{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PadrãoNovo
count40.00000040.000000
mean95.300000104.575000
std13.95266715.807962
min65.00000075.000000
25%85.00000090.000000
50%96.500000106.500000
75%104.500000116.000000
max120.000000133.000000
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" ], "text/plain": [ " Padrão Novo\n", "count 40.000000 40.000000\n", "mean 95.300000 104.575000\n", "std 13.952667 15.807962\n", "min 65.000000 75.000000\n", "25% 85.000000 90.000000\n", "50% 96.500000 106.500000\n", "75% 104.500000 116.000000\n", "max 120.000000 133.000000" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import norm\n", "import statistics\n", "import pandas as pd\n", "from scipy import stats\n", "from scipy.stats import norm\n", "\n", "# Dados de entrada\n", "df = pd.read_csv(\"C:/Users/Mariana/Dropbox (Pessoal)/1º sem 2020/MBA/Dados/ALAT.csv\", sep = ',', na_values = '-', encoding= 'unicode_escape')\n", "padrao = df[\"Padrão\"]\n", "novo = df[\"Novo\"]\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(96.5, 95.3, 13.952667238808612, 65, 120)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Descreve a variável padrao\n", "np.median(padrao), np.mean(padrao), np.std(padrao,ddof=1), np.min(padrao), np.max(padrao)" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(106.5, 104.575, 15.807962128834527, 75, 133)" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Descreve a variável novo\n", "np.median(novo), np.mean(novo), np.std(novo,ddof=1), np.min(novo), np.max(novo)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LeveneResult(statistic=0.5839724701580812, pvalue=0.44706504115188817)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Teste de Levene para igualdade de variâncias\n", "stats.levene(padrao,novo)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=-2.782111467812002, pvalue=0.006770071773719045)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Faz um teste t de Student comparando médias de novo e padrao\n", "stats.ttest_ind(padrao,novo, equal_var = True)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gráfico de probabilidade da Normal, para checar suposição de tal distribuição dos dados\n", "stats.probplot(novo, plot=plt)\n", "stats.probplot(padrao, plot=plt)\n", "plt.xlabel('quantis teóricos')\n", "plt.ylabel('valores ordenados da variável')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Repare que há dependência entre os dados observados num mesmo cão. O próprio gráfico reflete isso, além de ser uma constatação intuitiva. Por essa razão, deve-se pensar em trabalhar com as diferenças dos pares de valores." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Experimento ALAT')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dif = padrao-novo\n", "media = (padrao+novo)/2\n", "\n", "# Cálculo dos limites de precisã, levando em consideração os erros de medida dos métodos\n", "inf = [-25*.0882,-175*.082]\n", "sup = [25*.0882,175*.0882]\n", "ax = [25,175]\n", "\n", "# Gráfico da diferença em função da média dos métodos\n", "plt.scatter(media,dif)\n", "plt.hlines(0,xmin=25,xmax=175,color='g')\n", "plt.plot(ax,inf,'g:')\n", "plt.plot(ax,sup,'g:')\n", "plt.xlabel(\"média dos métodos\")\n", "plt.ylabel(\"diferença dos métodos (padrão-novo)\")\n", "plt.title(\"Experimento ALAT\")" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-9.275, 3.4714033222374243)" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(dif), np.std(dif,ddof=1)" ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_relResult(statistic=-16.898137482427465, pvalue=1.5340303096582e-19)" ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Faz um teste t de Student PAREADO comparando médias de novo e padrao\n", "stats.ttest_rel(padrao,novo) " ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_1sampResult(statistic=-16.898137482427465, pvalue=1.5340303096582e-19)" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# O t de Student PAREADO é equivalente a testar se a média da diferença entre as variáveis é zero\n", "stats.ttest_1samp(dif, 0)" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gráfico de probabilidade da Normal, para checar suposição de tal distribuição dos dados\n", "stats.probplot(dif, plot=plt)\n", "plt.xlabel('quantis teóricos')\n", "plt.ylabel('valores ordenados da variável padrão-novo')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se a suposição de normalidade não for satisfeita, pode-se fazer o teste de sinais de Wilcoxon (para dados pareados)" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WilcoxonResult(statistic=2.0, pvalue=3.937981800408753e-08)" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Teste de sinais de Wilcoxon sign-rank Wilcoxon test)\n", "stats.wilcoxon(padrao,novo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "O teste não paramétrico equivalente ao t de Student para duas amostras independentes é o teste de Mann–Whitney (ou sum rank test)\n", "Pode-se usar o comando scipy.stats.mannwhitneyu() para realizá-lo" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Constrói o gráfico de dispersão\n", "plt.scatter(x=padrao,y=novo)\n", "plt.xlabel(\"método padrão\")\n", "plt.ylabel(\"novo método\")\n", "plt.title(\"Experimento ALAT\")\n", "\n", "# Desenha a reta de igualdade das duas medidas\n", "plt.plot(padrao, padrao, 'k-', color = 'r', label=\"x=y\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coeficiente de correlação: 0.9804851386843999\n", "p-valor (H0: correlação igual a 0): 1.8746448220612426e-28\n" ] } ], "source": [ "#Calcula o coeficiente de correlação de Pearson\n", "cor,pvalor = stats.pearsonr(padrao,novo)\n", "print('Coeficiente de correlação:' ,cor)\n", "print('p-valor (H0: correlação igual a 0):' ,pvalor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Coeficiente de correlação de concordância\n", " Mede concordância\n", " Mais detalhes em: https://en.wikipedia.org/wiki/Concordance_correlation_coefficient\n", " Artigo: Lawrence, I., and Kuei Lin. \"A concordance correlation coefficient to evaluate\n", " reproducibility.\" Biometrics (1989): 255-268. \n", " Parameters\n", " -----\n", " Interpretação: Valor entre [-1,1], sendo 1 concordância perfeita, similar ao coeficiente\n", " de correlação de Pearson" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coeficiente de concordância: 0.8117829367829367\n" ] } ], "source": [ "def coef_concord(y_true, y_pred,\n", " sample_weight=None,\n", " multioutput='uniform_average'):\n", " cor,sig = stats.pearsonr(y_true,y_pred)\n", " \n", " mean_true=np.mean(y_true)\n", " mean_pred=np.mean(y_pred)\n", " \n", " var_true=np.var(y_true)\n", " var_pred=np.var(y_pred)\n", " \n", " sd_true=np.std(y_true)\n", " sd_pred=np.std(y_pred)\n", " \n", " numerator=2*cor*sd_true*sd_pred\n", "\n", " denominator=var_true+var_pred+(mean_true-mean_pred)**2\n", "\n", " return numerator/denominator\n", "\n", "print('Coeficiente de concordância:' ,coef_concord(novo,padrao))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ajustando o modelo de regressão linear simples" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Novo R-squared: 0.961\n", "Model: OLS Adj. R-squared: 0.960\n", "Method: Least Squares F-statistic: 945.2\n", "Date: Thu, 21 May 2020 Prob (F-statistic): 1.87e-28\n", "Time: 17:28:43 Log-Likelihood: -101.61\n", "No. Observations: 40 AIC: 207.2\n", "Df Residuals: 38 BIC: 210.6\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -1.2900 3.479 -0.371 0.713 -8.333 5.753\n", "Padrão 1.1109 0.036 30.744 0.000 1.038 1.184\n", "==============================================================================\n", "Omnibus: 11.680 Durbin-Watson: 1.644\n", "Prob(Omnibus): 0.003 Jarque-Bera (JB): 12.554\n", "Skew: -0.977 Prob(JB): 0.00188\n", "Kurtosis: 4.928 Cond. No. 673.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "from statsmodels.formula.api import ols\n", "\n", "mod = ols('Novo ~ Padrão',data=df)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testes de hipóteses:" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(padrao, novo, 'o', label=\"data\")\n", "ax.plot(padrao, res.fittedvalues, 'r-', label=\"E(Novo)=-1,3 + 1,11*Padrão\")\n", "ax.plot(padrao, padrao, 'k-', color = 'g', label=\"x=y\")\n", "plt.xlabel(\"método padrão\")\n", "plt.ylabel(\"novo método\")\n", "plt.title(\"Experimento ALAT\")\n", "ax.legend(loc='best');" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Test for Constraints \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "c0 -1.2900 3.479 -0.371 0.713 -8.333 5.753\n", "==============================================================================\n", " Test for Constraints \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "c0 1.1109 0.036 30.744 0.000 1.038 1.184\n", "==============================================================================\n" ] }, { "data": { "text/plain": [ "(None, None)" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(res.t_test([1, 0])), print(res.t_test([0, 1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testes de hipóteses conjuntos sobre os parâmetros do modelo" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0 1]\n", " [1 0]]\n", "\n" ] } ], "source": [ "# H0: intercepto = Padrão = 0\n", "R = [[0, 1], [1, 0]]\n", "print(np.array(R))\n", "print(res.f_test(R))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 0]]\n", "\n" ] } ], "source": [ "# H0: intercepto = 0 (repare que este teste é equivalente ao teste com a estatística t: F=t*t)\n", "R = [[1, 0]]\n", "print(np.array(R))\n", "print(res.f_test(R))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# H0: Padrão = 1\n", "print(res.f_test(\"Padrão = 1\"))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "hypotheses = '(Padrão = 1),(Intercept = 0)'\n", "print(res.f_test(hypotheses))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Obtendo os valores preditos (valores ajustados):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ypred=res.fittedvalues\n", "residuo = res.resid # este é o resíduo=observado-ajustado=novo-ypred\n", "plt.scatter(ypred,residuo, color='r')\n", "plt.hlines(0,xmin=min(ypred),xmax=max(ypred),color='g')\n", "plt.ylabel(\"resíduo (observado-ajustado)\")\n", "plt.xlabel(r'$\\hat{y}$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gráfico de probabilidade da Normal dos resíduos\n", "stats.probplot(residuo, plot=plt)\n", "plt.xlabel('quantis teóricos')\n", "plt.ylabel('resíduos ordenados')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Encontrando (Raiz) Erro Quadrático Médio" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Raiz MSE: 3.0686455212903536\n" ] } ], "source": [ "from statsmodels.tools.eval_measures import rmse\n", "\n", "rmse = rmse(df.Novo, ypred)\n", "print(\"Raiz MSE: \", rmse)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Resíduos padronizado (dividido por rmse)\n", "std_res = residuo/rmse\n", "plt.scatter(ypred,std_res, color='r')\n", "plt.hlines(0,xmin=min(ypred),xmax=max(ypred),color='g')\n", "plt.hlines(-2,xmin=min(ypred),xmax=max(ypred),color='g',linestyles='dotted')\n", "plt.hlines(2,xmin=min(ypred),xmax=max(ypred),color='g',linestyles='dotted')\n", "plt.ylabel(\"resíduo padronizado\")\n", "plt.xlabel(r'$\\hat{y}$')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }