From 9ff805ec8b6320ca5d1a17b2012d223a63277fe0 Mon Sep 17 00:00:00 2001 From: Ivan Savov Date: Wed, 6 Mar 2024 03:41:19 -0500 Subject: [PATCH] Added CH4 notebook headings + plot_lm_ttest + plot_lm_anova --- _toc.yml | 2 +- notebooks/41_simple_linear_regression.ipynb | 69 +- notebooks/42_multiple_linear_regression.ipynb | 12 +- notebooks/43_interpreting_linear_models.ipynb | 831 ++++++++++++++++-- ...gression_with_categorical_predictors.ipynb | 166 +++- ...ounders.ipynb => 45_model_selection.ipynb} | 4 +- notebooks/cut_material.ipynb | 24 +- notebooks/plot_helpers.py | 129 +++ notebooks/stats_helpers.py | 6 + 9 files changed, 1091 insertions(+), 152 deletions(-) rename notebooks/{45_causal_effects_and_confounders.ipynb => 45_model_selection.ipynb} (99%) diff --git a/_toc.yml b/_toc.yml index 60873feb..72146197 100644 --- a/_toc.yml +++ b/_toc.yml @@ -53,7 +53,7 @@ sections: - file: notebooks/42_multiple_linear_regression.ipynb - file: notebooks/43_interpreting_linear_models.ipynb - file: notebooks/44_regression_with_categorical_predictors.ipynb - - file: notebooks/45_causal_effects_and_confounders.ipynb + - file: notebooks/45_model_selection.ipynb - file: notebooks/46_generalized_linear_models.ipynb - file: 50_BAYESIAN_STATS.md - file: tutorials/appendix.md diff --git a/notebooks/41_simple_linear_regression.ipynb b/notebooks/41_simple_linear_regression.ipynb index 56850a95..52b82a85 100644 --- a/notebooks/41_simple_linear_regression.ipynb +++ b/notebooks/41_simple_linear_regression.ipynb @@ -810,10 +810,10 @@ " Method: Least Squares F-statistic: 44.37\n", "\n", "\n", - " Date: Wed, 28 Feb 2024 Prob (F-statistic): 1.56e-05\n", + " Date: Sat, 02 Mar 2024 Prob (F-statistic): 1.56e-05\n", "\n", "\n", - " Time: 07:46:54 Log-Likelihood: -44.140\n", + " Time: 11:01:30 Log-Likelihood: -44.140\n", "\n", "\n", " No. Observations: 15 AIC: 92.28\n", @@ -861,8 +861,8 @@ "\\textbf{Dep. Variable:} & score & \\textbf{ R-squared: } & 0.773 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.756 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 44.37 \\\\\n", - "\\textbf{Date:} & Wed, 28 Feb 2024 & \\textbf{ Prob (F-statistic):} & 1.56e-05 \\\\\n", - "\\textbf{Time:} & 07:46:54 & \\textbf{ Log-Likelihood: } & -44.140 \\\\\n", + "\\textbf{Date:} & Sat, 02 Mar 2024 & \\textbf{ Prob (F-statistic):} & 1.56e-05 \\\\\n", + "\\textbf{Time:} & 11:01:30 & \\textbf{ Log-Likelihood: } & -44.140 \\\\\n", "\\textbf{No. Observations:} & 15 & \\textbf{ AIC: } & 92.28 \\\\\n", "\\textbf{Df Residuals:} & 13 & \\textbf{ BIC: } & 93.70 \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -897,8 +897,8 @@ "Dep. Variable: score R-squared: 0.773\n", "Model: OLS Adj. R-squared: 0.756\n", "Method: Least Squares F-statistic: 44.37\n", - "Date: Wed, 28 Feb 2024 Prob (F-statistic): 1.56e-05\n", - "Time: 07:46:54 Log-Likelihood: -44.140\n", + "Date: Sat, 02 Mar 2024 Prob (F-statistic): 1.56e-05\n", + "Time: 11:01:30 Log-Likelihood: -44.140\n", "No. Observations: 15 AIC: 92.28\n", "Df Residuals: 13 BIC: 93.70\n", "Df Model: 1 \n", @@ -963,11 +963,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "20871151-8003-4496-8767-04b229af274f", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 274, + "width": 431 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from plot_helpers import simple_regplot\n", + "\n", + "efforts = students[\"effort\"]\n", + "scores = students[\"score\"]\n", + "\n", + "simple_regplot(efforts, scores)" + ] }, { "cell_type": "code", @@ -1003,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "id": "e59e24ff-9929-470a-a806-9ee34bd6f8b5", "metadata": {}, "outputs": [ @@ -1013,7 +1046,7 @@ "4.504850344209071" ] }, - "execution_count": 25, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1022,18 +1055,18 @@ "efforts = students[\"effort\"]\n", "scores = students[\"score\"]\n", "\n", - "effortsbar = efforts.mean()\n", - "scoresbar = scores.mean()\n", + "meaneffort = efforts.mean()\n", + "meanscore = scores.mean()\n", "#######################################################\n", - "num = sum((efforts - effortsbar)*(scores - scoresbar))\n", - "denom = sum( (efforts - effortsbar)**2 )\n", + "num = sum((efforts - meaneffort)*(scores - meanscore))\n", + "denom = sum( (efforts - meaneffort)**2 )\n", "b1 = num / denom\n", "b1" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "id": "b5c61dcc-5a64-4bb7-b1e7-018a327b1dc1", "metadata": {}, "outputs": [], @@ -1044,7 +1077,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "id": "d981271d-23e5-41ea-b183-bf2e100aaa42", "metadata": {}, "outputs": [ @@ -1054,7 +1087,7 @@ "32.46580930159963" ] }, - "execution_count": 27, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/42_multiple_linear_regression.ipynb b/notebooks/42_multiple_linear_regression.ipynb index a83f853d..d16b5c00 100644 --- a/notebooks/42_multiple_linear_regression.ipynb +++ b/notebooks/42_multiple_linear_regression.ipynb @@ -601,10 +601,10 @@ " Method: Least Squares F-statistic: 270.3\n", "\n", "\n", - " Date: Wed, 28 Feb 2024 Prob (F-statistic): 1.05e-60\n", + " Date: Sat, 02 Mar 2024 Prob (F-statistic): 1.05e-60\n", "\n", "\n", - " Time: 07:46:51 Log-Likelihood: -547.63\n", + " Time: 11:35:07 Log-Likelihood: -547.63\n", "\n", "\n", " No. Observations: 156 AIC: 1103.\n", @@ -658,8 +658,8 @@ "\\textbf{Dep. Variable:} & score & \\textbf{ R-squared: } & 0.842 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.839 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 270.3 \\\\\n", - "\\textbf{Date:} & Wed, 28 Feb 2024 & \\textbf{ Prob (F-statistic):} & 1.05e-60 \\\\\n", - "\\textbf{Time:} & 07:46:51 & \\textbf{ Log-Likelihood: } & -547.63 \\\\\n", + "\\textbf{Date:} & Sat, 02 Mar 2024 & \\textbf{ Prob (F-statistic):} & 1.05e-60 \\\\\n", + "\\textbf{Time:} & 11:35:07 & \\textbf{ Log-Likelihood: } & -547.63 \\\\\n", "\\textbf{No. Observations:} & 156 & \\textbf{ AIC: } & 1103. \\\\\n", "\\textbf{Df Residuals:} & 152 & \\textbf{ BIC: } & 1115. \\\\\n", "\\textbf{Df Model:} & 3 & \\textbf{ } & \\\\\n", @@ -696,8 +696,8 @@ "Dep. Variable: score R-squared: 0.842\n", "Model: OLS Adj. R-squared: 0.839\n", "Method: Least Squares F-statistic: 270.3\n", - "Date: Wed, 28 Feb 2024 Prob (F-statistic): 1.05e-60\n", - "Time: 07:46:51 Log-Likelihood: -547.63\n", + "Date: Sat, 02 Mar 2024 Prob (F-statistic): 1.05e-60\n", + "Time: 11:35:07 Log-Likelihood: -547.63\n", "No. Observations: 156 AIC: 1103.\n", "Df Residuals: 152 BIC: 1115.\n", "Df Model: 3 \n", diff --git a/notebooks/43_interpreting_linear_models.ipynb b/notebooks/43_interpreting_linear_models.ipynb index 6ff56668..7ce36399 100644 --- a/notebooks/43_interpreting_linear_models.ipynb +++ b/notebooks/43_interpreting_linear_models.ipynb @@ -124,10 +124,10 @@ " Method: Least Squares F-statistic: 270.3\n", "\n", "\n", - " Date: Wed, 28 Feb 2024 Prob (F-statistic): 1.05e-60\n", + " Date: Sat, 02 Mar 2024 Prob (F-statistic): 1.05e-60\n", "\n", "\n", - " Time: 07:46:50 Log-Likelihood: -547.63\n", + " Time: 11:35:28 Log-Likelihood: -547.63\n", "\n", "\n", " No. Observations: 156 AIC: 1103.\n", @@ -181,8 +181,8 @@ "\\textbf{Dep. Variable:} & score & \\textbf{ R-squared: } & 0.842 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.839 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 270.3 \\\\\n", - "\\textbf{Date:} & Wed, 28 Feb 2024 & \\textbf{ Prob (F-statistic):} & 1.05e-60 \\\\\n", - "\\textbf{Time:} & 07:46:50 & \\textbf{ Log-Likelihood: } & -547.63 \\\\\n", + "\\textbf{Date:} & Sat, 02 Mar 2024 & \\textbf{ Prob (F-statistic):} & 1.05e-60 \\\\\n", + "\\textbf{Time:} & 11:35:28 & \\textbf{ Log-Likelihood: } & -547.63 \\\\\n", "\\textbf{No. Observations:} & 156 & \\textbf{ AIC: } & 1103. \\\\\n", "\\textbf{Df Residuals:} & 152 & \\textbf{ BIC: } & 1115. \\\\\n", "\\textbf{Df Model:} & 3 & \\textbf{ } & \\\\\n", @@ -219,8 +219,8 @@ "Dep. Variable: score R-squared: 0.842\n", "Model: OLS Adj. R-squared: 0.839\n", "Method: Least Squares F-statistic: 270.3\n", - "Date: Wed, 28 Feb 2024 Prob (F-statistic): 1.05e-60\n", - "Time: 07:46:50 Log-Likelihood: -547.63\n", + "Date: Sat, 02 Mar 2024 Prob (F-statistic): 1.05e-60\n", + "Time: 11:35:28 Log-Likelihood: -547.63\n", "No. Observations: 156 AIC: 1103.\n", "Df Residuals: 152 BIC: 1115.\n", "Df Model: 3 \n", @@ -259,6 +259,14 @@ "lm2.summary()" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "af5c29a4-96e7-4b3e-a962-74043ae47417", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "21949eb7-b2b1-43eb-bbbf-98d28412ab75", @@ -276,7 +284,7 @@ { "data": { "text/plain": [ - "(0.8421649167873537, 0.8390497506713147)" + "0.8421649167873537" ] }, "execution_count": 6, @@ -285,12 +293,33 @@ } ], "source": [ - "lm2.rsquared, lm2.rsquared_adj" + "lm2.rsquared" ] }, { "cell_type": "code", "execution_count": 7, + "id": "7ba1e6b5-7f0a-48ea-8490-d1083c480596", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8390497506713147" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm2.rsquared_adj" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "ad495e0a-ba71-4bf6-ba39-f35cfb19bacd", "metadata": {}, "outputs": [ @@ -300,7 +329,7 @@ "54570.51590209805" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "45ae7aa7-1443-4103-a3fa-ecaf9c36e36e", "metadata": {}, "outputs": [ @@ -322,7 +351,7 @@ "65.56013803717558" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -341,8 +370,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "666c1619-a758-4094-bf78-4438dc57cb61", + "execution_count": 10, + "id": "a4781ad0-bce9-4c8f-8443-10be1a8f2f12", "metadata": {}, "outputs": [], "source": [ @@ -351,61 +380,389 @@ "scoreshat = lm2.fittedvalues.values\n", "meanscore = np.mean(scores)\n", "\n", - "\n", + "tss = sum( (scores-meanscore)**2 )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "66d302f4-8831-4319-8e9b-27bd4bdf8b34", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.8421649167873537,\n", + " 0.8421649167873537,\n", + " 0.8421649167873537,\n", + " 0.8421649167873536,\n", + " 0.8421649167873543)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# R^2 alt.\n", + "lm2.rsquared, metrics.r2_score(scores, scoreshat), lm2.rsquared, 1-lm2.ssr/tss, lm2.ess/tss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d7296f9-f24f-4589-ae3e-c83aacae5a79", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f5b17889-7bb4-411b-99c5-a2c973bad443", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-547.6259042117637" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm2.llf" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "be48cf64-a063-4818-aea5-0f8a6c83cc71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1103.2518084235273, 1115.4512324525256)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm2.aic, lm2.bic" + ] + }, + { + "cell_type": "markdown", + "id": "24ddfa43-00ee-4fdd-b6c1-f86f66f0d25d", + "metadata": {}, + "source": [ + "### Other metrics (optional material)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ba51c2c5-4529-4ecf-8778-ac6e3a3eb53e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(64797.89743589744, 64797.8974358974)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# TSS = Total Sum of Squares\n", - "tss = sum( (scores-meanscore)**2 ) \n", - "# lm2.centered_tss, tss\n", - "\n", + "tss = sum( (scores-meanscore)**2 )\n", + "lm2.centered_tss, tss" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7fecad24-e751-48b0-a29e-e0e39bc2d44d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10227.381533799391, 10227.381533799391)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# SSR = Sum of Squares Residuals\n", - "# ssr = sum( (scores-scoreshat)**2 )\n", - "# ssr, lm2.ssr\n", - "\n", + "ssr = sum( (scores-scoreshat)**2 )\n", + "ssr, lm2.ssr" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9922e26e-c9b4-4831-9864-c796b554a0e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(54570.51590209805, 54570.51590209801)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# ESS = Explained Sum of Squares\n", - "# ess = sum( (scoreshat-ybar)**2 ) \n", - "# lm2.ess, ess\n", - "\n", - "# R^2\n", - "# metrics.r2_score(scores, scoreshat), lm2.rsquared, 1-lm2.ssr/tss, lm2.ess/tss\n", - "\n", + "ess = sum( (scoreshat-meanscore)**2 ) \n", + "lm2.ess, ess" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8f8eeec7-d17c-43d9-b15e-3026120d1664", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(65.56013803717558, 65.56013803717558)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# MSE\n", - "# metrics.mean_squared_error(scores, scoreshat), lm2.ssr/n\n", - "\n", + "metrics.mean_squared_error(scores, scoreshat), lm2.ssr/n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "12a548a8-2b15-40e0-b7d4-b7b64bd85759", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8.096921516056307, 8.096921516056307)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# RMSE\n", - "# metrics.root_mean_squared_error(scores, scoreshat), np.sqrt(lm2.ssr/n)\n", - "\n", + "metrics.root_mean_squared_error(scores, scoreshat), np.sqrt(lm2.ssr/n)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2583c814-aa2b-4045-9bf9-ef6d5e0b5706", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.2223447695690139, 0.2223447695690138)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# MAPE\n", - "# mape = sum( np.abs(scores - scoreshat)/scores ) / n\n", - "# metrics.mean_absolute_percentage_error(scores, scoreshat), mape\n", - "\n", + "mape = sum( np.abs(scores - scoreshat)/scores ) / n\n", + "metrics.mean_absolute_percentage_error(scores, scoreshat), mape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "431cdda8-8c27-4582-8f40-6259ed6a6389", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6.415932421491938, 6.415932421491938)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# MAE\n", - "# mae = sum( np.abs(scores - scoreshat) ) / n\n", - "# metrics.mean_absolute_error(scores, scoreshat), mae\n", - "\n", + "mae = sum( np.abs(scores - scoreshat) ) / n\n", + "metrics.mean_absolute_error(scores, scoreshat), mae" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9e9ee750-5497-4a7a-8bbc-cb133eaa2a04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18190.171967366015, 18190.171967366015)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# Mean squared error the model\n", "# The explained sum of squares divided by the model degrees of freedom\n", - "# lm2.mse_model, lm2.ess/3\n", - "\n", + "lm2.mse_model, lm2.ess/3" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6df755ad-d32a-421f-ab8d-c530f757feb1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(67.28540482762757, 67.28540482762757)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# Mean squared error of the residuals\n", "# The sum of squared residuals divided by the residual degrees of freedom.\n", - "# lm2.mse_resid, lm2.ssr/(n-4)\n", - "\n", + "lm2.mse_resid, lm2.ssr/(n-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "edd17b24-f35f-4121-bf56-9f557bbf80b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(418.05095119933833, 418.05095119933833)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# Total mean squared error\n", "# The centered total sum of squares divided by the number of observations.\n", - "# lm2.mse_total, lm2.centered_tss/(n-1)" + "lm2.mse_total, lm2.centered_tss/(n-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92ab3af0-f3fc-4946-aa55-1e17ce41028f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4910acfd-0699-40b0-87f1-e394592e8eb4", + "metadata": {}, + "source": [ + "## Parameter estimates" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "cc18bf04-b411-45ce-b485-daabc1d4d9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 60.452901\n", + "alc -1.800101\n", + "weed -1.021552\n", + "exrc 1.768289\n", + "dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# estimated parameters\n", + "lm2.params" ] }, { - "cell_type": "markdown", - "id": "4910acfd-0699-40b0-87f1-e394592e8eb4", + "cell_type": "code", + "execution_count": 25, + "id": "20b640ef-1c6a-466c-bf9c-be13e191bec1", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.202768119825622" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## Parameter estimates" + "# estimated sigma (noise term)\n", + "np.sqrt(lm2.scale)" ] }, { "cell_type": "code", "execution_count": null, - "id": "20b640ef-1c6a-466c-bf9c-be13e191bec1", + "id": "a142f3c4-e3e2-480d-b176-d2c329f8ab15", "metadata": {}, "outputs": [], "source": [] @@ -420,60 +777,115 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "cc18bf04-b411-45ce-b485-daabc1d4d9f4", + "execution_count": 26, + "id": "8d16b640-40e9-4398-9b7d-4e24ee93bc3e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Intercept 60.452901\n", - "alc -1.800101\n", - "weed -1.021552\n", - "exrc 1.768289\n", + "Intercept 1.289380\n", + "alc 0.069973\n", + "weed 0.476166\n", + "exrc 0.138056\n", "dtype: float64" ] }, - "execution_count": 10, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# estimated parameters\n", - "lm2.params" + "# standard errors\n", + "lm2.bse" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "8d16b640-40e9-4398-9b7d-4e24ee93bc3e", + "execution_count": 27, + "id": "13c52bc1-6edc-4784-8fa6-b7655e65e0d5", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "Intercept 1.289380\n", - "alc 0.069973\n", - "weed 0.476166\n", - "exrc 0.138056\n", - "dtype: float64" + " 0 1\n", + "Intercept 57.905480 63.000321\n", + "alc -1.938347 -1.661856\n", + "weed -1.962309 -0.080794\n", + "exrc 1.495533 2.041044" ] }, - "execution_count": 11, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# standard errors\n", - "lm2.bse" + "lm2.conf_int(alpha=0.05)" ] }, { "cell_type": "code", "execution_count": null, - "id": "13c52bc1-6edc-4784-8fa6-b7655e65e0d5", + "id": "a83b5589-e01c-4da4-9cb0-fc63ca4e1a07", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df2efa04-8677-4d98-a47c-451fe3ba33c5", "metadata": {}, "outputs": [], "source": [] @@ -486,9 +898,54 @@ "## Hypothesis testing for linear models" ] }, + { + "cell_type": "markdown", + "id": "5c0eaee7-42f3-475b-9582-fa9c894005f1", + "metadata": {}, + "source": [ + "### F-test for the overall model" + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, + "id": "e999bf5b-9ffc-46b1-a768-8a02b4b05ebf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(270.3435018926583, 1.0512133413866907e-60)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm2.fvalue, lm2.f_pvalue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f2c1954-1300-46da-a5ea-b0effc822ff2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a40a5ec3-0bcd-43f8-9004-058dc4fa770f", + "metadata": {}, + "source": [ + "### T-tests for individual parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "id": "bb52ddcb-26fa-40da-8cbe-accb9ff89d55", "metadata": {}, "outputs": [ @@ -502,7 +959,7 @@ "dtype: float64" ] }, - "execution_count": 12, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -513,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "id": "9620c62e-83e3-4c3f-ac63-f98490b20641", "metadata": {}, "outputs": [ @@ -523,7 +980,7 @@ "152.0" ] }, - "execution_count": 13, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -534,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "id": "080040a4-32ed-4f18-96db-875aefef05ec", "metadata": {}, "outputs": [ @@ -548,7 +1005,7 @@ "dtype: float64" ] }, - "execution_count": 14, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -559,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 32, "id": "75ccf902-763b-47ea-a071-2ee5030a14ac", "metadata": {}, "outputs": [ @@ -569,7 +1026,7 @@ "0.03351156181342321" ] }, - "execution_count": 15, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -600,6 +1057,110 @@ "## Assumptions checks and diagnostics" ] }, + { + "cell_type": "markdown", + "id": "bb52997b-c96f-4f27-bd15-22192488ed92", + "metadata": {}, + "source": [ + "### Diagnostics plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "477169d5-96d7-46f0-b659-6ce798371489", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0bed78e1-fa7c-4e75-8b54-72ddd017c46f", + "metadata": {}, + "source": [ + "### Linearity checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75d49ad3-dc17-49e3-8448-67c280cdd5c7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5d1034f0-2588-410c-9435-35da95b7eb77", + "metadata": {}, + "source": [ + "### Normality checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ee0ecc8-0ddd-4742-867a-ddb9294a0b9c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "630f850b-ef96-47a5-aa44-0aec1dddb560", + "metadata": {}, + "source": [ + "### Homoscedasticity checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b59ba8f-6fc6-4262-8ce5-3271be38b20b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3593c4d0-c746-490b-a301-70e81c7e0ee7", + "metadata": {}, + "source": [ + "### Independence checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4a3b4e2-da2a-411d-aa10-e68d3002e49b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ab3a495e-44da-41db-b7e4-6a0a88e35837", + "metadata": {}, + "source": [ + "### Collinearity checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27a743b4-19b7-4be7-882d-fe7fbaa4015f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58d60eb3-0476-4d3c-a55d-0a6824aa0c52", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "cc6e007c-59ee-433f-9af7-cae9142bba05", @@ -610,36 +1171,35 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "id": "bfe089b8-38a6-471b-839a-1f0eaf0cdd62", "metadata": {}, "outputs": [], "source": [ - "# TODO: move to Sec 4.3\n", "# Model fit diagnostics and plots\n", - "# influence = fit1.get_influence()\n", + "# influence = lm2.get_influence()\n", "# influence.summary_frame()\n", "\n", "# resid_studentized = influence.resid_studentized_internal\n", "# resid_studentized\n", "\n", - "# fit1.outlier_test()\n", + "# lm2.outlier_test()\n", "# import statsmodels.api as sm\n", - "# sm.graphics.influence_plot(fit1, criterion=\"cooks\");\n", - "# sm.graphics.plot_ccpr(fit1, \"effort\");\n", - "# sm.graphics.plot_regress_exog(fit1, \"effort\");" + "# sm.graphics.influence_plot(lm2, criterion=\"cooks\");\n", + "# sm.graphics.plot_ccpr(lm2, \"effort\");\n", + "# sm.graphics.plot_regress_exog(lm2, \"effort\");" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 34, "id": "7e378bc2-4afd-4a44-9d98-5218983a3dfd", "metadata": {}, "outputs": [], "source": [ "# lm2.fittedvalues\n", "# ALT. \n", - "# m2.predict(doctors)" + "# lm2.predict(doctors)" ] }, { @@ -660,7 +1220,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 35, "id": "de9c5e17-917f-4762-bcd4-24f1366d342f", "metadata": {}, "outputs": [ @@ -677,7 +1237,7 @@ " 'mineigval': 40.14996367643263}" ] }, - "execution_count": 18, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -713,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 36, "id": "d757fa73-23ec-4f1b-a5d2-38d6c7a7a6c0", "metadata": {}, "outputs": [ @@ -724,7 +1284,7 @@ "dtype: float64" ] }, - "execution_count": 19, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -736,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, "id": "f89814a5-a976-47e2-85d3-118e5f376d2a", "metadata": {}, "outputs": [], @@ -746,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 38, "id": "210fc4d5-d608-4304-9ec3-b1a86ba6ad87", "metadata": {}, "outputs": [ @@ -756,7 +1316,7 @@ "(array([68.17735504]), array([8.2626007]), array([[54.50324518, 81.85146489]]))" ] }, - "execution_count": 21, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +1328,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 39, "id": "46b8b5be-3322-409c-a157-1fe102067ae5", "metadata": {}, "outputs": [], @@ -781,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 40, "id": "3ebe0c36-0edd-48f7-8b85-dac4f53d6fcf", "metadata": {}, "outputs": [ @@ -793,7 +1353,7 @@ " array([[66.53473577, 69.81997431]]))" ] }, - "execution_count": 23, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -804,7 +1364,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 41, "id": "1894fb4e-2003-427a-9217-9b57e8db365d", "metadata": {}, "outputs": [], @@ -831,14 +1391,46 @@ "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "id": "e42e537e-ee05-45e7-8c54-92191d2e999a", + "metadata": {}, + "source": [ + "### Out-of-sample prediction accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29cbf9a7-8cac-4dde-b2fd-65ad468fb3ac", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1f6df411-39d3-4319-9881-43c93f82f0e2", + "metadata": {}, + "source": [ + "### Leave-one-out cross-validation" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "524cd797-5104-4eee-b340-db4342caee82", + "id": "76a2f93b-33dc-4096-a14d-7302644d8a21", "metadata": {}, "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "id": "e0348a24-f8e8-47ad-8469-2d2e982447d0", + "metadata": {}, + "source": [ + "### Regularization" + ] + }, { "cell_type": "code", "execution_count": null, @@ -855,6 +1447,14 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "id": "24b493d8-74a1-440f-92f5-751c463948fd", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "2646b903-78e3-49dd-a842-eca06cca5c46", @@ -879,10 +1479,36 @@ "## Discussion" ] }, + { + "cell_type": "markdown", + "id": "76146242-9817-4d66-aa7d-306644e69b17", + "metadata": {}, + "source": [ + "### Resampling methods for regression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a7b0fa0-610a-4d35-85cc-7cafb8f2332f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b83662cc-e7b6-4980-bc18-c3729436a9d9", + "metadata": {}, + "source": [ + "### Towards machine learning\n", + "\n", + "The out-of-sample prediction accuracy is a common metric used in machine learning (ML) tasks." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "f91072d1-0737-4c1b-a404-44728d890748", + "id": "9a661118-93f5-49cb-9b24-5c0006fdeec5", "metadata": {}, "outputs": [], "source": [] @@ -895,6 +1521,14 @@ "## Exercises" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "5209d912-19de-4e8e-9684-2d81f9524adb", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -908,7 +1542,12 @@ "id": "062c6f1b-9a08-4c3d-9d66-2b3a963a4652", "metadata": {}, "source": [ - "## Links" + "## Links\n", + "\n", + "- More details about model checking\n", + " https://ethanweed.github.io/pythonbook/05.04-regression.html#model-checking\n", + "- Statistical Modeling: The Two Cultures paper that explains the importance of out-of-sample predictions for statistical modelling.\n", + " https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full" ] }, { diff --git a/notebooks/44_regression_with_categorical_predictors.ipynb b/notebooks/44_regression_with_categorical_predictors.ipynb index e71cddb8..0003f494 100644 --- a/notebooks/44_regression_with_categorical_predictors.ipynb +++ b/notebooks/44_regression_with_categorical_predictors.ipynb @@ -58,7 +58,8 @@ "plt.clf() # needed otherwise `sns.set_theme` doesn't work\n", "from plot_helpers import RCPARAMS\n", "# RCPARAMS.update({'figure.figsize': (10, 3)}) # good for screen\n", - "RCPARAMS.update({'figure.figsize': (5, 1.6)}) # good for print\n", + "RCPARAMS.update({'figure.figsize': (5, 1.6), # good for print\n", + " 'text.usetex': True}) \n", "sns.set_theme(\n", " context=\"paper\",\n", " style=\"whitegrid\",\n", @@ -520,6 +521,14 @@ "lm5.diagn" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcdc41f3-f096-4125-91b0-6b54ea8b65c1", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "95b7a0a9-7643-45f5-9a33-29d5ab6e362e", @@ -665,33 +674,41 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "47312033-6f11-476b-8da8-8f5104c2b22b", + "execution_count": 21, + "id": "b15aef7b-05b2-434c-8230-dac5b1984304", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": { "image/png": { - "height": 293, - "width": 450 + "height": 384, + "width": 372 } }, "output_type": "display_data" } ], "source": [ - "with plt.rc_context({\"figure.figsize\":(5,3)}):\n", - " sns.stripplot(data=eprices, x=\"loc\", y=\"price\", hue=\"loc\", jitter=0, alpha=0.3)\n", - " sns.pointplot(data=eprices, x=\"loc\", y=\"price\", hue=\"loc\", estimator=\"mean\",\n", - " errorbar=None, marker=\"D\")" + "from plot_helpers import plot_lm_ttest\n", + "\n", + "with plt.rc_context({\"figure.figsize\":(4,4)}):\n", + " ax = plot_lm_ttest(eprices, x=\"loc\", y=\"price\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd8080c0-f302-4a67-be5a-5c1b0e014426", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "0a1f56d5-a6cf-4d45-a6bd-0590eb9f3bc7", @@ -702,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "4e39e263-864a-4a13-ac26-82399ca300bc", "metadata": {}, "outputs": [ @@ -712,7 +729,7 @@ "(-1.9657612140164198, 0.05112460353979369)" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -729,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "id": "90438cea-92e3-4812-9fc6-42d3e5cc8e48", "metadata": {}, "outputs": [ @@ -739,7 +756,7 @@ "(-1.9657612140164218, 0.051124603539793465)" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -751,20 +768,20 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 25, "id": "a95eef1d-22cf-4386-983d-f06d06a09a5f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { "image/png": { - "height": 294, + "height": 384, "width": 456 } }, @@ -772,10 +789,8 @@ } ], "source": [ - "with plt.rc_context({\"figure.figsize\":(5,3)}):\n", - " sns.stripplot(data=doctors, x=\"loc\", y=\"score\", hue=\"loc\", jitter=0, alpha=0.3)\n", - " sns.pointplot(data=doctors, x=\"loc\", y=\"score\", hue=\"loc\", estimator=\"mean\",\n", - " errorbar=None, marker=\"D\")" + "with plt.rc_context({\"figure.figsize\":(5,4)}):\n", + " ax = plot_lm_ttest(doctors, x=\"loc\", y=\"score\")" ] }, { @@ -788,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "id": "f1d97ed0-ade1-43b4-bcdc-4cc0bc20d6b5", "metadata": {}, "outputs": [ @@ -798,7 +813,7 @@ "F_onewayResult(statistic=0.5953116925291182, pvalue=0.5526627461285608)" ] }, - "execution_count": 22, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -815,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "id": "9f36e42f-2c0b-4004-8010-14c195da9428", "metadata": {}, "outputs": [ @@ -825,7 +840,7 @@ "(0.5953116925291042, 0.5526627461285702)" ] }, - "execution_count": 23, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -838,8 +853,90 @@ { "cell_type": "code", "execution_count": null, + "id": "b3f362d8-6822-4cff-b487-4ee885e48e48", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, "id": "e28c786e-10b8-46b0-b5a9-3f2643e00dc4", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 524, + "width": 533 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from plot_helpers import plot_lm_anova\n", + "\n", + "with plt.rc_context({\"figure.figsize\":(6,6)}):\n", + " ax = plot_lm_anova(doctors, x=\"work\", y=\"score\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5059f93d-5424-46ca-91e4-5d44226a3863", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "95d77268-5954-4266-a030-2933a311aca0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 524, + "width": 686 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Construct data as a pd.DataFrame\n", + "a = np.random.normal(0, 1, 20)\n", + "b = np.random.normal(-2, 1, 20)\n", + "c = np.random.normal(3, 1, 20)\n", + "d = np.random.normal(1.5, 1, 20)\n", + "\n", + "df = pd.DataFrame()\n", + "df[\"group\"] = [\"a\"]*20 + [\"b\"]*20 + [\"c\"]*20 + [\"d\"]*20\n", + "df[\"value\"] = np.concatenate([a, b, c, d])\n", + "\n", + "with plt.rc_context({\"figure.figsize\":(8,6)}):\n", + " ax = plot_lm_anova(df, x=\"group\", y=\"value\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95ab776d-3e2a-42af-b703-e1c29f92998c", + "metadata": {}, "outputs": [], "source": [] }, @@ -896,9 +993,22 @@ "id": "43985137-9b84-4846-8ae9-0c40ae939f78", "metadata": {}, "source": [ - "## Links" + "## Links\n", + "\n", + "- Patsy and Statsmodels https://www.youtube.com/watch?v=iEANEcWqAx4 \n", + "- Tests as LMs: https://lindeloev.github.io/tests-as-linear/\n", + "- https://stats.stackexchange.com/questions/59047/how-are-regression-the-t-test-and-the-anova-all-versions-of-the-general-linear\n", + "- https://danielroelfs.com/blog/everything-is-a-linear-model/ via [HN](https://news.ycombinator.com/item?id=39420111)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8e6fdfe-3228-47d4-811b-f07703e96924", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/45_causal_effects_and_confounders.ipynb b/notebooks/45_model_selection.ipynb similarity index 99% rename from notebooks/45_causal_effects_and_confounders.ipynb rename to notebooks/45_model_selection.ipynb index 0604aa9b..b78b23e5 100644 --- a/notebooks/45_causal_effects_and_confounders.ipynb +++ b/notebooks/45_model_selection.ipynb @@ -7,9 +7,9 @@ "tags": [] }, "source": [ - "# Section 4.5 — Causal effects and confounders\n", + "# Section 4.5 — Model selection\n", "\n", - "This notebook contains the code examples from [Section 4.5 Causal effects and confounders]() from the **No Bullshit Guide to Statistics**." + "This notebook contains the code examples from [Section 4.5 Model selection]() from the **No Bullshit Guide to Statistics**." ] }, { diff --git a/notebooks/cut_material.ipynb b/notebooks/cut_material.ipynb index 2929dc0a..17c27eee 100644 --- a/notebooks/cut_material.ipynb +++ b/notebooks/cut_material.ipynb @@ -105,7 +105,29 @@ "id": "1b2ed7b4-d6e0-4942-8ab7-484cf4412c03", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\n", + "\n", + "def jackknife(sample, estfunc):\n", + " \"\"\"\n", + " Evaluate the Jackknife estimate of the estimator `estfunc`.\n", + " via https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html\n", + " \"\"\"\n", + " n = len(sample)\n", + " stats = np.zeros(n)\n", + "\n", + " # Leaving out observations one-by-one\n", + " for i in range(n):\n", + " stats[i] = estfunc(np.delete(sample,i))\n", + "\n", + " # analysis\n", + " print(\"Jackknife Statistics:\")\n", + " print(\"original bias std. error\")\n", + " print(\"%8g %14g %15g\" % (estfunc(sample), (n-1)*mean(stats)/n, (n*var(stats))**.5))\n", + "\n", + " return stats\n", + "\n" + ] }, { "cell_type": "code", diff --git a/notebooks/plot_helpers.py b/notebooks/plot_helpers.py index 3c47e2e6..4a8449dc 100644 --- a/notebooks/plot_helpers.py +++ b/notebooks/plot_helpers.py @@ -13,6 +13,8 @@ import pandas as pd from scipy.integrate import quad import seaborn as sns +import statsmodels.api as sm +import statsmodels.formula.api as smf from scipy.stats import randint # special handling beta+1=beta from scipy.stats import nbinom # display parameter n as r @@ -907,6 +909,41 @@ def plot_alpha_beta_errors(cohend, ax=None, xlims=None, n=9, alpha=0.05, # Linear models ################################################################################ +def simple_regplot(x, y, n_std=2, n_pts=100, ax=None, + scatter_kws=None, line_kws=None, ci_kws=None): + """ + Draw a regression line with error interval. + via https://stackoverflow.com/a/59756979/127114 + See also https://github.com/ttesileanu/pydove/blob/main/pydove/regplot.py + """ + ax = plt.gca() if ax is None else ax + + # calculate best-fit line and interval + x_fit = sm.add_constant(x) + fit_results = sm.OLS(y, x_fit).fit() + + eval_x = sm.add_constant(np.linspace(np.min(x), np.max(x), n_pts)) + pred = fit_results.get_prediction(eval_x) + + # draw the fit line and error interval + ci_kws = {} if ci_kws is None else ci_kws + ax.fill_between( + eval_x[:, 1], + pred.predicted_mean - n_std * pred.se_mean, + pred.predicted_mean + n_std * pred.se_mean, + alpha=0.5, + **ci_kws, + ) + line_kws = {} if line_kws is None else line_kws + h = ax.plot(eval_x[:, 1], pred.predicted_mean, **line_kws) + + # draw the scatterplot + scatter_kws = {} if scatter_kws is None else scatter_kws + ax.scatter(x, y, c=h[0].get_color(), **scatter_kws) + + return fit_results + + def plot_residuals(xdata, ydata, b0, b1, xlims=None, ax=None): """ Plot residuals between the points (x,y) and the line y = b0 + b1*x. @@ -951,3 +988,95 @@ def get_aspect(ax): ax.add_patch(rect2) return ax + + +def plot_lm_ttest(data, x, y, ax=None): + """ + Plot a combined scatterplot, means, and LM slope line + to illustrate the equivalence between two-sample t-test + and a linear model with a single binary predictor `x`. + """ + # Fit the linear model + lm = smf.ols(formula=f"{y} ~ 1 + C({x})", data=data).fit() + beta0, beta1 = lm.params + interceptlab, slopelab = lm.params.index + + # Plot the data + ax = plt.gca() if ax is None else ax + sns.stripplot(data=data, x=x, y=y, hue=x, jitter=0, alpha=0.3) + sns.pointplot(data=data, x=x, y=y, hue=x, estimator="mean", errorbar=None, marker="D") + + # Customize plot labels + xlabel0, xlabel1 = [l.get_text() for l in ax.get_xticklabels()] + newxlabel0 = xlabel0 + "\n0" + newxlabel1 = xlabel1 + "\n1" + ax.set_xticks([0,1]) + ax.set_xticklabels([newxlabel0, newxlabel1]) + ax.set_xlim([-0.3, 1.3]) + + # Get seaborn colors + snspal = sns.color_palette() + + # Add h-lines to represent the two group means + ax.hlines(beta0, xmin=-0.3, xmax=1.3, color=snspal[0], + label=f"$\\beta_0$ = \\texttt{{{interceptlab}}} = {xlabel0} mean") + ax.hlines(beta0+beta1, xmin=0.8, xmax=1.2, color=snspal[1], + label=f"$\\beta_0 + \\beta_{{\\texttt{{{xlabel1}}}}}$ = {xlabel1} mean") + + # Add diagonal to represent difference between means + ax.plot( + [0, 1], + [beta0, beta0 + beta1], + color="k", + label=f"$\\beta_{{\\texttt{{{xlabel1}}}}}$ = \\texttt{{{slopelab}}} slope", + ) + + # Return axes + ax.legend() + return ax + + + +def plot_lm_anova(data, x, y, ax=None): + """ + Plot a combined scatterplot, means, and LM slope lines + to illustrate the equivalence between ANOVA test and + a linear model with a single categorical predictor `x`. + """ + # Fit the linear model + lm = smf.ols(formula=f"{y} ~ 1 + C({x})", data=data).fit() + + # Labels for the different levels of the categorical variable + labels = sorted(np.unique(data[x].values)) + + # Seaborn color palette, line styles, and aesthetics + snspal = sns.color_palette() + linestyles = ['solid', 'dotted', 'dashed', 'dashdot', + (0, (3, 5, 1, 5, 1, 5)), # dashdotdotted + (5, (10, 3))] # long dash with offset + + # Plot the data + ax = plt.gca() if ax is None else ax + sns.stripplot(data=data, x=x, y=y, hue=x, jitter=0, alpha=0.3, order=labels, hue_order=labels) + sns.pointplot(data=data, x=x, y=y, hue=x, estimator="mean", errorbar=None, marker="D", hue_order=labels) + + # Group 1 (baseline) + beta0 = lm.params[0] + interceptlab = lm.params.index[0] + ax.axhline(beta0, color=snspal[0], linewidth=1, + label=f"$\\beta_0$ = \\texttt{{{interceptlab}}} = {labels[0]} mean") + + # Remaining groups + for i in range(1, len(labels)): + label = labels[i] + beta = lm.params[i] + slopelab = lm.params.index[i] + linestyle = linestyles[i%len(linestyles)] + ax.hlines(beta0+beta, xmin=i-0.2, xmax=i+0.2, color=snspal[i]) + ax.plot([i-0.7, i], [beta0, beta0 + beta], color="k", linestyle=linestyle, + label=f"$\\beta_{{\\texttt{{{label}}}}}$ = \\texttt{{{slopelab}}} slope") + + # Return axes + ax.legend() + return ax + diff --git a/notebooks/stats_helpers.py b/notebooks/stats_helpers.py index 3e5b84ac..e77a1967 100644 --- a/notebooks/stats_helpers.py +++ b/notebooks/stats_helpers.py @@ -703,3 +703,9 @@ def simulate_ci_props(pops, methods=["a", "percentile", "bca"], ns=[20,40], print("Saved file to " + filepath) return results + + + +# LINEAR MODELS +################################################################################ +