variabili `Sex` e `Pclass` sembrano avere un qualche grado di associazione. Vediamo adesso un caso di associazione più forte, esplorando la relazione tra `Pclass` e `Survived`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consideriamo tre distribuzioni di frequenze relative di `Survived` condizionando rispetto ai valori `Pclass`:\n", "* f(Survived|Pclass=1)\n", "* f(Survived|Pclass=2)\n", "* f(Survived|Pclass=3)\n", "\n", "La tabella delle frequenze condizionate che segue riassume questi valori" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Survived01
Pclass
10.3703700.629630
20.5271740.472826
30.7576370.242363
\n", "
" ], "text/plain": [ "Survived 0 1\n", "Pclass \n", "1 0.370370 0.629630\n", "2 0.527174 0.472826\n", "3 0.757637 0.242363" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(titanic['Pclass'], titanic['Survived'], normalize=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dai numeri vediamo che le distribuzioni sono molto diverse, a seconda del condizionamento operato. Ciò non è soprendente, perché immaginiamo che i passeggeri in prima e seconda classe abbiano avuto un trattamento diverso rispetto a quelli in terza classe.\n", "\n", "Visualizziamo le distribuzioni graficamente:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": "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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.crosstab(titanic['Pclass'], titanic['Survived'], normalize=0).plot.bar(stacked=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il grafico mostra chiaramente lo sbilanciamento, per cui deduciamo che `Pclass` e `Survived` sono in effetti **associate** (o **correlate**).\n", "\n", "### Indipendenza e Frequenze Attese\n", "Prima di procedere con l'illustrare alcune misure di associazione tra variabili, dobbiamo parlare del concetto di **indipendenza**, che è opposto a quello di associazione. \n", "\n", "Due variabili si dicono indipendenti se l'osservazione di una variabile non ci permette di fare nessun tipo di predizione sui possibili valori dell'altra variabile.\n", "\n", "Immaginiamo due persone scelte a caso che vivono in parti diverse del mondo scelte casualmente. Se osserviamo il modo in cui una di queste due persone si veste, questa osservazione non ci dirà nulla sul modo in cui l'altra potrebbe vestirsi. Le variabili \"vestiti della prima persona\" e \"vestiti della seconda persona\" sono indipendenti. La stessa cosa non vale se le persone abitano nella stessa città. In tal caso, se piove, ad esempio, ci aspettiamo che le due persone indossino abiti simili, per cui osservare il modo in cui una delle due persone si veste può dirci qualcosa sul modo in cui si vestirà l'altra.\n", "\n", "Consideriamo nuovamente la generica tabella di contingenza:\n", "\n", "| | X=$x_1$ | X=$x_2$ | ... | X=$x_l$ | Total |\n", "|-------------|------------|------------|---------|-|-|\n", "| Y=$y_1$ | $n_{11}$ | $n_{12}$ | ... | $n_{1l}$ | $n_{1+}$ |\n", "| Y=$y_2$ | $n_{21}$ | $n_{22}$ | ... | $n_{2l}$ | $n_{2+}$ |\n", "| ... | ... | ... | ... | ...| ...| ...|\n", "| Y=$y_k$ | $n_{k1}$ | $n_{k2}$ | ... | $n_{kl}$ | $n_{k+}$ |\n", "| Total | $n_{+1}$ | $n_{+2}$ | ... | $n_{+l}$ | $n$ |\n", "\n", "Adesso supponiamo di non poter osservare le co-occorrenze, ma di poter osservare solo i valori marginali:\n", "\n", "| | X=$x_1$ | X=$x_2$ | ... | X=$x_l$ | Total |\n", "|-------------|------------|------------|---------|-|-|\n", "| Y=$y_1$ | | | ... | | $n_{1+}$ |\n", "| Y=$y_2$ | | | ... | | $n_{2+}$ |\n", "| ... | ... | ... | ... | ...| ...| ...|\n", "| Y=$y_k$ | | | ... | | $n_{k+}$ |\n", "| Total | $n_{+1}$ | $n_{+2}$ | ... | $n_{+l}$ | $n$ |\n", "\n", "Supponiamo adesso di dover \"ricostruire\" i valori mancanti. Se le due variabili sono indipendenti, allora:\n", "\n", "$$P(X=x_j, Y=y_i) = P(X=x_j) P(Y=y_i)$$\n", "\n", "Ricordiamo che:\n", "\n", "$$P(X=x_j) = \\frac{n_{+j}}{n}, P(Y=y_i) = \\frac{n_{i+}}{n}, P(X=x_j, Y=y_i)=\\frac{n_{ij}}{n}$$\n", "\n", "Da qui otteniamo che:\n", "\n", "$$\\tilde n_{ij} = n \\cdot P(X=x_j,Y=y_i) = n \\cdot P(X=x_j) \\cdot P(Y=y_i) = n \\cdot \\frac{n_{+j}}{n} \\frac{n_{i+}}{n} = \\frac{n_{+j}n_{i+}}{n}$$\n", "\n", "Da cui:\n", "\n", "$$\\tilde n_{ij} = P(X=x_j) n_{i+} = P(Y=y_i) n_{+j}$$\n", "\n", "Dove la tilde su $\\tilde n_{ij}$ indica che questo è un valore stimato, non il valore realmente osservato.\n", "\n", "> Quanto visto sopra suggerisce che le frequenze $n_{ij}$ seguando la stessa distribuzione delle frequenze marginali in caso di indipendenza tra le variabili." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Esempio\n", "Vediamo un esempio sulla nostra tabella di contingenza che mette in relazione `Pclass` e `Survived`:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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Survived01All
Pclass
180136216
29787184
3372119491
All549342891
\n", "
" ], "text/plain": [ "Survived 0 1 All\n", "Pclass \n", "1 80 136 216\n", "2 97 87 184\n", "3 372 119 491\n", "All 549 342 891" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(titanic['Pclass'], titanic['Survived'], margins=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La probabilità marginale $P(Survived)$ è data da:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/plain": [ "0 0.616162\n", "1 0.383838\n", "Name: Survived, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic['Survived'].value_counts(normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Queste sono le probabilità $P(Survived=0)$ e $P(Survived=1)$.\n", "\n", "Se le due variabili non fossero correlate, ci aspetteremmo di avere delle frequenze assolute proporzionali alle probabilità marginali:\n", "\n", "$$\\tilde{n}_{ij} = P(Y=y_j) \\cdot n_{i+}$$\n", "\n", "Questi valori saranno dati dalla seguente tabella:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
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Survived01All
Pclass
1133.09090982.909091216.0
2113.37373770.626263184.0
3302.535354188.464646491.0
All549.000000342.000000891.0
\n", "
" ], "text/plain": [ "Survived 0 1 All\n", "Pclass \n", "1 133.090909 82.909091 216.0\n", "2 113.373737 70.626263 184.0\n", "3 302.535354 188.464646 491.0\n", "All 549.000000 342.000000 891.0" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_ij = titanic['Survived'].value_counts(normalize=True).values\n", "n_iplus = titanic['Pclass'].value_counts().sort_index().values.reshape(-1,1)\n", "\n", "tab = f_ij*n_iplus\n", "ct = pd.crosstab(titanic['Pclass'], titanic['Survived'], margins=True).astype(float)\n", "ct.values[:-1,:-1] = tab[...]\n", "ct" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Confrontando queste frequenze con le frequenze effettive, ci accorgiamo che esistono delle discrepanze, ragionevolmente dovute al fatto che le variabili non sono in effetti indipendenti." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistica $\\mathcal{X}^2$ di Pearson\n", "La statistica $\\mathcal{X}^2$ di Pearson misura queste discrepanze con la seguente formula:\n", "\n", "$$\\mathcal{X}^2 = \\sum_{i=1}^k \\sum_{j=1}^l \\frac{(n_{ij}-\\tilde{n}_{ij})^2}{\\tilde{n}_{ij}}$$\n", "\n", "La formula sopra calcola le differenze tra le frequenze assolute osservate e attese al quadrato (per eliminare segni negativi) e scala il risultato per le frequenze attese. **Scalare il risultato fa si che piccole discrepanze ottenute su frequenze attese piccole pesino di più di piccole discrepanze ottenute tra frequenze attese grandi.**\n", "\n", "Si noti che se le frequenze attese sono identiche a quelle osservate, la statistica ottiene un valore pari a zero." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nell'esempio visto in precedenza, il risultato sarà pari a:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "102.89\n" ] } ], "source": [ "n_ij_pred = f_ij*n_iplus\n", "n_ij = pd.crosstab(titanic['Pclass'], titanic['Survived']).values\n", "\n", "chi_square = ((n_ij-n_ij_pred)**2/n_ij_pred).sum()\n", "print(f\"{chi_square:0.5}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistica $V$ di Cramer\n", "La statistica $\\mathcal{X}^2$ di Pearson non è normalizzata, nel senso che il suo valore massimo dipende dalla dimensione del campione. Per ovviare a questo problema, si può usare la statistica V di Cramer, definita come segue:\n", "\n", "$$V = \\sqrt{\\frac{\\mathcal{X}^2}{n(min(k,l)-1)}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il risultato sarà un valore compreso tra $0$ e $1$.\n", "\n", "La statistica V di Cramer sarà pari al seguente valore per l'esempio visto prima:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.34\n" ] } ], "source": [ "from scipy.stats.contingency import association\n", "\n", "print(f\"{association(pd.crosstab(titanic['Pclass'], titanic['Survived'])):0.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Questo valore indica una piccola associazione tra le due variabili, perché il valore è maggiore di $0$ ma comunque non prossimo a $1$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rischio Relativo\n", "\n", "Il rischio relativo è uno dei metodi di misurazione del grado di correlazione tra due variabili discrete più diffuso in epidemiologia. \n", "\n", "Vediamo un esempio (da [qui](https://en.wikipedia.org/wiki/Odds_ratio)): supponiamo che in un villaggio di $1000$ persone sia aumentata l'incidenza di una malattia rara. Investigando, scopriamo che recentemente una parte della popolazione è stata esposta a una radiazione.\n", "\n", "I dati relativi a soggetti malati e sani esposti o non esposti al rischio (la radiazione) viene riassunta dalla seguente tabella di contingenza che mette in relazione la variabile \"Diseased/Healthy\" con quella \"Exposed/Non Exposed\":" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ExposedNon Exposed
Diseased206
Healthy380594
\n", "
" ], "text/plain": [ " Exposed Non Exposed\n", "Diseased 20 6\n", "Healthy 380 594" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contingency = pd.DataFrame({\n", " \"Exposed\": [20, 380],\n", " \"Non Exposed\": [6,594]\n", "}, index=[\"Diseased\", \"Healthy\"])\n", "contingency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La tabella indica il numero di persone che hanno contratto una data malattia o meno in relazione al fatto che siano stati esposti a un determinato rischio o meno (es. a un elemento inquinante).\n", "\n", "Il rischio di sviluppare la malattia se esposti può essere calcolato con la seguente probabilità:\n", "\n", "$$P(Diseased|Exposed) = \\frac{\\#\\ (Diseased, Exposed)}{\\#\\ Exposed} = \\frac{20}{20+380} = 0.05$$\n", "\n", "Vediamo che abbiamo un rischio del $5\\%$ di sviluppare la malattia, se esposti alla radiazione. Ad esempio, tra $100$ persone esposte al rischio, ci aspettiamo di trovarne $5$ malate.\n", "\n", "Il rischio calcolato sopra, da solo, non ci dice molto sull'associazione tra le variabili \"Exposed\" e \"Non Exposed\". Consideriamo adesso la probabilità di contrarre la malattia se non esposti al rischio:\n", "\n", "$$P(Diseased|Non\\ Exposed) = \\frac{\\#\\ (Diseased, Non\\ Exposed)}{\\#\\ Non\\ Exposed} = \\frac{6}{6+594} = 0.01$$\n", "\n", "Questo valore ci dice che abbiamo un rischio di circa l'$1\\%$ di contrarre la malattia se non esposti. In pratica, su $100$ soggetti non esposti al rischio, $1$ si ammala.\n", "\n", "È utile mettere a confronto questi due valori di rischio. Infatti, sebbene il $5\\%$ di rischio di ammalarsi tra i soggetti esposti non sia in valore assoluto un numero molto alto, va anche detto che la proporzione di soggetti malati non esposti al rischio è molto più bassa ($1\\%$). Definiamo dunque il rischio relativo come il rapporto tra questi due rischi (o probabilità):\n", "\n", "$$\\textit{RR} = \\frac{P(Diseased|Exposed)}{P(Diseased|Non\\ Exposed)} = \\frac{\\frac{\\#\\ (Diseased, Exposed)}{\\#\\ Exposed}}{\\frac{\\#\\ (Diseased, Non\\ Exposed)}{\\#\\ Non\\ Exposed}} = \\frac{0.05}{0.1}=5$$\n", "\n", "Interpretiamo questo valore così:\n", "\n", "> La proporzione di soggetti che contraggono la malattia è di $5$ volte più grande tra coloro che sono stati esposti al rischio, rispetto al gruppo di coloro che non lo sono stati.\n", "\n", "Ad esempio, se tra $100$ persone non esposte al rischio, generalmente $10$ si ammalano (rischio del $10\\%$), allora ci aspettiamo che tra $100$ persone esposte al rischio, ben il $50\\%$ si ammali!\n", "\n", "Il rischio relativo non ci dice nulla sul rischio assoluto (la proporzione di persone che si ammala), ma solo su quanto osservare soggetti esposti al rischio, rispetto a osservare soggetti non esposti al rischio possa influenzare la proporzione di soggetti malati.\n", "\n", "In genere:\n", "* $RR=1$ indica che l'esposizione al rischio è ininfluente nello sviluppo della malattia (le variabili sono indipendenti);\n", "* $RR>1$ indica che l'esposizione al rischio è associata positivamente allo sviluppo della malattia (ci sono più malati tra i soggetti esposti al rischio);\n", "* $RR<1$ indica che l'esposizione al rischio è associata negativamente allo sviluppo della malattia (ci sono meno malati tra i soggetti esposti al rishcio). In questo caso si dice che il rischio \"protegge\" dalla comparsa della malattia (utile se il \"rischio\" è in realtà l'assunzione di un farmaco).\n", "\n", "**Attenzione al fatto che il rischio relativo misura una correlazione tra variabili, non un rapporto di causa-effetto, quindi non è sempre corretto dire che un in presenza di un rischio relativo maggiore di 1, allora l'esposizione al rischio \"causa\" la malattia. Esistono però degli strumenti (analisi causale) che ci permettono di verificare in quali casi possiamo dare questa interpretazione. Questi strumenti sono fondamentali per poter appurare se un determinato farmaco causa (o è co-responsabile di) una guarigione o se una determinata abitudine (es. il fumo) causa (o è co-responsabile della) comparsa di una malattia. Vedremo meglio qualche esempio più in là nel corso.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Odds Ratio\n", "\n", "Il rischio relativo non è sempre calcolabile. Infatti, nel caso precedente abbiamo assunto di avere a disposizione tutti i dati sulla popolazione: di tutti i $1000$ abitanti sapevamo chi era stato esposto al rischio e chi no e chi aveva sviluppato la malattia e chi no. In molti casi, potremmo non avere tutti questi dati. Ad esempio, immaginiamo che l'incidente sia avvenuto $50$ fa e che ai tempi sia stato intervistato solo un campione casuale del $50\\%$ dei $1000$ abitanti del villaggio. Immaginiamo di aver ottenere la seguente tabella di contingenza:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ExposedNon Exposed
Diseased206
Healthy1016
\n", "
" ], "text/plain": [ " Exposed Non Exposed\n", "Diseased 20 6\n", "Healthy 10 16" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contingency2 = pd.DataFrame({\n", " \"Exposed\": [20, 10],\n", " \"Non Exposed\": [6,16]\n", "}, index=[\"Diseased\", \"Healthy\"])\n", "contingency2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si noti che questo caso (quello del campionamento) sarà il più frequente, in quanto spesso non abbiamo accesso a tutta la popolazione, ma solo a un campione.\n", "\n", "In questo caso, non possiamo calcolare il rischio relativo in quanto non sono disponibili i numeri reali di persone esposte e non esposte al rischio, che stavano al denominatore delle espressioni del calcolo del rischio. Se applichiamo comunque la formula, otterremo una stima falsata dal nostro campionamento:\n", "\n", "$$\\textit{RR} = \\frac{f(Diseased|Exposed)}{f(Diseased|Non\\ Exposed)} = \\frac{\\frac{\\#\\ (Diseased, Exposed)}{\\#\\ Exposed}}{\\frac{\\#\\ (Diseased, Non\\ Exposed)}{\\#\\ Non\\ Exposed}} = \\frac{20/(20+10)}{6/(6+16)}=2.45$$\n", "\n", "Sebbene non possiamo calcolare il rischio relativo in quanto non conosciamo il numero assoluto di persone malate e esposte, **ci aspettiamo che il nostro campione sia una buona rappresentazione della popolazione**, per cui possiamo ragionare in termini di \"odds\".\n", "\n", "Gli odds sono un concetto di probabilità molto usato nell'ambito delle scommesse, perché permettono allo scommettitore di calcolare in modo facile la vinciata da incassare. Ad esempio, se la vittoria della squadra $A$ sulla squadra $B$ in una partita di calcio è data $5:1$, il bookmaker si aspetta che sia $5$ volte più probabile che vinca la squadra $A$ rispetto alla $B$. Se scometto la cifra di $2$ euro sulla squadra $B$ e la squadra $B$ vince, allora vincerò $5\\times 2 = 10$ euro (ho scomesso su un evento raro). L'odd in questo caso sarebbe definito come:\n", "\n", "$$odd = \\frac{5}{1} = 5$$\n", "\n", "Alternativamente, posso ragionare in termini di probabilità. Visto lo schema sopra, mi aspetto che, se giocassimo la partita $100$ volte, allora la probabilità di vittorie per la squadra $A$ sarebbe pari a $P(A\\ vince)=\\frac{5}{5+1}$, mentre la probabilità di sconfitta sarebbe pari a $P(A\\ perde)=\\frac{1}{5+1}$. L'odd può essere definito in termini di queste probabilità:\n", "\n", "$$odd = \\frac{P(A\\ vince)}{P(A\\ perde)} = \\frac{\\frac{5}{5+1}}{\\frac{1}{5+1}} = 5$$\n", "\n", "In generale, l'odd per un evento $E$ è la probabilità che l'evento accade, fratto la probabilità che l'evento non accada:\n", "\n", "$$odd = \\frac{P(E)}{1-P(E)}$$\n", "\n", "L'odd è un concetto simile a quello del rischio perché ci dice quanto è più grande la frequenza che un evento si realizzi (A vince) rispetto alla frequenza che l'evento non si realizzi. Applichiamo questo concetto al nostro esempio. Definiamo due odds (così come abbiamo definito due rischi):\n", "\n", "* L'odds di ammalarsi se esposti: $\\frac{P(Diseased|Exposed)}{P(Healthy|Exposed)} = \\frac{\\frac{20}{20+10}}{\\frac{10}{20+10}} = \\frac{20}{10} = 2$\n", "* L'odds di ammalarsi se non esposti: $\\frac{P(Diseased|Non\\ Exposed)}{P(Healthy|Non\\ Exposed)} = \\frac{\\frac{6}{6+16}}{\\frac{16}{6+16}} = \\frac{6}{16} 0.375$\n", "\n", "Va notato che nelle formule sopra i conteggi assoluti, ovvero quelli di cui non ci fidiamo dato il campionamento, \"si annullano\".\n", "\n", "L'odds ratio sarà definito come segue:\n", "\n", "$$odd = \\frac{\\frac{f(Diseased|Exposed)}{f(Healthy|Exposed)}}{\\frac{f(Diseased|Non\\ Exposed)}{f(Healthy|Non\\ Exposed)}} = \\frac{2}{0.375} \\approx 5.3$$\n", "\n", "Notiamo come l'odds ratio ottenuto sia molto vicino al rischio relativo calolcato in precedenza $5$. **In pratica si può dimostrare che, se si assume che la malattia sia rara (rare-disease assumption), l'odds ratio e il rischio relativo convergono a volori simili.** È quindi una pratica abbastanza comune di effettuare questa ipotesi e calcolare l'odds ratio al posto del rischio relativo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Associazioni tra Variabili Continue\n", "\n", "Quando le variabili sono continue, non è più possibile costruire tabelle di contingenza. In quei casi, si utilizzano altri strumenti, che vedremo in questa sezione.\n", "\n", "Considereremo il dataset \"diabetes\" per questa parte. Il dataset è stato introdotto nel $1979$ da Reaven e Miller ed esamina la relazione tra misure del sangue e l'insulina in $145$ adulti:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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relwtglufastglutestinstestsspggroup
00.818035612455Normal
10.959728911776Normal
20.94105319143105Normal
31.0490356199108Normal
41.0090323240143Normal
.....................
1401.05353142841480Overt_Diabetic
1410.9118092377150Overt_Diabetic
1420.90213102529209Overt_Diabetic
1431.113281246124442Overt_Diabetic
1440.74346156815253Overt_Diabetic
\n", "

145 rows × 6 columns

\n", "
" ], "text/plain": [ " relwt glufast glutest instest sspg group\n", "0 0.81 80 356 124 55 Normal\n", "1 0.95 97 289 117 76 Normal\n", "2 0.94 105 319 143 105 Normal\n", "3 1.04 90 356 199 108 Normal\n", "4 1.00 90 323 240 143 Normal\n", ".. ... ... ... ... ... ...\n", "140 1.05 353 1428 41 480 Overt_Diabetic\n", "141 0.91 180 923 77 150 Overt_Diabetic\n", "142 0.90 213 1025 29 209 Overt_Diabetic\n", "143 1.11 328 1246 124 442 Overt_Diabetic\n", "144 0.74 346 1568 15 253 Overt_Diabetic\n", "\n", "[145 rows x 6 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.datasets import get_rdataset\n", "data = get_rdataset('Diabetes','heplots').data\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le $6$ variabili hanno i seguenti significati:\n", "\n", "* ``relwt``\n", " relative weight, expressed as the ratio of actual weight to\n", " expected weight, given the person's height, a numeric vector\n", "\n", "* ``glufast``\n", " fasting plasma glucose level, a numeric vector\n", "\n", "* ``glutest``\n", " test plasma glucose level, a measure of glucose intolerance, a\n", " numeric vector\n", "\n", "* ``instest``\n", " plasma insulin during test, a measure of insulin response to oral\n", " glucose, a numeric vector\n", "\n", "* ``sspg``\n", " steady state plasma glucose, a measure of insulin resistance, a\n", " numeric vector\n", "\n", "* ``group``\n", " diagnostic group, a factor with levels ``Normal``\n", " ``Chemical_Diabetic`` ``Overt_Diabetic``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rappresentazione grafica di due variabili continue\n", "Iniziamo considerando le variabili `sspg` e `glutest`. La prima forma di rappresentazione utile è quella dello scatterplot, che visualizza le osservazioni come dati bidimensionali:\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "data.plot.scatter(x='sspg',y='glutest')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notiamo che, sembra esserci una relazione tra le due variabili. Infatti, al crescere di `sspg`, i valori di `glutest` tendono a crescere. Sembra esserci una **correlazione** (o **associazione**) positiva. Prendiamo adesso la coppia `relwt` e `sspg`:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.plot.scatter(x='relwt',y='sspg')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notiamo che in questo caso la relazione appare meno marcata, ma notiamo comunque un trend ascendente di `sspg` al crescere di `relwt`. È spesso utile cercare studiare le correlazioni con una **scattermatrix** che mostra tutti i possibili scatterplot. Nel caso del nostro esempio:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "sns.pairplot(data)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dalla visualizzazione sopra, possiamo dedurre alcune cose:\n", "* alcune coppie di variabili sono correlate positivamente (ad esempio `relwt`-`sspg`), mentre altre lo sono negativamente (`glutest`-`instest`);\n", "* alcune relazioni sono \"lineari\" (ad esempio `glutest` e `glufast`), mentre altre tengono a non esserlo (ad esempio `glufast` e `sspg`):\n", "* alcune coppie di variabili (ad esempio `sspg` e `relwt`) non sembrano essere correlate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Covarianza\n", "Abbiamo visto che la **varianza** indica quanto un campione univariato sia disperso attorno alla sua media. Possiamo utilizzare un'idea simile per quantificare fino a che punto **due variabili si distribuiscono in modo simile attorno alla loro media**. Ci aspettiamo che, se le variabili sono correlate, allora si distribuiranno in maniera simile. \n", "\n", "Il concetto di **varianza** viene generalizzato dal concetto di **covarianza** nel caso di dati bivariati. La covarianza misura quanto le due variabili varino assieme e si misura come segue:\n", "\n", "$$\n", "Cov(X,Y) = \\frac{1}{n}\\sum_i^n (x^{(i)} - \\overline x) (y^{(i)} - \\overline y)\n", "$$\n", "\n", "dove $X$ e $Y$ sono le due variabili di interesse, $x^{(i)}$ è il valore di $X$ per l'iesima osservazione, $y^{(i)}$ è il valore di $Y$ per l'iesima osservazione e $\\overline x$, $\\overline y$ sono le medie delle osservazioni per le due variabili $X$ e $Y$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I principali \"attori\" nella formula sopra sono i termini:\n", "\n", "* $(x^{(i)}-\\overline x)$: misura la distanza tra un punto $x^{(i)}$ e la rispettiva media $\\overline x$. Se il valore del punto in considerazione è sopra la media, allora questa differenza sarà positiva, altrimenti negativa.\n", "* $(y^{(i)}-\\overline y)$: misura la distanza tra un punto $y^{(i)}$ e la rispettiva media $\\overline y$. Se il valore del punto in considerazione è sopra la media, allora questa differenza sarà positiva, altrimenti negativa.\n", "\n", "Ogni termine della somma nella formula della covarianza effettua i prodotti tra questi due termini:\n", "\n", "$$(x^{(i)}-\\overline x)(y^{(i)}-\\overline y)$$\n", "\n", "Che saranno:\n", "* **positivi** se gli elementi $x^{(i)}$ e $y^{(i)}$ sono \"concordi\", ovvero se sono entrambi sopra le rispettive medie o entrambi sotto le rispettive medie;\n", "* **negativi** se gli elementi $x^{(i)}$ e $y^{(i)}$ sono \"discordi\", ovvero se uno dei due è sopra la rispettiva media, mentre l'altro è sotto la rispettiva media;\n", "* **nulli o prossimi allo zero** se gli elementi $x^{(i)}$ e $y^{(i)}$ sono molto vicini alle rispettive medie.\n", "\n", "Queste quantità hanno una interpretazione geometrica. Consideriamo il seguente plot:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.stats import pearsonr\n", "\n", "# Generate example data\n", "np.random.seed(42)\n", "x = np.random.rand(50)\n", "y = 2 * x + 1 + np.random.randn(50) # A linear relationship with some noise\n", "\n", "x*=50\n", "y*=40\n", "\n", "# Calculate the Covariance\n", "cov = np.cov(x,y)[0,1]\n", "\n", "# Calculate the means of the two samples\n", "mean_x = np.mean(x)\n", "mean_y = np.mean(y)\n", "\n", "# Create a scatter plot\n", "plt.figure(figsize=(8, 6))\n", "plt.scatter(x, y, label=f'Covariance: {cov:.2f}', color='blue')\n", "\n", "# Plot the means as horizontal and vertical lines\n", "plt.axhline(mean_y, color='green', linestyle='--', label=f'Mean Y: {mean_y:.2f}')\n", "plt.axvline(mean_x, color='orange', linestyle='--', label=f'Mean X: {mean_x:.2f}')\n", "\n", "# Add line segments from data points to the respective mean lines\n", "for i in range(len(x)):\n", " plt.plot([x[i], mean_x], [y[i], y[i]], 'r--', linewidth=0.8, alpha=0.7)\n", " plt.plot([x[i], x[i]], [y[i], mean_y], 'r--', linewidth=0.8, alpha=0.7)\n", "\n", "# Labels and title\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", "\n", "# Show legend\n", "plt.legend()\n", "\n", "# Display the plot\n", "plt.grid()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il grafico mostra un campione di punti e le rispettive medie con una linea orizzontale (per Y) e verticale (per X). Le due medie dividono il grafico in quattro quadranti. Per ogni punto, le linee tratteggiate rosse indicano i valori delle grandezze $(x^{(i)}-\\overline x)$ e $(y^{(i)}-\\overline y)$. In particolare:\n", "* I punti nel quadrante in alto a destra avranno entrambe queste grandezze positive. I prodotti $(x^{(i)}-\\overline x)(y^{(i)}-\\overline y)$ saranno positivi.\n", "* I punti nel quadrante in basso a destra avranno entrambe queste grandezze negative. I prodotti $(x^{(i)}-\\overline x)(y^{(i)}-\\overline y)$ saranno positivi.\n", "* I punti negli altri due quadranti avranno una delle grandezze positive e l'altra negativa. I prodotti $(x^{(i)}-\\overline x)(y^{(i)}-\\overline y)$ saranno negativi.\n", "\n", "In pratica:\n", "* i punti nel primo e terzo quadrante contribuiscono **positivamente** all'indice di correlazione;\n", "* i punti nel secondo e quarto quadrante contribuiscono **negativamente** all'indice di correlazione.\n", "* I punti vicini alla media (l'intersezione tra le due medie) corrisponderanno a grandezze molto piccole e **non contribuiranno** a cambiare il valore della covarianza." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Intuitivamente, la covarianza assumerà valori molto positivi quando $X$ e $Y$ variano in maniera concorde (ovvero, se $X$ assume valori alti, $Y$ assume valori alti e se $X$ assume valori bassi $Y$ assume valori bassi). In tal caso infatti i segni dei due fattori all'interno della sommatoria saranno concordi e il loro prodotto avrà segno positivo. Se invece $X$ e $Y$ variano in maniera discorde, il prodotto avrà segno negativo e la varianza assumerà valori negativi.\n", "\n", "Notiamo che la covarianza di una variabile $X$ con se stessa è uguale alla varianza:\n", "\n", "$$\n", "Cov(X,X) = \\frac{1}{n}\\sum_i^n (x_i - \\overline x) (x_i - \\overline x) = s_X\n", "$$\n", "\n", "\n", "Va notato che:\n", "* Valori negativi indicano una correlazione negativa (quando una delle due variabili cresce, l'altra diminuisce);\n", "* Valori positivi indicano una correlazione positiva (le due variabili crescono o decrescono insieme);\n", "* Valori nulli (o vicini allo zero) indicano che le due variabili non sono correlate (o lo sono debolmente).\n", "\n", "Va anche notato che i valori della correlazione non sono normalizzati e dipendono dai range delle singole variabili, per cui non è possibile confrontare le covarianze. Ad esempio, la covarianza tra `relwt` e se stessa (dunque la varianza di `relwt`) è prossima allo zero, tuttavia, ciò non vuol dire che `relwt` non sia correlata con se stessa!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In pratica, in presenza di più variabili, si calcolano le covarianze di tutte le coppie possibili di variabili, un po' come visto nel caso della scatter matrix. Nel nostro caso avremo:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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relwtglufastglutestinstestsspg
relwt0.016702-0.0728150.9824263.4733735.266255
glufast-0.0728154087.09703119546.064080-3063.4636494849.905651
glutest0.98242619546.064080100457.849808-12918.16273925908.490182
instest3.473373-3063.463649-12918.16273914625.312548101.482519
sspg5.2662554849.90565125908.490182101.48251911242.331897
\n", "
" ], "text/plain": [ " relwt glufast glutest instest sspg\n", "relwt 0.016702 -0.072815 0.982426 3.473373 5.266255\n", "glufast -0.072815 4087.097031 19546.064080 -3063.463649 4849.905651\n", "glutest 0.982426 19546.064080 100457.849808 -12918.162739 25908.490182\n", "instest 3.473373 -3063.463649 -12918.162739 14625.312548 101.482519\n", "sspg 5.266255 4849.905651 25908.490182 101.482519 11242.331897" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.cov()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La matrice vista sopra viene anche detta \"matrice di covarianza\" e indicata con la lettere $\\Sigma$. Date le variabili $X=(X_i,\\ldots,X_k)$, $\\Sigma$ è una matrice $[k \\times k]$ il cui termine generale è:\n", "\n", "$$\\Sigma_{ij} = Cov(X_i,X_j)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indice di Correlazione di Pearson\n", "\n", "Il coefficiente di correlazione di Pearson cerca di risolvere i problemi della covarianza fornendo uno score quantitativo normalizzato. Dato un campione bivariato $\\{(x^{(i)},y^{(i)})\\}$, il coefficiente di correlazione di Pearson è definito come la covarianza tra le due variabili, diviso il prodotto delle deviazioni standard:\n", "\n", "$$\n", "\\rho(x,y)= \\frac{Cov(X,Y)}{s_X s_Y} \n", "$$\n", "\n", "Notiamo che:\n", "\n", "$$\n", " \\rho(x,y) = \\frac{Cov(X,Y)}{s_X s_Y} = \\frac{1}{n} \\sum \\frac{(x_i - \\overline x)}{s_X} \\frac{(y_i - \\overline y)}{s_X} = Cov(z(X),z(Y))\n", "$$\n", "\n", "Dove\n", "\n", "$$\n", "z(X) = \\frac{X-\\overline X}{s_x}\n", "$$\n", "\n", "è la funzione di z-scoring.\n", "\n", "Possiamo dunque vedere l'indice di correlazione di Pearson come la covarianza delle variabili normalizzate con z-scoring. Si può facilmente vedere che il coefficiente di correlazione di Pearson può essere scritto anche come segue:\n", "\n", "$$\n", "\\rho(x,y) = \\frac{\\sum_{i=1}^n (x^{(i)}-\\overline x)(y^{(i)}-\\overline y)}{\\sqrt{\\sum_{i=1}^n (x^{(i)}-\\overline x)^2} \\sqrt{\\sum_{i=1}^n (y^{(i)}-\\overline y)^2}}\n", "$$\n", "\n", "Dove $\\overline x$ e $\\overline y$ sono rispettivamente le medie dei valori delle due variabili.\n", "\n", "Il grafico che segue mostra un plot analogo al precedente dopo aver effettuato lo z-scoring. Si noti come adesso i range di $x$ e $y$ sono normalizzati e la media si trova nell'origine." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.stats import pearsonr, zscore\n", "\n", "# Generate example data\n", "np.random.seed(42)\n", "x = np.random.rand(50)\n", "y = 2 * x + 1 + np.random.randn(50) # A linear relationship with some noise\n", "\n", "x = zscore(x)\n", "y = zscore(y)\n", "\n", "# Calculate the Covariance\n", "#cov = np.cov(x,y)[0,1]\n", "cov = pearsonr(x,y)[0]\n", "\n", "# Calculate the means of the two samples\n", "mean_x = np.mean(x)\n", "mean_y = np.mean(y)\n", "\n", "# Create a scatter plot\n", "plt.figure(figsize=(8, 6))\n", "plt.scatter(x, y, label=f'Pearson: {cov:.2f}', color='blue')\n", "\n", "# Plot the means as horizontal and vertical lines\n", "plt.axhline(mean_y, color='green', linestyle='--', label=f'Mean Y: {mean_y:.2f}')\n", "plt.axvline(mean_x, color='orange', linestyle='--', label=f'Mean X: {mean_x:.2f}')\n", "\n", "# Add line segments from data points to the respective mean lines\n", "for i in range(len(x)):\n", " plt.plot([x[i], mean_x], [y[i], y[i]], 'r--', linewidth=0.8, alpha=0.7)\n", " plt.plot([x[i], x[i]], [y[i], mean_y], 'r--', linewidth=0.8, alpha=0.7)\n", "\n", "# Labels and title\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", "\n", "# Show legend\n", "plt.legend()\n", "\n", "\n", "x = zscore(x)\n", "y = zscore(y)\n", "\n", "# Display the plot\n", "plt.grid()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'indice di correlazione di Pearson è un numero compreso tra $-1$ (massima anticorrelazione) e $1$ (massima correlazione).\n", "\n", "In pratica, si avrà:\n", "* $\\rho(x,-x)=-1$\n", "* $\\rho(x,x) = 1$\n", "\n", "L'indice di correlazione tra le variabili `relwt` e `sspg` sarà pari a:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.38\n" ] } ], "source": [ "from scipy.stats import pearsonr\n", "print(f\"{pearsonr(data['relwt'], data['sspg'])[0]:0.2f}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Interpretazione dei valori dell'indice di correlazione di Pearson\n", "L'indice di correlazione di Pearson è un numero compreso tra $-1$ e $1$ che indica se sussiste una correlazione (ovvero, quando i valori della prima variabile crescono, anche i valori della seconda variabile crescono) o un'anticorrelazione (ovvero, quando i valori della prima variabile crescono, i valori della seconda variabile decrescono e viceversa). Valori prossimi a $-1$ indicano che le variabili sono anticorrelate; valori prossimi a $1$ indicano che le variabili sono correlate; valori prossimi a $0$ indicano che le variabili sono decorrelate. In pratica:\n", " * Il segno dell'indice indica il \"verso\" della correlazione:\n", " * Positivo: le variabili sono correlate;\n", " * Negativo: le variabili sono anticorrelate;\n", " * Il valore assoluto dell'indice indica quanto la correlazione (o anticorrelazione) sia forte:\n", " * per valori compresi tra $0$ e $0.3$ si parla di correlazione (o anticorrelazione) **debole**;\n", " * per valori compresi tra $0.3$ e $0.7$ si parla di correlazione (o anticorrelazione) **moderata**;\n", " * per valori compresi tra $0.7$ e $1$ si parla di correlazione (o anticorrelazione) **forte**.\n", "\n", "Si noti che l'indice di correlazione di Pearson misura solo le **correlazioni lineari**. Pertanto, tra due variabili potrebbe sussistere una correlazione non lineare anche se l'indice di correlazione ottenuto è basso. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il seguente grafico mostra gli scatterplot di alcuni campioni bivariati di esempio con diversi gradi di correlazione, insieme ai relativi indici di correlazione di Pearson:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "np.random.seed(1234)\n", "\n", "x = np.random.rand(1000)\n", "y = np.random.rand(1000)\n", "\n", "campione_decorrelato = pd.DataFrame({'x':x,'y':y})\n", "\n", "campione_correlato = pd.DataFrame({'x':x,'y':2*x+8})\n", "campione_anticorrelato = pd.DataFrame({'x':x,'y':-2*x+3})\n", "\n", "campione_fortemente_correlato = pd.DataFrame({'x':x,'y':2*x+8+np.random.normal(0,0.3,1000)})\n", "campione_fortemente_anticorrelato = pd.DataFrame({'x':x,'y':-2*x+8+np.random.normal(0,0.3,1000)})\n", "\n", "campione_moderatamente_correlato = pd.DataFrame({'x':x,'y':2*x+8+np.random.normal(0,1,1000)})\n", "campione_moderatamente_anticorrelato = pd.DataFrame({'x':x,'y':-2*x+8+np.random.normal(0,1,1000)})\n", "\n", "campione_debolmente_correlato = pd.DataFrame({'x':x,'y':2*x+8+np.random.normal(0,2,1000)})\n", "campione_debolmente_anticorrelato = pd.DataFrame({'x':x,'y':-2*x+8+np.random.normal(0,2,1000)})\n", "\n", "plt.figure(figsize=(12,12))\n", "plt.subplot(331)\n", "s=campione_correlato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Correlato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(332)\n", "s=campione_decorrelato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Decorrelato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(333)\n", "s=campione_anticorrelato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Anticorrelato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(334)\n", "s=campione_fortemente_correlato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Fortemente correlato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(335)\n", "s=campione_moderatamente_correlato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Moderatamente correlato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(336)\n", "s=campione_debolmente_correlato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Debolmente correlato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(337)\n", "s=campione_fortemente_anticorrelato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Fortemente anticorrelato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(338)\n", "s=campione_moderatamente_anticorrelato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Moderatamente anticorrelato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(339)\n", "s=campione_debolmente_anticorrelato\n", "plt.scatter(s['x'],s['y'])\n", "plt.title(\"Debolmente anticorrelato (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Osservando i grafici mostrati sopra, possiamo notare che sussistono le seguenti relazioni tra indice e scatterplot:\n", " * Un indice positivo individua un andamento ascendente (una retta con coefficiente angolare positivo);\n", " * Un indice negativo individua un andamento discendente (una retta con coefficiente angolare negativo);\n", " * Il valore assoluto dell'indice è correlato alla \"larghezza\" del \"corridoio\" formato dai dati.\n", " \n", "Si noti che non esiste alcuna relazione tra indice di correlazione di Pearson e pendenza della retta. Possiamo vederlo nel seguente grafico:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "campione1 = pd.DataFrame({'x':x,'y':x+8})\n", "campione2 = pd.DataFrame({'x':x,'y':3*x+8})\n", "campione3 = pd.DataFrame({'x':x,'y':-2*x+8})\n", "\n", "plt.figure(figsize=(12,4))\n", "plt.subplot(131)\n", "s=campione1\n", "plt.scatter(s['x'],s['y'])\n", "plt.axis('equal')\n", "plt.title(\"Campione 1 (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(132)\n", "s=campione2\n", "plt.scatter(s['x'],s['y'])\n", "plt.axis('equal')\n", "plt.title(\"Campione 2 (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "\n", "plt.subplot(133)\n", "s=campione3\n", "plt.scatter(s['x'],s['y'])\n", "plt.axis('equal')\n", "plt.title(\"Campione 3 (%0.2f)\"%pearsonr(s['x'],s['y'])[0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indice di Correlazione di Spearman per ranghi\n", "\n", "L'indice di Pearson cattura le relazioni lineari tra le variabili. Esistono tuttavia dei casi in cui la relazione tra due variabili può non essere lineare, ma ha comunque senso cercare di capire se esiste una correlazione. \n", "\n", "Immaginiamo di avere una serie di utenti che assegnano dei punteggi a dei prodotti che recensiscono. Ogni utente userà una scala soggettiva diversa e probabilmente non lineare. L'indice di correlazione di Pearson non sarebbe il più adeguato per verificare se i punteggi assegnati da due utenti sono correlati. \n", "\n", "**L'indice di correlazione di Spearman cerca di risolvere questo problema passando dai punteggi ai ranghi**. Invece di ragionare sui punteggi individuali, l'indice di correlazione di Spearman ordina i prodotti per score e verifica se gli ordinamenti ottenuti sono simili. \n", "\n", "Consideriamo il campione $\\{(x^{(i)},y^{(i)})\\}_i$. Siano $R(x^{(i)})$ e $R(y^{(i)})$ i ranghi associati ai valori delle due variabili e sia $d_i=R(x^{(i)})$ - $R(y^{(i)})$. Il coefficiente di correlazione di Spearman per ranghi è definito come segue:\n", "\n", "$$ R = 1 - \\frac{6\\sum_{i=1}^n d_i^2}{n(n^2-1)} $$\n", "\n", "I valori del coefficiente di Spearman sono normalizzati tra $-1$ e $+1$ e hanno una interpretazione simile al coefficiente di Pearson.\n", "\n", "Consideriamo il seguente esempio. Chiediamo a Alice, Bob, Charlie e David di assegnare uno score da $1$ a $5$ a quattro gusti di gelato, ottenendo questi risultati:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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ParticipantChocolateVanillaStrawberryMintChip
0Alice5432
1Bob3542
2Charlie4352
3David2325
\n", "
" ], "text/plain": [ " Participant Chocolate Vanilla Strawberry MintChip\n", "0 Alice 5 4 3 2\n", "1 Bob 3 5 4 2\n", "2 Charlie 4 3 5 2\n", "3 David 2 3 2 5" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from scipy.stats import spearmanr\n", "\n", "# Create a DataFrame with ice cream flavor scores\n", "data = {\n", " 'Participant': ['Alice', 'Bob', 'Charlie', 'David'],\n", " 'Chocolate': [5, 3, 4, 2], # Scores out of 5\n", " 'Vanilla': [4, 5, 3, 3], # Scores out of 5\n", " 'Strawberry': [3, 4, 5, 2], # Scores out of 5\n", " 'MintChip': [2, 2, 2, 5] # Scores out of 5\n", "}\n", "\n", "df = pd.DataFrame(data)\n", "df\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dato che ogni bambino può usare una diversa scala, utilizziamo il coefficiente di Spearman ottenendo i seguenti valori:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spearman Correlation (Chocolate vs. Vanilla): 0.21\n", "Spearman Correlation (Chocolate vs. Strawberry): 0.40\n", "Spearman Correlation (Chocolate vs. MintChip): -0.77\n" ] } ], "source": [ "\n", "# Calculate the Spearman correlation coefficients\n", "spearman_corr_chocolate_vs_vanilla, _ = spearmanr(df['Chocolate'], df['Vanilla'])\n", "spearman_corr_chocolate_vs_strawberry, _ = spearmanr(df['Chocolate'], df['Strawberry'])\n", "spearman_corr_chocolate_vs_mintchip, _ = spearmanr(df['Chocolate'], df['MintChip'])\n", "\n", "# Calculate and display the Spearman correlation coefficients\n", "print(f\"Spearman Correlation (Chocolate vs. Vanilla): {spearman_corr_chocolate_vs_vanilla:.2f}\")\n", "print(f\"Spearman Correlation (Chocolate vs. Strawberry): {spearman_corr_chocolate_vs_strawberry:.2f}\")\n", "print(f\"Spearman Correlation (Chocolate vs. MintChip): {spearman_corr_chocolate_vs_mintchip:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Questi valori ci dicono che il gusto di gelati che ha ricevuto score \"più simili\" al cioccolato è la fragola.\n", "\n", "A differenza del coefficiente di Pearson, l'indice di Spearman può catturare relazioni non lineari, come mostrato nell'esempio che segue:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.stats import spearmanr, pearsonr\n", "\n", "# Generate non-linear data\n", "np.random.seed(42)\n", "x = np.linspace(-8, 20, 100)\n", "#y = x**2 + np.random.randn(50) # A non-linear relationship with some noise\n", "y = x**3-2*(1+np.exp(-x)) + 0.01*np.random.randn(100) \n", "\n", "y, x = x, y\n", "\n", "# Calculate the Spearman and Pearson correlation coefficients\n", "spearman_corr, _ = spearmanr(x, y)\n", "pearson_corr, _ = pearsonr(x, y)\n", "\n", "# Create a scatter plot\n", "plt.figure(figsize=(8, 6))\n", "plt.scatter(x, y, label=f'Spearman Correlation: {spearman_corr:.2f}\\nPearson Correlation: {pearson_corr:.2f}', color='blue')\n", "\n", "# Labels and title\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", "plt.title('Scatter Plot with Correlation Coefficients')\n", "\n", "# Show legend\n", "plt.legend()\n", "\n", "# Display the plot\n", "plt.grid()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indice di Correlazione di Kendall (Opzionale)\n", "\n", "L'indice di correlazione per ranghi di Kendall, detto anche **Kendall's $\\tau$**, è anch'esso usato per misurare il livello di associazione tra due variabili, considerandone i ranghi. \n", "\n", "Consideriamo il campione $\\{(x^{(i)},y^{(i)})\\}_i$. Ogni coppia di osservazioni $(x_i,y_i)$ e $(x_j,y_j)$, con $ix_j$ e $y_i>y_j$ o $x_iy_j$ o $x_i>x_j$ e $y_i" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from scipy.stats import kendalltau\n", "\n", "# Sample data\n", "x = [1, 2, 3, 4]\n", "y = [3, 1, 2, 5]\n", "\n", "# Calculate Kendall tau coefficient\n", "tau, _ = kendalltau(x, y)\n", "\n", "# Create lists to store concordant and discordant pairs\n", "concordant_pairs_x = []\n", "concordant_pairs_y = []\n", "discordant_pairs_x = []\n", "discordant_pairs_y = []\n", "\n", "# Determine concordant and discordant pairs\n", "for i in range(len(x)):\n", " for j in range(i+1, len(y)):\n", " if (x[i] - x[j]) * (y[i] - y[j]) > 0:\n", " concordant_pairs_x.append([x[i], x[j]])\n", " concordant_pairs_y.append([y[i], y[j]])\n", " else:\n", " discordant_pairs_x.append([x[i], x[j]])\n", " discordant_pairs_y.append([y[i], y[j]])\n", "\n", "# Create a scatter plot\n", "plt.figure(figsize=(6, 6))\n", "plt.scatter(x, y, c='b', marker='o', label='Data Points')\n", "\n", "# Plot concordant pairs in green\n", "for i in range(len(concordant_pairs_x)):\n", " plt.plot(concordant_pairs_x[i], concordant_pairs_y[i], c='g', linestyle='-', linewidth=1, alpha=0.5, label='Concordant')\n", "\n", "# Plot discordant pairs in red\n", "for i in range(len(discordant_pairs_x)):\n", " plt.plot(discordant_pairs_x[i], discordant_pairs_y[i], c='r', linestyle='-', linewidth=1, alpha=0.5, label='Discordant')\n", "\n", "# Set labels and title\n", "plt.xlabel('X')\n", "plt.ylabel('Y')\n", "plt.title(f'Kendall Tau Coefficient = {tau:.2f}')\n", "\n", "# Show the legend\n", "plt.legend()\n", "\n", "# Show the plot\n", "plt.grid(True)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matrice di Correlazione" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analogamente a quanto visto nel caso degli scatterplot, quando abbiamo più variabili, possiamo calcolare gli indici di correlazione tra tutte le variabili del dataset. Otteniamo in questo modo una \"matrice di correlazione\". La matrice di correlazione calcolata mediante il coefficiente di Pearson, nel caso del nostro dataset `diabetes` sarà:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "text/html": [ "
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relwtglufastglutestinstestsspg
relwt1.000000-0.0088130.0239840.2222380.384320
glufast-0.0088131.0000000.964628-0.3962350.715480
glutest0.0239840.9646281.000000-0.3370200.770942
instest0.222238-0.396235-0.3370201.0000000.007914
sspg0.3843200.7154800.7709420.0079141.000000
\n", "
" ], "text/plain": [ " relwt glufast glutest instest sspg\n", "relwt 1.000000 -0.008813 0.023984 0.222238 0.384320\n", "glufast -0.008813 1.000000 0.964628 -0.396235 0.715480\n", "glutest 0.023984 0.964628 1.000000 -0.337020 0.770942\n", "instest 0.222238 -0.396235 -0.337020 1.000000 0.007914\n", "sspg 0.384320 0.715480 0.770942 0.007914 1.000000" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.datasets import get_rdataset\n", "data = get_rdataset('Diabetes','heplots').data\n", "data.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esiste anche una rappresentazione grafica della matrice di correlazione, spesso detta \"correlation plot\":" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "tags": [ "remove-input" ] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(data.corr(), annot=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Referenze\n", "* Capitolo 3 di: Heumann, Christian, and Michael Schomaker Shalabh. Introduction to statistics and data analysis. Springer International Publishing Switzerland, 2016.\n", "* https://en.wikipedia.org/wiki/Odds_ratio" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 1 }