{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Putting it all together - a case study\n", "> A Summary of lecture \"Statistical Thinking in Python (Part 2)\", via datacamp\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp, Data_Science, Statistics]\n", "- image: images/bootstrap-plot.png" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def ecdf(data):\n", " \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n", " # Number of data points: n\n", " n = len(data)\n", "\n", " # x-data for the ECDF: x\n", " x = np.sort(data)\n", "\n", " # y-data for the ECDF: y\n", " y = np.arange(1, n + 1) / n\n", "\n", " return x, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finch beaks and the need for statistics\n", "- Data source\n", " - Peter and Rosemary Grant\n", " - 40 Years of Evolution: Darwins's Finches on Dphne Major Island\n", " - Princeton University Press, 2014\n", " - Data acquired from Dryad Digital Repository [link](http://dx.doi.org/10.5061/dryad.g6g3h)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EDA of beak depths of Darwin's finches\n", "For your first foray into the Darwin finch data, you will study how the beak depth (the distance, top to bottom, of a closed beak) of the finch species Geospiza scandens has changed over time. The Grants have noticed some changes of beak geometry depending on the types of seeds available on the island, and they also noticed that there was some interbreeding with another major species on Daphne Major, Geospiza fortis. These effects can lead to changes in the species over time.\n", "\n", "In the next few problems, you will look at the beak depth of G. scandens on Daphne Major in 1975 and in 2012. To start with, let's plot all of the beak depth measurements in 1975 and 2012 in a bee swarm plot." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bandspeciesbeak_lengthbeak_depthyear
02fortis9.48.01975
19fortis9.28.31975
212fortis9.57.51975
315fortis9.58.01975
4305fortis11.59.91975
\n", "
" ], "text/plain": [ " band species beak_length beak_depth year\n", "0 2 fortis 9.4 8.0 1975\n", "1 9 fortis 9.2 8.3 1975\n", "2 12 fortis 9.5 7.5 1975\n", "3 15 fortis 9.5 8.0 1975\n", "4 305 fortis 11.5 9.9 1975" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_1 = pd.read_csv('./dataset/finch_beaks_1975.csv')\n", "df_2 = pd.read_csv('./dataset/finch_beaks_2012.csv')\n", "\n", "df_1['year'] = 1975\n", "df_2['year'] = 2012\n", "\n", "df_1.columns = ['band', 'species', 'beak_length', 'beak_depth', 'year']\n", "df_2.columns = ['band', 'species', 'beak_length', 'beak_depth', 'year']\n", "\n", "df = pd.concat([df_1, df_2])\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create bee swarm plot\n", "_ = sns.swarmplot(x='year', y='beak_depth', data=df)\n", "\n", "# Label the axes\n", "_ = plt.xlabel('year')\n", "_ = plt.ylabel('beak depth (mm)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ECDFs of beak depths\n", "While bee swarm plots are useful, we found that ECDFs are often even better when doing EDA. Plot the ECDFs for the 1975 and 2012 beak depth measurements on the same plot." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "bd_1975 = np.array(df[(df['year'] == 1975) & (df['species'] == 'scandens')]['beak_depth'])\n", "bd_2012 = np.array(df[(df['year'] == 2012) & (df['species'] == 'scandens')]['beak_depth'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compute ECDFs\n", "x_1975, y_1975 = ecdf(bd_1975)\n", "x_2012, y_2012 = ecdf(bd_2012)\n", "\n", "# Plot the ECDFs\n", "_ = plt.plot(x_1975, y_1975, marker='.', linestyle='none')\n", "_ = plt.plot(x_2012, y_2012, marker='.', linestyle='none')\n", "\n", "# Set margins\n", "plt.margins(0.02)\n", "\n", "# Add axis labels and legend\n", "_ = plt.xlabel('beak depth (mm)')\n", "_ = plt.ylabel('ECDF')\n", "_ = plt.legend(('1975', '2012'), loc='lower right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameter estimates of beak depths\n", "Estimate the difference of the mean beak depth of the G. scandens samples from 1975 and 2012 and report a 95% confidence interval." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def bootstrap_replicate_1d(data, func):\n", " \"\"\"Generate bootstrap replicate of 1D data.\"\"\"\n", " bs_sample = np.random.choice(data, len(data))\n", " return func(bs_sample)\n", "\n", "def draw_bs_reps(data, func, size=1):\n", " \"\"\"Draw bootstrap replicates.\"\"\"\n", " \n", " # Initialize array of replicates: bs_replicates\n", " bs_replicates = np.empty(size)\n", " \n", " # Generate replicates\n", " for i in range(size):\n", " bs_replicates[i] = bootstrap_replicate_1d(data, func)\n", " \n", " return bs_replicates" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "difference of means = 0.22622047244094645 mm\n", "95% confidence interval = [0.05979819 0.39109804] mm\n" ] } ], "source": [ "# Compute the difference of the sample means: mean_diff\n", "mean_diff = np.mean(bd_2012) - np.mean(bd_1975)\n", "\n", "# Get bootstrap replicates of means\n", "bs_replicates_1975 = draw_bs_reps(bd_1975, np.mean, 10000)\n", "bs_replicates_2012 = draw_bs_reps(bd_2012, np.mean, 10000)\n", "\n", "# Compute samples of difference of means: bs_diff_replicates\n", "bs_diff_replicates = bs_replicates_2012 - bs_replicates_1975\n", "\n", "# Compute 95% confidence interval: conf_int\n", "conf_int = np.percentile(bs_diff_replicates, [2.5, 97.5])\n", "\n", "# Print the results\n", "print('difference of means =', mean_diff, 'mm')\n", "print('95% confidence interval =', conf_int, 'mm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hypothesis test: Are beaks deeper in 2012?\n", "Your plot of the ECDF and determination of the confidence interval make it pretty clear that the beaks of G. scandens on Daphne Major have gotten deeper. But is it possible that this effect is just due to random chance? In other words, what is the probability that we would get the observed difference in mean beak depth if the means were the same?\n", "\n", "Be careful! The hypothesis we are testing is not that the beak depths come from the same distribution. For that we could use a permutation test. The hypothesis is that the means are equal. To perform this hypothesis test, we need to shift the two data sets so that they have the same mean and then use bootstrap sampling to compute the difference of means." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p = 0.0034\n" ] } ], "source": [ "# Compute mean of combined data set: combined_mean\n", "combined_mean = np.mean(np.concatenate((bd_1975, bd_2012)))\n", "\n", "# Shift the samples\n", "bd_1975_shifted = bd_1975 - np.mean(bd_1975) + combined_mean\n", "bd_2012_shifted = bd_2012 - np.mean(bd_2012) + combined_mean\n", "\n", "# Get bootstrap replicates of shifted data sets\n", "bs_replicates_1975 = draw_bs_reps(bd_1975_shifted, np.mean, 10000)\n", "bs_replicates_2012 = draw_bs_reps(bd_2012_shifted, np.mean, 10000)\n", "\n", "# Compute replicates of difference of means: bs_diff_replicates\n", "bs_diff_replicates = bs_replicates_2012 - bs_replicates_1975\n", "\n", "# Compute the p-value\n", "p = np.sum(bs_diff_replicates >= mean_diff) / len(bs_diff_replicates)\n", "\n", "# Print p-value\n", "print('p =', p)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We get a p-value of 0.0033, which suggests that there is a statistically significant difference. But remember: it is very important to know how different they are! In the previous exercise, you got a difference of 0.2 mm between the means. You should combine this with the statistical significance. Changing by 0.2 mm in 37 years is substantial by evolutionary standards. If it kept changing at that rate, the beak depth would double in only 400 years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variation in beak shapes\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EDA of beak length and depth\n", "Make scatter plots of beak depth (y-axis) versus beak length (x-axis) for the 1975 and 2012 specimens." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "bl_1975 = np.array(df[(df['year'] == 1975) & (df['species'] == 'scandens')]['beak_length'])\n", "bl_2012 = np.array(df[(df['year'] == 2012) & (df['species'] == 'scandens')]['beak_length'])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make scatter plot of 1975 data\n", "_ = plt.plot(bl_1975, bd_1975, marker='.', linestyle='None', color='blue', alpha=0.5)\n", "\n", "# Make scatter plot of 2012 data\n", "_ = plt.plot(bl_2012, bd_2012, marker='.', linestyle='None', color='red', alpha=0.5)\n", "\n", "# Label axes and make legend\n", "_ = plt.xlabel('beak length (mm)')\n", "_ = plt.ylabel('beak depth (mm)')\n", "_ = plt.legend(('1975', '2012'), loc='upper left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear regressions\n", "Perform a linear regression for both the 1975 and 2012 data. Then, perform pairs bootstrap estimates for the regression parameters. Report 95% confidence intervals on the slope and intercept of the regression line." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def draw_bs_pairs_linreg(x, y, size=1):\n", " \"\"\"Perform pairs bootstrap for linear regression.\"\"\"\n", "\n", " # Set up array of indices to sample from: inds\n", " inds = np.arange(len(x))\n", "\n", " # Initialize replicates: bs_slope_reps, bs_intercept_reps\n", " bs_slope_reps = np.empty(size)\n", " bs_intercept_reps = np.empty(size)\n", "\n", " # Generate replicates\n", " for i in range(size):\n", " bs_inds = np.random.choice(inds, size=len(inds))\n", " bs_x, bs_y = x[bs_inds], y[bs_inds]\n", " bs_slope_reps[i], bs_intercept_reps[i] = np.polyfit(bs_x, bs_y, deg=1)\n", "\n", " return bs_slope_reps, bs_intercept_reps" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def bootstrap_replicate_1d(data, func):\n", " \"\"\"Generate bootstrap replicate of 1D data.\"\"\"\n", " bs_sample = np.random.choice(data, len(data))\n", " return func(bs_sample)\n", "\n", "def draw_bs_reps(data, func, size=1):\n", " \"\"\"Draw bootstrap replicates.\"\"\"\n", " \n", " # Initialize array of replicates: bs_replicates\n", " bs_replicates = np.empty(size)\n", " \n", " # Generate replicates\n", " for i in range(size):\n", " bs_replicates[i] = bootstrap_replicate_1d(data, func)\n", " \n", " return bs_replicates" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1975: slope = 0.4652051691605937 conf int = [0.32492037 0.59321466]\n", "1975: intercept = 2.3908752365842276 conf int = [0.57667688 4.3381558 ]\n", "2012: slope = 0.46263035883531306 conf int = [0.32689249 0.59623919]\n", "2012: intercept = 2.9772474982360198 conf int = [1.14976518 4.79237901]\n" ] } ], "source": [ "# Compute the linear regressions\n", "slope_1975, intercept_1975 = np.polyfit(bl_1975, bd_1975, deg=1)\n", "slope_2012, intercept_2012 = np.polyfit(bl_2012, bd_2012, deg=1)\n", "\n", "# Perform pairs bootstrap for the linear regressions\n", "bs_slope_reps_1975, bs_intercept_reps_1975 = draw_bs_pairs_linreg(bl_1975, bd_1975, 1000)\n", "bs_slope_reps_2012, bs_intercept_reps_2012 = draw_bs_pairs_linreg(bl_2012, bd_2012, 1000)\n", "\n", "# Compute confidence intervals of slopes\n", "slope_conf_int_1975 = np.percentile(bs_slope_reps_1975, [2.5, 97.5])\n", "slope_conf_int_2012 = np.percentile(bs_slope_reps_2012, [2.5, 97.5])\n", "intercept_conf_int_1975 = np.percentile(bs_intercept_reps_1975, [2.5, 97.5])\n", "intercept_conf_int_2012 = np.percentile(bs_intercept_reps_2012, [2.5, 97.5])\n", "\n", "# Print the results\n", "print('1975: slope =', slope_1975, 'conf int =', slope_conf_int_1975)\n", "print('1975: intercept =', intercept_1975, 'conf int =', intercept_conf_int_1975)\n", "print('2012: slope =', slope_2012, 'conf int =', slope_conf_int_2012)\n", "print('2012: intercept =', intercept_2012, 'conf int =', intercept_conf_int_2012)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Displaying the linear regression results\n", "Now, you will display your linear regression results on the scatter plot." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make scatter plot of 1975 data\n", "_ = plt.plot(bl_1975, bd_1975, marker='.', linestyle='none', color='blue', alpha=0.5)\n", "\n", "# Make scatter plot of 2012 data\n", "_ = plt.plot(bl_2012, bd_2012, marker='.', linestyle='none', color='red', alpha=0.5)\n", "\n", "# Label axes and make legend\n", "_ = plt.xlabel('beak length (mm)')\n", "_ = plt.ylabel('beak depth (mm)')\n", "_ = plt.legend(('1975', '2012'), loc='upper left')\n", "\n", "# Generate x-values for bootstrap lines: x\n", "x = np.array([10, 17])\n", "\n", "# Plot the bootstrap lines\n", "for i in range(100):\n", " plt.plot(x, bs_slope_reps_1975[i] * x + bs_intercept_reps_1975[i], \n", " linewidth=0.5, alpha=0.2, color='blue')\n", " plt.plot(x, bs_slope_reps_2012[i] * x + bs_intercept_reps_2012[i],\n", " linewidth=0.5, alpha=0.2, color='red')\n", "plt.savefig('../images/bootstrap-plot.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Beak length to depth ratio\n", "The linear regressions showed interesting information about the beak geometry. The slope was the same in 1975 and 2012, suggesting that for every millimeter gained in beak length, the birds gained about half a millimeter in depth in both years. However, if we are interested in the shape of the beak, we want to compare the ratio of beak length to beak depth. Let's make that comparison." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1975: mean ratio = 1.5788823771858533 conf int = [1.55709396 1.6006049 ]\n", "2012: mean ratio = 1.4658342276847767 conf int = [1.44422244 1.48826439]\n" ] } ], "source": [ "# Compute length-to-depth ratios\n", "ratio_1975 = bl_1975 / bd_1975\n", "ratio_2012 = bl_2012 / bd_2012\n", "\n", "# Compute means\n", "mean_ratio_1975 = np.mean(ratio_1975)\n", "mean_ratio_2012 = np.mean(ratio_2012)\n", "\n", "# Generate bootstrap replicates of the means\n", "bs_replicates_1975 = draw_bs_reps(ratio_1975, np.mean, 10000)\n", "bs_replicates_2012 = draw_bs_reps(ratio_2012, np.mean, 10000)\n", "\n", "# Compute the 99% confidence intervals\n", "conf_int_1975 = np.percentile(bs_replicates_1975, [0.5, 99.5])\n", "conf_int_2012 = np.percentile(bs_replicates_2012, [0.5, 99.5])\n", "\n", "# Print the results\n", "print('1975: mean ratio =', mean_ratio_1975, 'conf int =', conf_int_1975)\n", "print('2012: mean ratio =', mean_ratio_2012, 'conf int =', conf_int_2012)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean beak length-to-depth ratio decreased by about 0.1, or 7%, from 1975 to 2012. The 99% confidence intervals are not even close to overlapping, so this is a real change. The beak shape changed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculation of heritability\n", "- Heredity\n", " - The tendency for parental traits to be inherited by offspring" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EDA of heritability\n", "The array ```bd_parent_scandens``` contains the average beak depth (in mm) of two parents of the species G. scandens. The array ```bd_offspring_scandens``` contains the average beak depth of the offspring of the respective parents. The arrays ```bd_parent_fortis``` and ```bd_offspring_fortis``` contain the same information about measurements from G. fortis birds.\n", "\n", "Make a scatter plot of the average offspring beak depth (y-axis) versus average parental beak depth (x-axis) for both species. Use the alpha=0.5 keyword argument to help you see overlapping points." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "df_fortis = pd.read_csv('./dataset/fortis_beak_depth_heredity.csv')\n", "df_scandens = pd.read_csv('./dataset/scandens_beak_depth_heredity.csv')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " mid_parent mid_offspring\n", "0 8.3318 8.4190\n", "1 8.4035 9.2468\n", "2 8.5317 8.1532\n", "3 8.7202 8.0089\n", "4 8.7089 8.2215" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_scandens.head()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "df_fortis['mid_parent'] = (df_fortis['Male BD'] + df_fortis['Female BD']) / 2\n", "df_fortis.drop(['Male BD', 'Female BD'], axis='columns', inplace=True)\n", "df_fortis.columns = df_scandens.columns" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "bd_parent_scandens = np.array(df_scandens['mid_parent'])\n", "bd_offspring_scandens = np.array(df_scandens['mid_offspring'])\n", "\n", "bd_parent_fortis = np.array(df_fortis['mid_parent'])\n", "bd_offspring_fortis = np.array(df_fortis['mid_offspring'])" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make scatter plots\n", "_ = plt.plot(bd_parent_fortis, bd_offspring_fortis,\n", " marker='.', linestyle='none', color='blue', alpha=0.5)\n", "_ = plt.plot(bd_parent_scandens, bd_offspring_scandens,\n", " marker='.', linestyle='none', color='red', alpha=0.5)\n", "\n", "# Label axes\n", "_ = plt.xlabel('parental beak depth (mm)')\n", "_ = plt.ylabel('offspring beak depth (mm)')\n", "\n", "# Add legend\n", "_ = plt.legend(('G. fortis', 'G. scandens'), loc='lower right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation of offspring and parental data\n", "In an effort to quantify the correlation between offspring and parent beak depths, we would like to compute statistics, such as the Pearson correlation coefficient, between parents and offspring. To get confidence intervals on this, we need to do a pairs bootstrap." ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "def draw_bs_pairs(x, y, func, size=1):\n", " \"\"\"Perform pairs bootstrap for a single statistic.\"\"\"\n", "\n", " # Set up array of indices to sample from: inds\n", " inds = np.arange(len(x))\n", "\n", " # Initialize replicates: bs_replicates\n", " bs_replicates = np.empty(size)\n", "\n", " # Generate replicates\n", " for i in range(size):\n", " bs_inds = np.random.choice(inds, size=len(inds))\n", " bs_x, bs_y = x[bs_inds], y[bs_inds]\n", " bs_replicates[i] = func(bs_x, bs_y)\n", "\n", " return bs_replicates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pearson correlation of offspring and parental data\n", "The Pearson correlation coefficient seems like a useful measure of how strongly the beak depth of parents are inherited by their offspring. Compute the Pearson correlation coefficient between parental and offspring beak depths for G. scandens. Do the same for G. fortis. Then, use the function you wrote in the last exercise to compute a 95% confidence interval using pairs bootstrap." ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "def pearson_r(x, y):\n", " \"\"\"Compute Pearson correlation coefficient between two arrays\n", " \n", " Args:\n", " x: arrays\n", " y: arrays\n", " \n", " returns:\n", " r: int\n", " \"\"\"\n", " # Compute correlation matrix: corr_mat\n", " corr_mat = np.corrcoef(x, y)\n", " \n", " # Return entry[0, 1]\n", " return corr_mat[0, 1]" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G. scandens: 0.4117063629401258 [0.27809076 0.5301343 ]\n", "G. fortis: 0.7283412395518487 [0.66929551 0.78065398]\n" ] } ], "source": [ "# Compute the Pearson correlation coefficients\n", "r_scandens = pearson_r(bd_parent_scandens, bd_offspring_scandens)\n", "r_fortis = pearson_r(bd_parent_fortis, bd_offspring_fortis)\n", "\n", "# Acquire 1000 bootstrap replicates of Pearson r\n", "bs_replicates_scandens = draw_bs_pairs(bd_parent_scandens, bd_offspring_scandens, pearson_r, 1000)\n", "bs_replicates_fortis = draw_bs_pairs(bd_parent_fortis, bd_offspring_fortis,\n", " pearson_r, 1000)\n", "\n", "# Compute 95% confidence intervals\n", "conf_int_scandens = np.percentile(bs_replicates_scandens, [2.5, 97.5])\n", "conf_int_fortis = np.percentile(bs_replicates_fortis, [2.5, 97.5])\n", "\n", "# Print results\n", "print('G. scandens:', r_scandens, conf_int_scandens)\n", "print('G. fortis:', r_fortis, conf_int_fortis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measuring heritability\n", "Remember that the Pearson correlation coefficient is the ratio of the covariance to the geometric mean of the variances of the two data sets. This is a measure of the correlation between parents and offspring, but might not be the best estimate of heritability. If we stop and think, it makes more sense to define heritability as the ratio of the covariance between parent and offspring to the variance of the parents alone. In this exercise, you will estimate the heritability and perform a pairs bootstrap calculation to get the 95% confidence interval.\n", "\n", "This exercise highlights a very important point. Statistical inference (and data analysis in general) is not a plug-n-chug enterprise. You need to think carefully about the questions you are seeking to answer with your data and analyze them appropriately. If you are interested in how heritable traits are, the quantity we defined as the heritability is more apt than the off-the-shelf statistic, the Pearson correlation coefficient.\n", "\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.47020406, 0.34504428],\n", " [0.34504428, 0.47730226]])" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.cov(bd_parent_fortis, bd_offspring_fortis)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G. scandens: 0.5485340868685982 [0.35458859 0.74433217]\n", "G. fortis: 0.7338181655502719 [0.66833664 0.80051671]\n" ] } ], "source": [ "def heritability(parents, offspring):\n", " \"\"\"Compute the heritability from parent and offspring samples.\"\"\"\n", " covariance_matrix = np.cov(parents, offspring)\n", " return covariance_matrix[0, 1] / covariance_matrix[0, 0]\n", "\n", "# Compute the heritability\n", "heritability_scandens = heritability(bd_parent_scandens, bd_offspring_scandens)\n", "heritability_fortis = heritability(bd_parent_fortis, bd_offspring_fortis)\n", "\n", "# Acquire 1000 bootstrap replicates of heritability\n", "replicates_scandens = draw_bs_pairs(\n", " bd_parent_scandens, bd_offspring_scandens, heritability, size=1000)\n", " \n", "replicates_fortis = draw_bs_pairs(\n", " bd_parent_fortis, bd_offspring_fortis, heritability, size=1000)\n", "\n", "\n", "# Compute 95% confidence intervals\n", "conf_int_scandens = np.percentile(replicates_scandens, [2.5, 97.5])\n", "conf_int_fortis = np.percentile(replicates_fortis, [2.5, 97.5])\n", "\n", "# Print results\n", "print('G. scandens:', heritability_scandens, conf_int_scandens)\n", "print('G. fortis:', heritability_fortis, conf_int_fortis)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Is beak depth heritable at all in G. scandens?\n", "The heritability of beak depth in G. scandens seems low. It could be that this observed heritability was just achieved by chance and beak depth is actually not really heritable in the species. You will test that hypothesis here. To do this, you will do a pairs permutation test.\n", "\n" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p-val = 0.0\n" ] } ], "source": [ "# Initialize array of replicates: perm_replicates\n", "perm_replicates = np.empty(10000)\n", "\n", "# Draw replicates\n", "for i in range(10000):\n", " # Permute parent beak depths\n", " bd_parent_permuted = np.random.permutation(bd_parent_scandens)\n", " perm_replicates[i] = heritability(bd_parent_permuted, bd_offspring_scandens)\n", "\n", "\n", "# Compute p-value: p\n", "p = np.sum(perm_replicates >= heritability_scandens) / len(perm_replicates)\n", "\n", "# Print the p-value\n", "print('p-val =', p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You get a p-value of zero, which means that none of the 10,000 permutation pairs replicates you drew had a heritability high enough to match that which was observed. This strongly suggests that beak depth is heritable in G. scandens, just not as much as in G. fortis. If you like, you can plot a histogram of the heritability replicates to get a feel for how extreme of a value of heritability you might expect by chance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistical thinking skills\n", "- Perform EDA\n", " - Generate effective plots like ECDFs\n", " - Compute summary statistics\n", "- Estimate parameters\n", " - By optimization, including linear regression\n", " - Determine confidence intervals\n", "- Formulate and test hypotheses" ] } ], "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }