{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lesson 30: Hacker statistics\n", "\n", "(c) 2018 Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT).\n", "\n", "This document was prepared at [Caltech](http://www.caltech.edu) with financial support from the [Donna and Benjamin M. Rosen Bioengineering Center](http://rosen.caltech.edu).\n", "\n", "\n", "\n", "*This tutorial was generated from a Jupyter notebook. You can download the notebook [here](l30_hackerstats.ipynb).*\n", "\n", "

" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import altair as alt\n", "\n", "import bootcamp_utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the field of statistics was in its early days, the practitioners did not have computers. They were therefore left to use pen and paper to compute things like confidence intervals. With just a little bit of programming experience, you can perform lots of the statistical analyses that may seem baffling when done with pencil and paper.\n", "\n", "At the heart of this \"hacker statistics\" is the ability to draw random numbers. We will focus on **bootstrap** methods in particular.\n", "\n", "To motivate this study, we will work with data measured by Peter and Rosemary Grant on the island of Daphne Major on the Galápagos. They have been going to the island every year for over forty years and have been taking a careful inventory of the finches there. We will look at the finch *Geospiza scandens*. The Grants measured the depths of the beaks (defined as the top-to-bottom thickness of the beak) of all of the finches of this species on the island. We will consider their measurements from 1975 and from 2012. We will investigate how the beaks got deeper over time.\n", "\n", "The data are from the book Grants' book *40 years of evolution: Darwin's finches on Daphne Major Island*](http://www.worldcat.org/oclc/854285415). They were generous and made their data publicly available on the [Dryad data repository](http://dx.doi.org/10.5061/dryad.g6g3h). In general, it is a very good idea to put your published data in public data repositories, both to preserve the data and also to make your findings public.\n", "\n", "Ok, let's start by loading in the data. You converted the Grants' data into a single DataFrame in [exercise 3](../l23_exercise_3_solution). Let's load the data, which are available in the file `~git/data/grant_complete.csv`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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bandbeak depth (mm)beak length (mm)speciesyear
0201238.059.25fortis1973
12012610.4511.35fortis1973
2201289.5510.15fortis1973
3201298.759.95fortis1973
42013310.1511.55fortis1973
\n", "
" ], "text/plain": [ " band beak depth (mm) beak length (mm) species year\n", "0 20123 8.05 9.25 fortis 1973\n", "1 20126 10.45 11.35 fortis 1973\n", "2 20128 9.55 10.15 fortis 1973\n", "3 20129 8.75 9.95 fortis 1973\n", "4 20133 10.15 11.55 fortis 1973" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('data/grant_complete.csv', comment='#')\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's trim down the `DataFrame` to only include *G. scandens* from 1975 and 2012 and only include the columns we need." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df = df.loc[(df['species']=='scandens') & (df['year'].isin([1975, 2012])),\n", " ['year', 'beak depth (mm)']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the ECDFs for these two years." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "ECDF": 0.16091954022988506, "beak depth (mm)": 8.4, "year": 1975 }, { "ECDF": 0.39080459770114945, "beak depth (mm)": 8.8, "year": 1975 }, { "ECDF": 0.1724137931034483, "beak depth (mm)": 8.4, "year": 1975 }, { "ECDF": 0.022988505747126436, "beak depth (mm)": 8, "year": 1975 }, { "ECDF": 0.011494252873563218, "beak depth (mm)": 7.9, "year": 1975 }, { "ECDF": 0.4482758620689655, "beak depth (mm)": 8.9, "year": 1975 }, { "ECDF": 0.26436781609195403, "beak depth (mm)": 8.6, "year": 1975 }, { "ECDF": 0.20689655172413793, "beak depth (mm)": 8.5, "year": 1975 }, { "ECDF": 0.45977011494252873, "beak depth (mm)": 8.9, "year": 1975 }, { "ECDF": 0.6091954022988506, "beak depth (mm)": 9.1, "year": 1975 }, { "ECDF": 0.27586206896551724, "beak depth (mm)": 8.6, "year": 1975 }, { "ECDF": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute ECDF\n", "df['ECDF'] = df.groupby('year').transform(bootcamp_utils.ecdf_y)\n", "\n", "# Make a plot\n", "alt.Chart(df\n", " ).mark_point(\n", " ).encode(\n", " x=alt.X('beak depth (mm):Q', \n", " scale=alt.Scale(zero=False)),\n", " y='ECDF:Q',\n", " color='year:N')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Judging from the ECDFs, it seems as though beaks have gotten deeper over time. But now, we would like a *statistic* to compare. One statistic that comes to mind it the mean. So, let's compare those. First, we'll pull out the data sets as Numpy arrays for convenience (and speed later on when we start doing bootstrap replicates)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(8.959999999999999, 9.188492063492063)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bd_1975 = df.loc[df['year']==1975, 'beak depth (mm)'].values\n", "bd_2012 = df.loc[df['year']==2012, 'beak depth (mm)'].values\n", "\n", "np.mean(bd_1975), np.mean(bd_2012)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, indeed, the mean beak depth is bigger in 2012 than in 1975. There is clearly some variability in beak depth among the birds measured each year, so it is possible that this observation is just due to random chance and the mean beak depth is really not that big. So, we would like to compute a *confidence interval* of the mean. We will compute the 95% confidence interval.\n", "\n", "What is a 95% confidence interval? It can be thought of as follows. If we were to repeat the experiment over and over and over again, 95% of the time, the observed mean would lie in the 95% confidence interval. So, if the confidence intervals of the means of measurements from 1975 and from 2012 overlapped, we might not be so sure that the beaks got deeper due to some underlying selective pressure, but that we just happened to *observe* deeper beaks as a result of natural variability.\n", "\n", "So, how do we compute a confidence interval? ....We use our computer!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrap confidence intervals\n", "\n", "The notion of the bootstrap was first published by Brad Efron in 1979. The idea is simple, and we will take the fact that it works as a given; Efron proved it for us. \n", "\n", "Here's the idea: If we could somehow repeat the measurements of the beak depths on Daphne Major, we could do it many many times, and we could then just compute the 2.5th and 97.5th percentiles to get a 95% confidence interval. The problem is, we can't repeat the experiments over and over again. 1975 only happened once, and all birds on the island were measured. We cannot have 1975 happen again under exactly the same conditions. \n", "\n", "Instead, we will have our computer *simulate* doing the experiment over and over again. Hacker statistics! We have one set of measurements. We \"repeat\" the experiment by drawing measurements out of the ones we have again and again. Here's what we do to compute a bootstrap estimate of the mean of a set of $n$ data points.\n", ">1. Draw *n* data points out of the original data set *with replacement*. This set of data points is called a **bootstrap sample**.\n", "2. Compute the mean of the bootstrap sample. This is called a **bootstrap replicate** of the mean.\n", "3. Do this over and over again, storing the results.\n", "\n", "So, it is as if we did the experiment over and over again, obtaining a mean each time. Remember, our bootstrap sample has exactly the same number of \"measurements\" as the original data set. Let's try it with the `bd_1975` data (remember the mean beak depth was 8.96 mm). First we'll generate a bootstrap sample. Remember, the function `np.random.choice()` allows us to sample out of an array with replacement, if we like." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(42)\n", "bs_sample = np.random.choice(bd_1975, replace=True, size=len(bd_1975))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a quick look at this bootstrap sample by plotting its ECDF right next to that of the original data set." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "ECDF": 0.011494252873563218, "beak depth (mm)": 8, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.022988505747126436, "beak depth (mm)": 8, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.034482758620689655, "beak depth (mm)": 8, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.04597701149425287, "beak depth (mm)": 8, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.05747126436781609, "beak depth (mm)": 8.1, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.06896551724137931, "beak depth (mm)": 8.1, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.08045977011494253, "beak depth (mm)": 8.1, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.09195402298850575, "beak depth (mm)": 8.1, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.10344827586206896, "beak depth (mm)": 8.2, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.11494252873563218, "beak depth (mm)": 8.3, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.12643678160919541, "beak depth (mm)": 8.3, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.13793103448275862, "beak depth (mm)": 8.3, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.14942528735632185, "beak depth (mm)": 8.35, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.16091954022988506, "beak depth (mm)": 8.35, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.1724137931034483, "beak depth (mm)": 8.4, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.1839080459770115, "beak depth (mm)": 8.4, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.19540229885057472, "beak depth (mm)": 8.4, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.20689655172413793, "beak depth (mm)": 8.5, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.21839080459770116, "beak depth (mm)": 8.5, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.22988505747126436, "beak depth (mm)": 8.5, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.2413793103448276, "beak depth (mm)": 8.5, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.25287356321839083, "beak depth (mm)": 8.5, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.26436781609195403, "beak depth (mm)": 8.5, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.27586206896551724, "beak depth (mm)": 8.6, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.28735632183908044, "beak depth (mm)": 8.6, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.2988505747126437, "beak depth (mm)": 8.6, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.3103448275862069, "beak depth (mm)": 8.65, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.3218390804597701, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.3333333333333333, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.3448275862068966, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.3563218390804598, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.367816091954023, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.3793103448275862, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.39080459770114945, "beak depth (mm)": 8.7, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.40229885057471265, "beak depth (mm)": 8.75, "set": "bootstrap", "year": 1975 }, { "ECDF": 0.41379310344827586, "beak depth (mm)": 8.8, 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute ECDF of bootstrap sample\n", "x_bs, y_bs = bootcamp_utils.ecdf_vals(bs_sample)\n", "df_bs = pd.DataFrame(data={'beak depth (mm)': x_bs,\n", " 'ECDF': y_bs, \n", " 'set': 'bootstrap',\n", " 'year': 1975})\n", "\n", "# DataFrame for original data\n", "df_original = df.copy().loc[df['year']==1975, :]\n", "df_original['set'] = 1975\n", "\n", "# DataFrame for plotting\n", "df_plot = pd.concat([df_bs, df_original], ignore_index=True, sort=True)\n", "\n", "# Plot the ECDFs\n", "alt.Chart(df_plot\n", " ).mark_point(\n", " ).encode(x=alt.X('beak depth (mm):Q', scale=alt.Scale(zero=False)),\n", " y='ECDF:Q',\n", " color=alt.Color('set:N'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is qualitatively similar, but obviously not exactly the same data set.\n", "\n", "Now, let's compute our bootstrap replicate. It's as simple as computing the mean of the bootstrap sample." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.92609195402299" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bs_replicate = np.mean(bs_sample)\n", "bs_replicate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, the mean of the bootstrap replicate is 8.93 mm, which is less than the mean of 8.96 from the original data set.\n", "\n", "Now, we can write a **`for`** loop to get lots and lots of bootstrap replicas. Note the since you are doing the replicates many many times, speed matters. For this reason, be sure you convert the data you are bootstrapping into a Numpy array. The calculations with them are **much** faster than with Pandas `Series`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of replicatess\n", "n_reps = 100000\n", "\n", "# Initialize bootstrap replicas array\n", "bs_reps_1975 = np.empty(n_reps)\n", "\n", "# Compute replicates\n", "for i in range(n_reps):\n", " bs_sample = np.random.choice(bd_1975, size=len(bd_1975))\n", " bs_reps_1975[i] = np.mean(bs_sample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our replicas, 100,000 of them, we can plot an ECDF to see what we might expect of the mean if we were to do the experiment again." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "ECDF": 1e-05, "mean beak depth (mm)": 8.711149425287356 }, { "ECDF": 0.01001, "mean beak depth (mm)": 8.82183908045977 }, { "ECDF": 0.02001, "mean beak depth (mm)": 8.837356321839081 }, { "ECDF": 0.03001, "mean beak depth (mm)": 8.847586206896551 }, { "ECDF": 0.04001, "mean beak depth (mm)": 8.855057471264368 }, { "ECDF": 0.05001, "mean beak depth (mm)": 8.861379310344828 }, { "ECDF": 0.06001, "mean beak depth (mm)": 8.866666666666667 }, { "ECDF": 0.07001, "mean beak depth (mm)": 8.871149425287356 }, { "ECDF": 0.08001, "mean beak depth (mm)": 8.875402298850576 }, { "ECDF": 0.09001, "mean beak depth (mm)": 8.879195402298851 }, { "ECDF": 0.10001, "mean beak depth (mm)": 8.882758620689655 }, { "ECDF": 0.11001, "mean beak depth (mm)": 8.885977011494251 }, { "ECDF": 0.12001, "mean beak depth (mm)": 8.888965517241378 }, { "ECDF": 0.13001, "mean beak depth (mm)": 8.891724137931035 }, { "ECDF": 0.14001, "mean beak depth (mm)": 8.894597701149424 }, { "ECDF": 0.15001, "mean beak depth (mm)": 8.89712643678161 }, { 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute ECDF\n", "x, y = bootcamp_utils.ecdf_vals(bs_reps_1975)\n", "\n", "# Thinned set for plotting\n", "df_ecdf = pd.DataFrame(data={'mean beak depth (mm)': x[::1000],\n", " 'ECDF': y[::1000]})\n", "\n", "# Make the plot\n", "alt.Chart(df_ecdf\n", " ).mark_point(\n", " ).encode(\n", " x=alt.X('mean beak depth (mm):Q', scale=alt.Scale(zero=False)),\n", " y='ECDF:Q')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks Normally distributed, and in fact it must be Normally distributed by the Central Limit Theorem (which we will not discuss here, but we didn't really need to derive; hacker statistics brought us here!). The most probable mean (located at the inflection point on the CDF) we would get is 8.96 mm, which was what was measured, but upon repeating the experiment, we could get a mean as low as about 8.7 mm or as high as about 9.2 mm.\n", "\n", "Let's compute the 95% confidence interval." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([8.84298851, 9.08103448])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conf_int_1975 = np.percentile(bs_reps_1975, [2.5, 97.5])\n", "conf_int_1975" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aside: list comprehensions\n", "\n", "The construction we had for making our bootstrap replicates was a bit clunky:\n", "\n", " # Initialize bootstrap replicas array\n", " bs_reps_1975 = np.empty(n_reps)\n", "\n", " # Compute replicates\n", " for i in range(n_reps):\n", " bs_sample = np.random.choice(bd_1975, size=len(bd_1975))\n", " bs_reps_1975[i] = np.mean(bs_sample)\n", " \n", " We had to set up an empty array, and then loop through each index, draw a bootstrap sample, compute its mean to get the replicate, and then place it in the array. We could, instead, write a function to compute a bootstrap replicate from data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def draw_bs_rep(data, func=np.mean):\n", " \"\"\"Compute a bootstrap replicate from data.\"\"\"\n", " bs_sample = np.random.choice(data, size=len(data))\n", " return func(bs_sample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this function is generic in that it can compute the replicate using any function, such as `np.median()`, `np.std()`, or anything else. With this function in hand, our code starts to look a little cleaner.\n", "```python\n", "# Initialize bootstrap replicas array\n", "bs_reps_1975 = np.empty(n_reps)\n", "\n", "# Compute replicates\n", "for i in range(n_reps):\n", " bs_reps_1975 = draw_bs_rep(bd_1975, func=np.mean)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We still have the problem that we initialize the array and then do the **`for`** loop. Python offers us a very useful alternative to this kind of procedure of initializing and array and the looping over it: **list comprehensions**. This, as usual, is best seen by example." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bs_reps_1975 = [draw_bs_reps(bd_1975, func=np.mean) for _ in range(n_reps)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use a list comprehension to make a list, you enclose the expression in brackets. The first item in the brackets is what you want to put at each element of the list you are creating. Next, you use a **`for`** keyword to set up the iteration over the elements of the list. In our case, the index of the element does not matter; we just want to compute `n_reps` replicates, so we do not need to keep track of the index.\n", "\n", "The result is a list, so we might want to convert it to a Numpy array." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bs_reps_1975 = np.array([draw_bs_rep(bd_1975, func=np.mean) for _ in range(n_reps)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is much more concise and perhaps cleaner syntax.\n", "\n", "Just to show how values of an iterator can be passed for each element, here is how you can make a list of the first 10 cubes." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[i**3 for i in range(1, 11)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now let's use a list comprehension to make bootstrap replicates for the 2012 samples." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compute replicates\n", "bs_reps_2012 = np.array([draw_bs_rep(bd_2012, func=np.mean) for _ in range(n_reps)])\n", " \n", "# Compute the confidence interval\n", "conf_int_2012 = np.percentile(bs_reps_2012, [2.5, 97.5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's print the two confidence intervals next to each other for comparison." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[8.84298851 9.08103448]\n", "[9.07142857 9.30515873]\n" ] } ], "source": [ "print(conf_int_1975)\n", "print(conf_int_2012)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, the 95% confidence intervals for the 2012 and 1975 juuust overlap. This implies that the inherent variation in beak depths is likely not responsible for the observed difference. There was likely some selective pressure toward deeper beaks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Equivalence of bootstrap samples and standard error of the mean\n", "\n", "The **standard error of the mean**, or SEM, is a measure of uncertainty of the estimate of the mean. In other words, if we did the set of measurements again, we would get a different mean. The variability in these measured means is described by the SEM. Specifically, it is the standard deviation of the Normal distribution describing the mean of repeated measurements. So, from bootcamp replicates, we can directly apply this formula." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.060493114594221874" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bs_sem = np.std(bs_reps_1975)\n", "bs_sem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be shown analytically that the SEM can be computed directly from the measurements as the standard deviation of the measurements divided by the square root of the number of measurements." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.06074539219629801" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sem = np.std(bd_1975, ddof=1) / np.sqrt(len(bd_1975))\n", "sem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hey, we got the same result! Bootstrap replicates are easy to generate in general for any statistic, and the error on those statistics might not be as simple as for the mean." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bootstrap confidence interval of the standard deviation\n", "\n", "We are not limited to computing bootstrap confidence intervals of the mean. We could compute bootstrap confidence intervals of any statistic, like the median, standard deviation, the standard deviation by the mean (coefficient of variation), whatever we like. Computing the confidence interval for the standard deviation is the same procedure as we have done; we just put `np.std` in for `np.mean`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.47735063 0.63863112]\n" ] }, { "data": { "application/vnd.vegalite.v2+json": { "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json", "config": { "view": { "height": 300, "width": 400 } }, "data": { "values": [ { "ECDF": 1e-05, "std beak depth (mm)": 0.3605288536309346 }, { "ECDF": 0.01001, "std beak depth (mm)": 0.4621550214369033 }, { "ECDF": 0.02001, "std beak depth (mm)": 0.4733910801973468 }, { "ECDF": 0.03001, "std beak depth (mm)": 0.480462519928335 }, { "ECDF": 0.04001, "std beak depth (mm)": 0.48569141990629244 }, { "ECDF": 0.05001, "std beak depth (mm)": 0.4901477474949997 }, { "ECDF": 0.06001, "std beak depth (mm)": 0.49398083933094655 }, { "ECDF": 0.07001, "std beak depth (mm)": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute replicates\n", "bs_reps_1975 = np.array([draw_bs_rep(bd_1975, func=np.std) for _ in range(n_reps)])\n", " \n", "# Compute confidence interval\n", "conf_int_1975 = np.percentile(bs_reps_1975, [2.5, 97.5])\n", "print(conf_int_1975)\n", "\n", "# Compute ECDF\n", "x, y = bootcamp_utils.ecdf_vals(bs_reps_1975)\n", "\n", "# Thinned set for plotting\n", "df_ecdf = pd.DataFrame(data={'std beak depth (mm)': x[::1000],\n", " 'ECDF': y[::1000]})\n", "\n", "# Make the plot\n", "alt.Chart(df_ecdf\n", " ).mark_point(\n", " ).encode(\n", " x=alt.X('std beak depth (mm):Q', scale=alt.Scale(zero=False)),\n", " y='ECDF:Q')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, we now also have an estimate for the variability in beak depth. It could range from about 0.48 to 0.64 mm." ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }