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January 28, 2021 00:45
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Implementation of intercept-only dyads model from McElreath Ch14/Koester and Leckie 2014
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# McElreath's dyads example\n", | |
"\n", | |
"Covariance of gift giving between households from Chapter 14 pg 462 of Statistical Rethinking 2: https://www.dropbox.com/s/pongh5ga0ey72m5/statisticalrethinking2.pdf?dl=0; code mostly from https://github.com/pymc-devs/resources/blob/master/Rethinking_2/Chp_14.ipynb" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Import python packages\n", | |
"%matplotlib inline\n", | |
"import pandas as pd\n", | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"import pymc3 as pm\n", | |
"import theano as T\n", | |
"import theano.tensor as tt\n", | |
"from pymc3.backends import SQLite\n", | |
"import seaborn as sns\n", | |
"import scipy as sp\n", | |
"import pdb\n", | |
"import arviz as az\n", | |
"from matplotlib.patches import Ellipse, transforms\n", | |
"\n", | |
"# Helper functions\n", | |
"def indexall(L):\n", | |
" poo = []\n", | |
" for p in L:\n", | |
" if not p in poo:\n", | |
" poo.append(p)\n", | |
" Ix = np.array([poo.index(p) for p in L])\n", | |
" return poo,Ix\n", | |
"\n", | |
"# Helper functions\n", | |
"def indexall_B(L,B):\n", | |
" poo = []\n", | |
" for p in L:\n", | |
" if not p in poo:\n", | |
" poo.append(p)\n", | |
" Ix = np.array([poo.index(p) for p in L])\n", | |
" a, b = poo.index(B), 0\n", | |
" poo[b], poo[a] = poo[a], poo[b]\n", | |
" \n", | |
" Ix[Ix==b] = -1\n", | |
" Ix[Ix==a] = 0\n", | |
" Ix[Ix==-1] = a\n", | |
" return poo,Ix\n", | |
"\n", | |
"def subindexall(short,long):\n", | |
" poo = []\n", | |
" out = []\n", | |
" for s,l in zip(short,long):\n", | |
" if not l in poo:\n", | |
" poo.append(l)\n", | |
" out.append(s)\n", | |
" return indexall(out)\n", | |
"\n", | |
"match = lambda a, b: np.array([ b.index(x) if x in b else None for x in a ])\n", | |
"grep = lambda s, l: np.array([i for i in l if s in i])\n", | |
"\n", | |
"# Function to standardize covariates\n", | |
"def stdize(x):\n", | |
" return (x-np.mean(x))/(2*np.std(x))\n", | |
"\n", | |
"# Coefficient of variation\n", | |
"cv = lambda x: np.var(x) / np.mean(x)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Social relations as correlated varying effects\n", | |
"\n", | |
"The data for this example consist of pairs of households (dyads) in Nicaragua, the publicaiton of which you can find in the original paper, [Koester and Leckie 2014](https://www.researchgate.net/profile/Jeremy_Koster/publication/261764179_Food_sharing_networks_in_lowland_Nicaragua_An_application_of_the_social_relations_model_to_count_data/links/5c413437299bf12be3d04539/Food-sharing-networks-in-lowland-Nicaragua-An-application-of-the-social-relations-model-to-count-data.pdf). " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | |
" }\n", | |
"\n", | |
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"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>Unnamed: 0</th>\n", | |
" <th>hidA</th>\n", | |
" <th>hidB</th>\n", | |
" <th>did</th>\n", | |
" <th>giftsAB</th>\n", | |
" <th>giftsBA</th>\n", | |
" <th>offset</th>\n", | |
" <th>drel1</th>\n", | |
" <th>drel2</th>\n", | |
" <th>drel3</th>\n", | |
" <th>drel4</th>\n", | |
" <th>dlndist</th>\n", | |
" <th>dass</th>\n", | |
" <th>d0125</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1</td>\n", | |
" <td>1</td>\n", | |
" <td>2</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>4</td>\n", | |
" <td>0.000</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>-2.790</td>\n", | |
" <td>0.000</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>2</td>\n", | |
" <td>1</td>\n", | |
" <td>3</td>\n", | |
" <td>2</td>\n", | |
" <td>6</td>\n", | |
" <td>31</td>\n", | |
" <td>-0.003</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>-2.817</td>\n", | |
" <td>0.044</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>3</td>\n", | |
" <td>1</td>\n", | |
" <td>4</td>\n", | |
" <td>3</td>\n", | |
" <td>2</td>\n", | |
" <td>5</td>\n", | |
" <td>-0.019</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>-1.886</td>\n", | |
" <td>0.025</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>4</td>\n", | |
" <td>1</td>\n", | |
" <td>5</td>\n", | |
" <td>4</td>\n", | |
" <td>4</td>\n", | |
" <td>2</td>\n", | |
" <td>0.000</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>-1.892</td>\n", | |
" <td>0.011</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>5</td>\n", | |
" <td>1</td>\n", | |
" <td>6</td>\n", | |
" <td>5</td>\n", | |
" <td>8</td>\n", | |
" <td>2</td>\n", | |
" <td>-0.003</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>-3.499</td>\n", | |
" <td>0.022</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" Unnamed: 0 hidA hidB did giftsAB giftsBA offset drel1 drel2 drel3 \\\n", | |
"0 1 1 2 1 0 4 0.000 0 0 1 \n", | |
"1 2 1 3 2 6 31 -0.003 0 1 0 \n", | |
"2 3 1 4 3 2 5 -0.019 0 1 0 \n", | |
"3 4 1 5 4 4 2 0.000 0 1 0 \n", | |
"4 5 1 6 5 8 2 -0.003 1 0 0 \n", | |
"\n", | |
" drel4 dlndist dass d0125 \n", | |
"0 0 -2.790 0.000 0 \n", | |
"1 0 -2.817 0.044 0 \n", | |
"2 0 -1.886 0.025 0 \n", | |
"3 0 -1.892 0.011 0 \n", | |
"4 0 -3.499 0.022 0 " | |
] | |
}, | |
"execution_count": 7, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"kdata = pd.read_csv('kl_dyads.csv')\n", | |
"kdata.head()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Basically households (`hidA` and `hidB`) trade with each other unequally (`giftsAB` and `giftsBA`) in a series of recorded exchanges between pairs (`did`). The question here is how does gift giving vary among households and among dyad pairs. \n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Number of observations\n", | |
"N = len(kdata)\n", | |
"# Number of households\n", | |
"N_households = max(kdata.hidB)\n", | |
"# Dyad ID\n", | |
"did = kdata.did.values-1\n", | |
"# \n", | |
"hidA = kdata.hidA.values-1\n", | |
"# \n", | |
"hidB = kdata.hidB.values-1\n", | |
"# Gifts from A to B\n", | |
"giftsAB = kdata.giftsAB\n", | |
"# Gifts from B to A\n", | |
"giftsBA = kdata.giftsBA" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Dyads basic model\n", | |
"\n", | |
"The question here is to estiamte average giving and receiving rates for Nicaraguan households, as well as rates for particular pairs (dyads) of households. Because gifts from household A to B hold no priority over gifts from B to A, a model that does not depend on direction is required. However there are directions involved, in that a gift from A to B has a direction and is equivalent to a reception from B to A. This implies two key observation-level models:\n", | |
"\n", | |
"$$\n", | |
"\\begin{align}\n", | |
"y_{A\\rightarrow B} \\sim & Poisson(\\lambda_{AB}) \\\\\n", | |
"log(\\lambda_{AB}) = & \\alpha + g_{A} + r_{B} + d_{AB}\n", | |
"\\end{align}\n", | |
"$$\n", | |
"\n", | |
"And conversely\n", | |
"\n", | |
"$$\n", | |
"\\begin{align}\n", | |
"y_{B\\rightarrow A} \\sim & Poisson(\\lambda_{BA}) \\\\\n", | |
"log(\\lambda_{BA}) = & \\alpha + g_{B} + r_{A} + d_{BA}\n", | |
"\\end{align}\n", | |
"$$\n", | |
"\n", | |
"where $\\alpha$ represents an average rate of giving; $g_{A}$ represents the average rate of giving from household A, $r_{B}$ represents the average rate of receiving for household B, and $d_{AB}$ is the dyad-specific giving rate from A to B. Doing this implies that each household H needs varying effects for giving $g_{H}$ and receiving $r_{H}$. On top of this each dyad has their own specific $d_{AB}$ and $d_{BA}$ effects. One goal here is to see if the $g$ and $r$ paremters are correlated - go givers get a lot in return? In addition, we're interested in knowing if there are asymetries in gifts within each dyad - is there a gift balance or not for each pair? These are handled with a couple of multi-normal priors. The first deals with the household effects:\n", | |
"\n", | |
"$$\n", | |
"\\begin{pmatrix} g_{i} \\\\ r_{i} \\end{pmatrix} \\sim MvN \\left( \\begin{pmatrix} 0 \\\\ 0 \\end{pmatrix}, \\begin{pmatrix} \\sigma_{g}^2 & \\sigma_{g}\\sigma_{r}\\rho_{gr} \\\\ \\sigma_{g}\\sigma_{r}\\rho_{gr} & \\sigma_{r}^2 \\end{pmatrix} \\right).\n", | |
"$$\n", | |
"\n", | |
"The second, deals with the dyad effects:\n", | |
"\n", | |
"$$\n", | |
"\\begin{pmatrix} d_{ij} \\\\ d_{ji} \\end{pmatrix} \\sim MvN \\left( \\begin{pmatrix} 0 \\\\ 0 \\end{pmatrix}, \\begin{pmatrix} \\sigma_{d}^2 & \\sigma_{d}^2\\rho_{d} \\\\ \\sigma_{d}^2\\rho_{d} & \\sigma_{d}^2 \\end{pmatrix} \\right).\n", | |
"$$\n", | |
"\n", | |
"It's important to note that the standard deviation $\\sigma_{d}$ is common for both terms because the direction (i or j) doesn't matter. And $\\rho_{d}$ tells us if there is an asymetry ($\\rho_{d}$ negative) or not ($\\rho_{d}$ positive). \n", | |
"\n", | |
"We can implement this in pymc3 as:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"with pm.Model() as Dyads:\n", | |
" # gr matrix of varying effects per household\n", | |
" sd_dist = pm.Exponential.dist(1.0)\n", | |
" chol_gr, _, _ = pm.LKJCholeskyCov(\"chol_gr\", n=2, eta=4, sd_dist=sd_dist, compute_corr=True)\n", | |
" gr = pm.MvNormal(\"gr\", mu=0, chol=chol_gr, shape=(N_households, 2))\n", | |
"\n", | |
" # dyad effects\n", | |
" chol_dyad, _, _ = pm.LKJCholeskyCov(\"chol_dyad\", n=2, eta=8, sd_dist=sd_dist, compute_corr=True)\n", | |
" z = pm.Normal(\"z\", 0, 1, shape=(2, N))\n", | |
" d = pm.Deterministic(\"d\", pm.math.dot(chol_dyad, z).T)\n", | |
"\n", | |
" # linear models\n", | |
" a = pm.Normal(\"a\", 0, 1)\n", | |
" lambdaAB = tt.exp(a + gr[hidA, 0] + gr[hidB, 1] + d[did, 0])\n", | |
" lambdaBA = tt.exp(a + gr[hidB, 0] + gr[hidA, 1] + d[did, 1])\n", | |
"\n", | |
" # likelihood\n", | |
" giftsAB = pm.Poisson(\"giftsAB\", lambdaAB, observed=giftsAB)\n", | |
" giftsBA = pm.Poisson(\"giftsBA\", lambdaBA, observed=giftsBA)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Auto-assigning NUTS sampler...\n", | |
"Initializing NUTS using jitter+adapt_diag...\n", | |
"Multiprocess sampling (4 chains in 4 jobs)\n", | |
"NUTS: [a, z, chol_dyad, gr, chol_gr]\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/html": [ | |
"\n", | |
" <div>\n", | |
" <style>\n", | |
" /* Turns off some styling */\n", | |
" progress {\n", | |
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" border: none;\n", | |
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" 100.00% [12000/12000 01:42<00:00 Sampling 4 chains, 0 divergences]\n", | |
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"<IPython.core.display.HTML object>" | |
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}, | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 114 seconds.\n", | |
"The number of effective samples is smaller than 25% for some parameters.\n" | |
] | |
} | |
], | |
"source": [ | |
"with Dyads:\n", | |
" trace = pm.sample(2000)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Convert into Arviz object\n", | |
"Dyadz = az.from_pymc3(trace, dims={\"d\": [\"Dyad\", \"House\"], \"gr\": [\"Household\", \"Rate\"]}, coords={\"Rate\": [\"giving\", \"receiving\"]},)\n", | |
"# Rename sub-objects within chol_gr and chol_dyad\n", | |
"PostDyads = Dyadz.posterior = Dyadz.posterior.rename_vars({\"chol_gr_corr\": \"Rho_gr\", \"chol_gr_stds\": \"sigma_gr\", \"chol_dyad_corr\": \"Rho_d\", \"chol_dyad_stds\": \"sigma_d\",})" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 23, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>mean</th>\n", | |
" <th>sd</th>\n", | |
" <th>hdi_3%</th>\n", | |
" <th>hdi_97%</th>\n", | |
" <th>mcse_mean</th>\n", | |
" <th>mcse_sd</th>\n", | |
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" <th>ess_sd</th>\n", | |
" <th>ess_bulk</th>\n", | |
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" <th>r_hat</th>\n", | |
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" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>Rho_gr[0,0]</th>\n", | |
" <td>1.00</td>\n", | |
" <td>0.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>NaN</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Rho_gr[0,1]</th>\n", | |
" <td>-0.41</td>\n", | |
" <td>0.20</td>\n", | |
" <td>-0.77</td>\n", | |
" <td>-0.04</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2665.16</td>\n", | |
" <td>2623.48</td>\n", | |
" <td>2615.43</td>\n", | |
" <td>4633.08</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Rho_gr[1,0]</th>\n", | |
" <td>-0.41</td>\n", | |
" <td>0.20</td>\n", | |
" <td>-0.77</td>\n", | |
" <td>-0.04</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2665.16</td>\n", | |
" <td>2623.48</td>\n", | |
" <td>2615.43</td>\n", | |
" <td>4633.08</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Rho_gr[1,1]</th>\n", | |
" <td>1.00</td>\n", | |
" <td>0.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8007.91</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>sigma_gr[0]</th>\n", | |
" <td>0.83</td>\n", | |
" <td>0.14</td>\n", | |
" <td>0.59</td>\n", | |
" <td>1.08</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>4177.64</td>\n", | |
" <td>4177.64</td>\n", | |
" <td>4095.86</td>\n", | |
" <td>5747.06</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>sigma_gr[1]</th>\n", | |
" <td>0.41</td>\n", | |
" <td>0.09</td>\n", | |
" <td>0.25</td>\n", | |
" <td>0.58</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2214.11</td>\n", | |
" <td>2214.11</td>\n", | |
" <td>2097.70</td>\n", | |
" <td>2956.52</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", | |
"Rho_gr[0,0] 1.00 0.00 1.00 1.00 0.0 0.0 8000.00 \n", | |
"Rho_gr[0,1] -0.41 0.20 -0.77 -0.04 0.0 0.0 2665.16 \n", | |
"Rho_gr[1,0] -0.41 0.20 -0.77 -0.04 0.0 0.0 2665.16 \n", | |
"Rho_gr[1,1] 1.00 0.00 1.00 1.00 0.0 0.0 8000.00 \n", | |
"sigma_gr[0] 0.83 0.14 0.59 1.08 0.0 0.0 4177.64 \n", | |
"sigma_gr[1] 0.41 0.09 0.25 0.58 0.0 0.0 2214.11 \n", | |
"\n", | |
" ess_sd ess_bulk ess_tail r_hat \n", | |
"Rho_gr[0,0] 8000.00 8000.00 8000.00 NaN \n", | |
"Rho_gr[0,1] 2623.48 2615.43 4633.08 1.0 \n", | |
"Rho_gr[1,0] 2623.48 2615.43 4633.08 1.0 \n", | |
"Rho_gr[1,1] 8000.00 8007.91 8000.00 1.0 \n", | |
"sigma_gr[0] 4177.64 4095.86 5747.06 1.0 \n", | |
"sigma_gr[1] 2214.11 2097.70 2956.52 1.0 " | |
] | |
}, | |
"execution_count": 23, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"az.summary(Dyadz, var_names=[\"Rho_gr\", \"sigma_gr\"], round_to=2)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The correlation here is -0.41, which implies that individuals who give more thend to receive less across all dyads. The standard deviation for giving (0.83) is twice as varaible as rates of receiving (0.41).\n", | |
"\n", | |
"Let's take a look at the estimated household giving and receiving rates" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Household level log-giving rate posteriors\n", | |
"g = (PostDyads[\"a\"] + PostDyads[\"gr\"].sel(Rate=\"giving\")).stack(sample=(\"chain\", \"draw\"))\n", | |
"# Household level log-receiving rate posteriors\n", | |
"r = (PostDyads[\"a\"] + PostDyads[\"gr\"].sel(Rate=\"receiving\")).stack(sample=(\"chain\", \"draw\"))\n", | |
"\n", | |
"# Household expected giving rates\n", | |
"Eg_mu = np.exp(g).mean(dim=\"sample\")\n", | |
"# Household expected receiving rates\n", | |
"Er_mu = np.exp(r).mean(dim=\"sample\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 30, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"_, ax = plt.subplots(1, 1, constrained_layout=True)\n", | |
"x = np.linspace(0, 9, 101)\n", | |
"ax.plot(x, x, \"k--\", lw=1.5, alpha=0.4)\n", | |
"\n", | |
"# Plot uncertainty ellipses\n", | |
"for house in range(25):\n", | |
" Sigma = np.cov(np.stack([np.exp(g[house].values), np.exp(r[house].values)]))\n", | |
" Mu = np.stack([np.exp(g[house].values.mean()), np.exp(r[house].values.mean())])\n", | |
" pearson = Sigma[0, 1] / np.sqrt(Sigma[0, 0] * Sigma[1, 1])\n", | |
" ellipse = Ellipse((0, 0),np.sqrt(1 + pearson),np.sqrt(1 - pearson),edgecolor=\"k\",alpha=0.5,facecolor=\"none\",)\n", | |
" std_dev = sp.stats.norm.ppf((1 + np.sqrt(0.5)) / 2)\n", | |
" scale_x = 2 * std_dev * np.sqrt(Sigma[0, 0])\n", | |
" scale_y = 2 * std_dev * np.sqrt(Sigma[1, 1])\n", | |
" scale = transforms.Affine2D().rotate_deg(45).scale(scale_x, scale_y)\n", | |
" ellipse.set_transform(scale.translate(Mu[0], Mu[1]) + ax.transData)\n", | |
" ax.add_patch(ellipse)\n", | |
"\n", | |
"# household means\n", | |
"ax.plot(Eg_mu, Er_mu, \"ko\", mfc=\"white\", lw=1.5)\n", | |
"\n", | |
"ax.set(xlim=(0, 8.6),ylim=(0, 8.6),xlabel=\"generalized giving\",ylabel=\"generalized receiving\",);\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"What becomes clear in this is that there are a bunch of generous households that receive very little and that there are some households that don't give much but often receive more. \n", | |
"\n", | |
"At the dyad level, what do the paired gift exchange relationships look like?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | |
" }\n", | |
"\n", | |
" .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | |
" }\n", | |
"\n", | |
" .dataframe thead th {\n", | |
" text-align: right;\n", | |
" }\n", | |
"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>mean</th>\n", | |
" <th>sd</th>\n", | |
" <th>hdi_3%</th>\n", | |
" <th>hdi_97%</th>\n", | |
" <th>mcse_mean</th>\n", | |
" <th>mcse_sd</th>\n", | |
" <th>ess_mean</th>\n", | |
" <th>ess_sd</th>\n", | |
" <th>ess_bulk</th>\n", | |
" <th>ess_tail</th>\n", | |
" <th>r_hat</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>Rho_d[0,0]</th>\n", | |
" <td>1.00</td>\n", | |
" <td>0.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>NaN</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Rho_d[0,1]</th>\n", | |
" <td>0.88</td>\n", | |
" <td>0.03</td>\n", | |
" <td>0.82</td>\n", | |
" <td>0.94</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2159.19</td>\n", | |
" <td>2148.23</td>\n", | |
" <td>2123.02</td>\n", | |
" <td>3680.47</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Rho_d[1,0]</th>\n", | |
" <td>0.88</td>\n", | |
" <td>0.03</td>\n", | |
" <td>0.82</td>\n", | |
" <td>0.94</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2159.19</td>\n", | |
" <td>2148.23</td>\n", | |
" <td>2123.02</td>\n", | |
" <td>3680.47</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Rho_d[1,1]</th>\n", | |
" <td>1.00</td>\n", | |
" <td>0.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>1.00</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8000.00</td>\n", | |
" <td>8028.82</td>\n", | |
" <td>7443.49</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>sigma_d[0]</th>\n", | |
" <td>1.07</td>\n", | |
" <td>0.07</td>\n", | |
" <td>0.95</td>\n", | |
" <td>1.20</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2869.21</td>\n", | |
" <td>2845.59</td>\n", | |
" <td>2913.44</td>\n", | |
" <td>3676.12</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>sigma_d[1]</th>\n", | |
" <td>1.13</td>\n", | |
" <td>0.07</td>\n", | |
" <td>1.01</td>\n", | |
" <td>1.26</td>\n", | |
" <td>0.0</td>\n", | |
" <td>0.0</td>\n", | |
" <td>2668.97</td>\n", | |
" <td>2668.97</td>\n", | |
" <td>2675.29</td>\n", | |
" <td>3994.24</td>\n", | |
" <td>1.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", | |
"Rho_d[0,0] 1.00 0.00 1.00 1.00 0.0 0.0 8000.00 \n", | |
"Rho_d[0,1] 0.88 0.03 0.82 0.94 0.0 0.0 2159.19 \n", | |
"Rho_d[1,0] 0.88 0.03 0.82 0.94 0.0 0.0 2159.19 \n", | |
"Rho_d[1,1] 1.00 0.00 1.00 1.00 0.0 0.0 8000.00 \n", | |
"sigma_d[0] 1.07 0.07 0.95 1.20 0.0 0.0 2869.21 \n", | |
"sigma_d[1] 1.13 0.07 1.01 1.26 0.0 0.0 2668.97 \n", | |
"\n", | |
" ess_sd ess_bulk ess_tail r_hat \n", | |
"Rho_d[0,0] 8000.00 8000.00 8000.00 NaN \n", | |
"Rho_d[0,1] 2148.23 2123.02 3680.47 1.0 \n", | |
"Rho_d[1,0] 2148.23 2123.02 3680.47 1.0 \n", | |
"Rho_d[1,1] 8000.00 8028.82 7443.49 1.0 \n", | |
"sigma_d[0] 2845.59 2913.44 3676.12 1.0 \n", | |
"sigma_d[1] 2668.97 2675.29 3994.24 1.0 " | |
] | |
}, | |
"execution_count": 32, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"az.summary(Dyadz, var_names=[\"Rho_d\", \"sigma_d\"], round_to=2)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Grab dyad giving\n", | |
"dy1 = PostDyads[\"d\"].sel(House=0).mean(dim=(\"chain\", \"draw\"))\n", | |
"dy2 = PostDyads[\"d\"].sel(House=1).mean(dim=(\"chain\", \"draw\"))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 34, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"_, ax = plt.subplots(1, 1, constrained_layout=True)\n", | |
"x = np.linspace(-2, 4, 101)\n", | |
"\n", | |
"ax.plot(x, x, \"k--\", lw=1.5, alpha=0.4)\n", | |
"ax.axhline(linewidth=1.5, color=\"k\", ls=\"--\", alpha=0.4)\n", | |
"ax.axvline(linewidth=1.5, color=\"k\", ls=\"--\", alpha=0.4)\n", | |
"ax.plot(dy1, dy2, \"ko\", mfc=\"none\", lw=1.5, alpha=0.6)\n", | |
"\n", | |
"ax.set(xlim=(-2, 4),ylim=(-2, 4),xlabel=\"household A in dyad\",ylabel=\"household B in dyad\",);" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Here we can see that, once we have accounted for individual household behaviour, there is a remarkable consistency within dyads - balanced giving." | |
] | |
} | |
], | |
"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": "3.8.3" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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hidA | hidB | did | giftsAB | giftsBA | offset | drel1 | drel2 | drel3 | drel4 | dlndist | dass | d0125 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 2 | 1 | 0 | 4 | 0 | 0 | 0 | 1 | 0 | -2.79 | 0 | 0 | |
2 | 1 | 3 | 2 | 6 | 31 | -0.003 | 0 | 1 | 0 | 0 | -2.817 | 0.044 | 0 | |
3 | 1 | 4 | 3 | 2 | 5 | -0.019 | 0 | 1 | 0 | 0 | -1.886 | 0.025 | 0 | |
4 | 1 | 5 | 4 | 4 | 2 | 0 | 0 | 1 | 0 | 0 | -1.892 | 0.011 | 0 | |
5 | 1 | 6 | 5 | 8 | 2 | -0.003 | 1 | 0 | 0 | 0 | -3.499 | 0.022 | 0 | |
6 | 1 | 7 | 6 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | -1.853 | 0.071 | 0 | |
7 | 1 | 8 | 7 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | -1.475 | 0.046 | 0 | |
8 | 1 | 9 | 8 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | -1.644 | 0.003 | 0 | |
9 | 1 | 10 | 9 | 10 | 110 | -0.186 | 1 | 0 | 0 | 0 | -1.897 | 0.552 | 0 | |
10 | 1 | 11 | 10 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | -2.379 | 0.018 | 0 | |
11 | 1 | 12 | 11 | 0 | 0 | -0.471 | 0 | 0 | 1 | 0 | -2.2 | 0.004 | 0 | |
12 | 1 | 13 | 12 | 0 | 6 | -0.019 | 0 | 0 | 1 | 0 | -2.117 | 0.004 | 0 | |
13 | 1 | 14 | 13 | 1 | 11 | -0.011 | 0 | 1 | 0 | 0 | -2.83 | 0.036 | 0 | |
14 | 1 | 15 | 14 | 6 | 0 | -0.014 | 0 | 0 | 0 | 0 | -2.492 | 0.006 | 0 | |
15 | 1 | 16 | 15 | 0 | 1 | -0.019 | 0 | 0 | 0 | 0 | -1.848 | 0.014 | 0 | |
16 | 1 | 17 | 16 | 0 | 4 | -0.019 | 0 | 0 | 0 | 1 | -2.58 | 0 | 0 | |
17 | 1 | 18 | 17 | 0 | 0 | -0.019 | 0 | 0 | 0 | 0 | -2.184 | 0.025 | 0 | |
18 | 1 | 19 | 18 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | -1.883 | 0 | 0 | |
19 | 1 | 20 | 19 | 0 | 0 | -0.028 | 0 | 0 | 1 | 0 | -2.089 | 0.012 | 0 | |
20 | 1 | 21 | 20 | 0 | 7 | -0.031 | 0 | 0 | 0 | 1 | -2.364 | 0.007 | 0 | |
21 | 1 | 22 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -2.538 | 0.01 | 0 | |
22 | 1 | 23 | 22 | 0 | 13 | -0.092 | 0 | 1 | 0 | 0 | -4.068 | 0.012 | 0 | |
23 | 1 | 24 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1.824 | 0 | 0 | |
24 | 1 | 25 | 24 | 16 | 2 | -0.637 | 0 | 0 | 1 | 0 | -1.766 | 0.08 | 1 | |
25 | 2 | 3 | 25 | 1 | 3 | -0.003 | 0 | 0 | 1 | 0 | -2.111 | 0.007 | 0 | |
26 | 2 | 4 | 26 | 2 | 1 | -0.019 | 0 | 0 | 1 | 0 | -1.58 | 0.013 | 0 | |
27 | 2 | 5 | 27 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | -1.571 | 0 | 0 | |
28 | 2 | 6 | 28 | 3 | 0 | -0.003 | 0 | 1 | 0 | 0 | -2.401 | 0 | 0 | |
29 | 2 | 7 | 29 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -1.609 | 0 | 0 | |
30 | 2 | 8 | 30 | 43 | 10 | 0 | 1 | 0 | 0 | 0 | -1.274 | 0.252 | 0 | |
31 | 2 | 9 | 31 | 5 | 4 | 0 | 1 | 0 | 0 | 0 | -1.534 | 0.035 | 0 | |
32 | 2 | 10 | 32 | 0 | 1 | -0.186 | 0 | 0 | 0 | 1 | -1.56 | 0.01 | 0 | |
33 | 2 | 11 | 33 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | -1.877 | 0.004 | 0 | |
34 | 2 | 12 | 34 | 0 | 0 | -0.471 | 0 | 0 | 0 | 1 | -1.779 | 0 | 0 | |
35 | 2 | 13 | 35 | 1 | 2 | -0.019 | 0 | 0 | 0 | 1 | -1.711 | 0 | 0 | |
36 | 2 | 14 | 36 | 1 | 0 | -0.011 | 0 | 0 | 1 | 0 | -2.277 | 0 | 0 | |
37 | 2 | 15 | 37 | 2 | 0 | -0.014 | 0 | 0 | 0 | 0 | -2.074 | 0.027 | 0 | |
38 | 2 | 16 | 38 | 4 | 2 | -0.019 | 0 | 0 | 0 | 1 | -1.567 | 0 | 0 | |
39 | 2 | 17 | 39 | 22 | 16 | -0.019 | 1 | 0 | 0 | 0 | -3.434 | 0.239 | 0 | |
40 | 2 | 18 | 40 | 1 | 1 | -0.019 | 0 | 0 | 0 | 1 | -2.198 | 0.019 | 0 | |
41 | 2 | 19 | 41 | 6 | 9 | 0 | 1 | 0 | 0 | 0 | -1.832 | 0.093 | 0 | |
42 | 2 | 20 | 42 | 0 | 5 | -0.028 | 0 | 0 | 0 | 1 | -1.953 | 0 | 0 | |
43 | 2 | 21 | 43 | 1 | 3 | -0.031 | 0 | 0 | 1 | 0 | -2.078 | 0.011 | 0 | |
44 | 2 | 22 | 44 | 14 | 25 | 0 | 1 | 0 | 0 | 0 | -2.322 | 0.194 | 0 | |
45 | 2 | 23 | 45 | 2 | 5 | -0.092 | 0 | 0 | 1 | 0 | -2.558 | 0.017 | 0 | |
46 | 2 | 24 | 46 | 5 | 2 | 0 | 0 | 0 | 1 | 0 | -1.692 | 0.07 | 0 | |
47 | 2 | 25 | 47 | 1 | 0 | -0.637 | 0 | 0 | 0 | 1 | -1.464 | 0.01 | 0 | |
48 | 3 | 4 | 48 | 15 | 0 | -0.022 | 0 | 1 | 0 | 0 | -2.252 | 0.085 | 0 | |
49 | 3 | 5 | 49 | 23 | 2 | -0.003 | 0 | 1 | 0 | 0 | -2.307 | 0.116 | 0 | |
50 | 3 | 6 | 50 | 49 | 4 | -0.005 | 1 | 0 | 0 | 0 | -3.442 | 0.118 | 0 | |
51 | 3 | 7 | 51 | 6 | 0 | -0.003 | 0 | 0 | 0 | 0 | -2.056 | 0.012 | 0 | |
52 | 3 | 8 | 52 | 3 | 0 | -0.003 | 0 | 0 | 0 | 1 | -1.678 | 0.02 | 0 | |
53 | 3 | 9 | 53 | 0 | 0 | -0.003 | 0 | 0 | 0 | 1 | -1.675 | 0.009 | 0 | |
54 | 3 | 10 | 54 | 2 | 8 | -0.186 | 0 | 0 | 1 | 0 | -2.384 | 0.018 | 0 | |
55 | 3 | 11 | 55 | 11 | 1 | -0.003 | 0 | 1 | 0 | 0 | -3.321 | 0.024 | 0 | |
56 | 3 | 12 | 56 | 7 | 0 | -0.471 | 0 | 0 | 1 | 0 | -2.814 | 0.016 | 0 | |
57 | 3 | 13 | 57 | 7 | 2 | -0.022 | 0 | 0 | 1 | 0 | -2.763 | 0.026 | 0 | |
58 | 3 | 14 | 58 | 12 | 2 | -0.014 | 0 | 1 | 0 | 0 | -2.798 | 0.113 | 0 | |
59 | 3 | 15 | 59 | 22 | 4 | -0.017 | 1 | 0 | 0 | 0 | -2.635 | 0.028 | 0 | |
60 | 3 | 16 | 60 | 5 | 1 | -0.022 | 0 | 0 | 0 | 0 | -2.15 | 0.046 | 0 | |
61 | 3 | 17 | 61 | 10 | 5 | -0.022 | 0 | 0 | 0 | 1 | -2.024 | 0.015 | 0 | |
62 | 3 | 18 | 62 | 0 | 0 | -0.022 | 0 | 0 | 0 | 0 | -1.958 | 0.033 | 0 | |
63 | 3 | 19 | 63 | 4 | 0 | -0.003 | 0 | 0 | 0 | 1 | -1.797 | 0.014 | 0 | |
64 | 3 | 20 | 64 | 3 | 1 | -0.031 | 0 | 0 | 1 | 0 | -2.023 | 0.039 | 0 | |
65 | 3 | 21 | 65 | 2 | 1 | -0.033 | 0 | 0 | 0 | 1 | -2.348 | 0.006 | 0 | |
66 | 3 | 22 | 66 | 2 | 1 | -0.003 | 0 | 0 | 0 | 1 | -2.304 | 0.006 | 0 | |
67 | 3 | 23 | 67 | 13 | 26 | -0.095 | 0 | 1 | 0 | 0 | -3.107 | 0.146 | 0 | |
68 | 3 | 24 | 68 | 2 | 0 | -0.003 | 0 | 0 | 0 | 0 | -1.84 | 0.041 | 0 | |
69 | 3 | 25 | 69 | 9 | 0 | -0.637 | 0 | 0 | 1 | 0 | -2.178 | 0.018 | 0 | |
70 | 4 | 5 | 70 | 1 | 8 | -0.019 | 0 | 1 | 0 | 0 | -4.035 | 0.14 | 0 | |
71 | 4 | 6 | 71 | 19 | 4 | -0.022 | 1 | 0 | 0 | 0 | -2.099 | 0.093 | 0 | |
72 | 4 | 7 | 72 | 0 | 1 | -0.019 | 0 | 0 | 1 | 0 | -2.926 | 0.04 | 0 | |
73 | 4 | 8 | 73 | 0 | 0 | -0.019 | 0 | 0 | 0 | 1 | -1.473 | 0 | 0 | |
74 | 4 | 9 | 74 | 0 | 1 | -0.019 | 0 | 0 | 0 | 1 | -1.999 | 0.015 | 0 | |
75 | 4 | 10 | 75 | 1 | 8 | -0.21 | 0 | 0 | 1 | 0 | -2.968 | 0.01 | 0 | |
76 | 4 | 11 | 76 | 2 | 0 | -0.019 | 0 | 1 | 0 | 0 | -2.637 | 0.027 | 0 | |
77 | 4 | 12 | 77 | 1 | 3 | -0.502 | 0 | 0 | 1 | 0 | -3.089 | 0.012 | 0 | |
78 | 4 | 13 | 78 | 1 | 11 | -0.059 | 0 | 0 | 1 | 0 | -2.896 | 0.02 | 0 | |
79 | 4 | 14 | 79 | 0 | 6 | -0.031 | 0 | 1 | 0 | 0 | -2.252 | 0.147 | 0 | |
80 | 4 | 15 | 80 | 2 | 2 | -0.033 | 0 | 0 | 0 | 0 | -2.453 | 0.021 | 0 | |
81 | 4 | 16 | 81 | 7 | 17 | -0.059 | 0 | 0 | 0 | 0 | -3.942 | 0.064 | 0 | |
82 | 4 | 17 | 82 | 1 | 3 | -0.059 | 0 | 0 | 0 | 1 | -1.594 | 0.008 | 0 | |
83 | 4 | 18 | 83 | 0 | 1 | -0.059 | 0 | 0 | 0 | 0 | -1.813 | 0.028 | 0 | |
84 | 4 | 19 | 84 | 0 | 2 | -0.019 | 0 | 0 | 0 | 1 | -1.866 | 0 | 0 | |
85 | 4 | 20 | 85 | 0 | 2 | -0.048 | 0 | 0 | 1 | 0 | -2.07 | 0.08 | 0 | |
86 | 4 | 21 | 86 | 0 | 5 | -0.051 | 0 | 0 | 0 | 1 | -2.273 | 0 | 0 | |
87 | 4 | 22 | 87 | 0 | 2 | -0.019 | 0 | 0 | 0 | 1 | -2.033 | 0.026 | 0 | |
88 | 4 | 23 | 88 | 1 | 13 | -0.092 | 0 | 1 | 0 | 0 | -1.947 | 0.115 | 0 | |
89 | 4 | 24 | 89 | 1 | 0 | -0.019 | 0 | 0 | 0 | 0 | -2.108 | 0 | 0 | |
90 | 4 | 25 | 90 | 1 | 1 | -0.674 | 0 | 1 | 0 | 0 | -2.857 | 0.03 | 0 | |
91 | 5 | 6 | 91 | 13 | 17 | -0.003 | 1 | 0 | 0 | 0 | -2.113 | 0.362 | 0 | |
92 | 5 | 7 | 92 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | -2.644 | 0 | 0 | |
93 | 5 | 8 | 93 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -1.551 | 0 | 0 | |
94 | 5 | 9 | 94 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | -1.879 | 0 | 0 | |
95 | 5 | 10 | 95 | 2 | 17 | -0.186 | 0 | 0 | 1 | 0 | -3.386 | 0 | 0 | |
96 | 5 | 11 | 96 | 1 | 3 | 0 | 0 | 1 | 0 | 0 | -2.749 | 0.044 | 0 | |
97 | 5 | 12 | 97 | 1 | 5 | -0.471 | 0 | 0 | 1 | 0 | -3.21 | 0.058 | 0 | |
98 | 5 | 13 | 98 | 2 | 19 | -0.019 | 0 | 0 | 1 | 0 | -3.156 | 0.068 | 0 | |
99 | 5 | 14 | 99 | 1 | 2 | -0.011 | 0 | 1 | 0 | 0 | -2.209 | 0 | 0 | |
100 | 5 | 15 | 100 | 4 | 2 | -0.014 | 0 | 0 | 0 | 0 | -2.371 | 0.036 | 0 | |
101 | 5 | 16 | 101 | 3 | 7 | -0.019 | 0 | 0 | 0 | 0 | -3.296 | 0 | 0 | |
102 | 5 | 17 | 102 | 1 | 3 | -0.019 | 0 | 0 | 0 | 1 | -1.572 | 0.016 | 0 | |
103 | 5 | 18 | 103 | 0 | 0 | -0.019 | 0 | 0 | 0 | 0 | -1.748 | 0 | 0 | |
104 | 5 | 19 | 104 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | -1.776 | 0 | 0 | |
105 | 5 | 20 | 105 | 3 | 3 | -0.028 | 0 | 0 | 1 | 0 | -1.97 | 0.017 | 0 | |
106 | 5 | 21 | 106 | 1 | 3 | -0.031 | 0 | 0 | 0 | 1 | -2.178 | 0 | 0 | |
107 | 5 | 22 | 107 | 1 | 5 | 0 | 0 | 0 | 0 | 1 | -1.97 | 0 | 0 | |
108 | 5 | 23 | 108 | 0 | 11 | -0.092 | 0 | 1 | 0 | 0 | -1.965 | 0.034 | 0 | |
109 | 5 | 24 | 109 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -1.982 | 0 | 0 | |
110 | 5 | 25 | 110 | 3 | 1 | -0.637 | 0 | 0 | 1 | 0 | -3.201 | 0.098 | 0 | |
111 | 6 | 7 | 111 | 0 | 2 | -0.003 | 0 | 0 | 0 | 0 | -2.018 | 0.009 | 0 | |
112 | 6 | 8 | 112 | 0 | 0 | -0.003 | 0 | 0 | 1 | 0 | -1.544 | 0.031 | 0 | |
113 | 6 | 9 | 113 | 0 | 0 | -0.003 | 0 | 0 | 1 | 0 | -1.718 | 0 | 0 | |
114 | 6 | 10 | 114 | 0 | 29 | -0.186 | 0 | 0 | 1 | 0 | -2.121 | 0.027 | 0 | |
115 | 6 | 11 | 115 | 3 | 4 | -0.003 | 1 | 0 | 0 | 0 | -2.771 | 0.033 | 0 | |
116 | 6 | 12 | 116 | 0 | 1 | -0.475 | 0 | 0 | 1 | 0 | -2.517 | 0.028 | 0 | |
117 | 6 | 13 | 117 | 0 | 9 | -0.019 | 0 | 0 | 1 | 0 | -2.404 | 0 | 0 | |
118 | 6 | 14 | 118 | 5 | 25 | -0.011 | 1 | 0 | 0 | 0 | -3.139 | 0.092 | 0 | |
119 | 6 | 15 | 119 | 0 | 2 | -0.017 | 0 | 0 | 0 | 0 | -2.763 | 0 | 0 | |
120 | 6 | 16 | 120 | 0 | 2 | -0.019 | 0 | 0 | 0 | 1 | -2.041 | 0.007 | 0 | |
121 | 6 | 17 | 121 | 0 | 6 | -0.019 | 0 | 0 | 1 | 0 | -2.301 | 0 | 0 | |
122 | 6 | 18 | 122 | 0 | 0 | -0.019 | 0 | 0 | 0 | 1 | -2.153 | 0.012 | 0 | |
123 | 6 | 19 | 123 | 0 | 2 | -0.003 | 0 | 0 | 1 | 0 | -1.913 | 0.01 | 0 | |
124 | 6 | 20 | 124 | 0 | 1 | -0.031 | 0 | 0 | 1 | 0 | -2.156 | 0 | 0 | |
125 | 6 | 21 | 125 | 0 | 6 | -0.031 | 0 | 0 | 1 | 0 | -2.51 | 0.002 | 0 | |
126 | 6 | 22 | 126 | 1 | 0 | -0.003 | 0 | 0 | 1 | 0 | -2.569 | 0.005 | 0 | |
127 | 6 | 23 | 127 | 4 | 50 | -0.092 | 1 | 0 | 0 | 0 | -3.882 | 0.099 | 0 | |
128 | 6 | 24 | 128 | 0 | 0 | -0.003 | 0 | 0 | 0 | 1 | -1.906 | 0.016 | 0 | |
129 | 6 | 25 | 129 | 2 | 1 | -0.637 | 0 | 0 | 1 | 0 | -1.959 | 0.033 | 0 | |
130 | 7 | 8 | 130 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -1.276 | 0.037 | 0 | |
131 | 7 | 9 | 131 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | -2.503 | 0.024 | 0 | |
132 | 7 | 10 | 132 | 0 | 4 | -0.186 | 0 | 0 | 0 | 0 | -2.264 | 0 | 0 | |
133 | 7 | 11 | 133 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | -2.265 | 0.022 | 0 | |
134 | 7 | 12 | 134 | 0 | 2 | -0.471 | 0 | 0 | 0 | 0 | -2.525 | 0.123 | 0 | |
135 | 7 | 13 | 135 | 3 | 6 | -0.019 | 0 | 0 | 0 | 0 | -2.309 | 0 | 0 | |
136 | 7 | 14 | 136 | 2 | 1 | -0.011 | 0 | 0 | 0 | 0 | -2.31 | 0.015 | 0 | |
137 | 7 | 15 | 137 | 3 | 4 | -0.014 | 0 | 0 | 0 | 0 | -2.588 | 0.065 | 0 | |
138 | 7 | 16 | 138 | 8 | 4 | -0.019 | 0 | 0 | 0 | 0 | -3.33 | 0.101 | 0 | |
139 | 7 | 17 | 139 | 2 | 4 | -0.019 | 0 | 0 | 0 | 0 | -1.667 | 0 | 0 | |
140 | 7 | 18 | 140 | 0 | 1 | -0.019 | 0 | 0 | 0 | 1 | -2.041 | 0.149 | 0 | |
141 | 7 | 19 | 141 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -2.216 | 0 | 0 | |
142 | 7 | 20 | 142 | 0 | 8 | -0.028 | 0 | 0 | 0 | 0 | -2.445 | 0.07 | 0 | |
143 | 7 | 21 | 143 | 0 | 18 | -0.031 | 0 | 0 | 0 | 0 | -2.552 | 0.074 | 0 | |
144 | 7 | 22 | 144 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | -2.221 | 0.035 | 0 | |
145 | 7 | 23 | 145 | 0 | 7 | -0.092 | 0 | 0 | 0 | 0 | -1.875 | 0.028 | 0 | |
146 | 7 | 24 | 146 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | -2.651 | 0.094 | 0 | |
147 | 7 | 25 | 147 | 2 | 2 | -0.637 | 0 | 0 | 0 | 0 | -2.199 | 0.032 | 0 | |
148 | 8 | 9 | 148 | 2 | 5 | 0 | 0 | 1 | 0 | 0 | -1.031 | 0.07 | 0 | |
149 | 8 | 10 | 149 | 0 | 4 | -0.186 | 0 | 0 | 0 | 0 | -1.723 | 0 | 0 | |
150 | 8 | 11 | 150 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | -1.686 | 0 | 0 | |
151 | 8 | 12 | 151 | 2 | 0 | -0.471 | 0 | 0 | 0 | 0 | -1.6 | 0 | 0 | |
152 | 8 | 13 | 152 | 1 | 3 | -0.019 | 0 | 0 | 0 | 0 | -1.714 | 0 | 0 | |
153 | 8 | 14 | 153 | 2 | 8 | -0.011 | 0 | 0 | 0 | 1 | -1.397 | 0.051 | 0 | |
154 | 8 | 15 | 154 | 1 | 0 | -0.014 | 0 | 0 | 0 | 0 | -1.374 | 0.013 | 0 | |
155 | 8 | 16 | 155 | 2 | 2 | -0.019 | 0 | 0 | 0 | 0 | -1.392 | 0 | 0 | |
156 | 8 | 17 | 156 | 2 | 16 | -0.019 | 0 | 1 | 0 | 0 | -1.194 | 0.139 | 0 | |
157 | 8 | 18 | 157 | 0 | 2 | -0.019 | 0 | 0 | 0 | 0 | -1.115 | 0 | 0 | |
158 | 8 | 19 | 158 | 1 | 10 | 0 | 0 | 1 | 0 | 0 | -1.051 | 0.13 | 0 | |
159 | 8 | 20 | 159 | 0 | 11 | -0.028 | 0 | 0 | 0 | 0 | -1.154 | 0 | 0 | |
160 | 8 | 21 | 160 | 0 | 4 | -0.031 | 0 | 0 | 0 | 1 | -1.277 | 0.039 | 0 | |
161 | 8 | 22 | 161 | 4 | 6 | 0 | 0 | 1 | 0 | 0 | -1.25 | 0.06 | 0 | |
162 | 8 | 23 | 162 | 0 | 7 | -0.092 | 0 | 0 | 0 | 1 | -1.55 | 0 | 0 | |
163 | 8 | 24 | 163 | 1 | 2 | 0 | 0 | 0 | 1 | 0 | -1.098 | 0 | 0 | |
164 | 8 | 25 | 164 | 0 | 2 | -0.637 | 0 | 0 | 0 | 1 | -1.708 | 0.111 | 0 | |
165 | 9 | 10 | 165 | 0 | 0 | -0.186 | 0 | 0 | 0 | 0 | -1.687 | 0 | 0 | |
166 | 9 | 11 | 166 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -1.75 | 0.003 | 0 | |
167 | 9 | 12 | 167 | 1 | 0 | -0.471 | 0 | 0 | 0 | 0 | -1.867 | 0 | 0 | |
168 | 9 | 13 | 168 | 0 | 3 | -0.019 | 0 | 0 | 0 | 0 | -1.734 | 0 | 0 | |
169 | 9 | 14 | 169 | 1 | 1 | -0.011 | 0 | 0 | 0 | 1 | -1.991 | 0.006 | 0 | |
170 | 9 | 15 | 170 | 9 | 0 | -0.014 | 0 | 0 | 0 | 0 | -2.144 | 0.011 | 0 | |
171 | 9 | 16 | 171 | 10 | 3 | -0.019 | 0 | 0 | 0 | 0 | -2.143 | 0.006 | 0 | |
172 | 9 | 17 | 172 | 11 | 8 | -0.019 | 0 | 1 | 0 | 0 | -1.644 | 0.091 | 0 | |
173 | 9 | 18 | 173 | 4 | 2 | -0.019 | 0 | 0 | 0 | 0 | -2.191 | 0.023 | 0 | |
174 | 9 | 19 | 174 | 9 | 3 | 0 | 0 | 1 | 0 | 0 | -2.729 | 0.066 | 0 | |
175 | 9 | 20 | 175 | 3 | 23 | -0.028 | 0 | 0 | 1 | 0 | -2.607 | 0.112 | 0 | |
176 | 9 | 21 | 176 | 3 | 15 | -0.031 | 0 | 0 | 0 | 1 | -2.309 | 0.013 | 0 | |
177 | 9 | 22 | 177 | 1 | 3 | 0 | 0 | 1 | 0 | 0 | -2.129 | 0.009 | 0 | |
178 | 9 | 23 | 178 | 1 | 2 | -0.092 | 0 | 0 | 0 | 1 | -1.625 | 0.021 | 0 | |
179 | 9 | 24 | 179 | 75 | 26 | 0 | 1 | 0 | 0 | 0 | -3.44 | 0.477 | 0 | |
180 | 9 | 25 | 180 | 0 | 0 | -0.637 | 0 | 0 | 0 | 1 | -1.646 | 0.006 | 0 | |
181 | 10 | 11 | 181 | 16 | 1 | -0.186 | 0 | 0 | 1 | 0 | -2.859 | 0.028 | 0 | |
182 | 10 | 12 | 182 | 15 | 0 | -0.627 | 0 | 0 | 0 | 1 | -3.078 | 0 | 0 | |
183 | 10 | 13 | 183 | 11 | 10 | -0.21 | 0 | 0 | 1 | 0 | -3.522 | 0.021 | 0 | |
184 | 10 | 14 | 184 | 13 | 3 | -0.21 | 0 | 0 | 1 | 0 | -2.113 | 0.023 | 0 | |
185 | 10 | 15 | 185 | 16 | 1 | -0.186 | 0 | 0 | 0 | 0 | -2.205 | 0 | 0 | |
186 | 10 | 16 | 186 | 16 | 2 | -0.186 | 0 | 0 | 0 | 0 | -2.648 | 0 | 0 | |
187 | 10 | 17 | 187 | 9 | 3 | -0.21 | 0 | 0 | 0 | 0 | -1.537 | 0 | 0 | |
188 | 10 | 18 | 188 | 2 | 0 | -0.21 | 0 | 0 | 0 | 0 | -1.639 | 0 | 0 | |
189 | 10 | 19 | 189 | 4 | 0 | -0.186 | 0 | 0 | 0 | 0 | -1.63 | 0 | 0 | |
190 | 10 | 20 | 190 | 3 | 0 | -0.186 | 0 | 0 | 0 | 1 | -1.807 | 0.011 | 0 | |
191 | 10 | 21 | 191 | 6 | 5 | -0.223 | 0 | 0 | 0 | 0 | -2.012 | 0 | 0 | |
192 | 10 | 22 | 192 | 6 | 1 | -0.186 | 0 | 0 | 0 | 0 | -1.858 | 0 | 0 | |
193 | 10 | 23 | 193 | 4 | 3 | -0.254 | 0 | 0 | 1 | 0 | -1.992 | 0 | 0 | |
194 | 10 | 24 | 194 | 2 | 0 | -0.186 | 0 | 0 | 0 | 0 | -1.784 | 0.029 | 0 | |
195 | 10 | 25 | 195 | 48 | 7 | -0.863 | 0 | 0 | 0 | 1 | -3.859 | 0.271 | 0 | |
196 | 11 | 12 | 196 | 22 | 36 | -0.471 | 1 | 0 | 0 | 0 | -3.638 | 0.454 | 0 | |
197 | 11 | 13 | 197 | 6 | 92 | -0.019 | 1 | 0 | 0 | 0 | -3.583 | 0.18 | 0 | |
198 | 11 | 14 | 198 | 2 | 10 | -0.011 | 0 | 1 | 0 | 0 | -2.642 | 0.02 | 0 | |
199 | 11 | 15 | 199 | 1 | 1 | -0.014 | 0 | 0 | 0 | 0 | -2.667 | 0.015 | 0 | |
200 | 11 | 16 | 200 | 1 | 3 | -0.019 | 0 | 0 | 0 | 0 | -2.461 | 0 | 0 | |
201 | 11 | 17 | 201 | 0 | 2 | -0.019 | 0 | 0 | 0 | 1 | -1.835 | 0 | 0 | |
202 | 11 | 18 | 202 | 0 | 0 | -0.019 | 0 | 0 | 0 | 0 | -1.889 | 0 | 0 | |
203 | 11 | 19 | 203 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | -1.801 | 0 | 0 | |
204 | 11 | 20 | 204 | 0 | 2 | -0.028 | 0 | 0 | 1 | 0 | -2.031 | 0.052 | 0 | |
205 | 11 | 21 | 205 | 0 | 6 | -0.031 | 0 | 0 | 0 | 1 | -2.348 | 0.008 | 0 | |
206 | 11 | 22 | 206 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -2.206 | 0.027 | 0 | |
207 | 11 | 23 | 207 | 0 | 13 | -0.092 | 0 | 1 | 0 | 0 | -2.533 | 0.028 | 0 | |
208 | 11 | 24 | 208 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | -1.906 | 0 | 0 | |
209 | 11 | 25 | 209 | 1 | 0 | -0.637 | 0 | 0 | 1 | 0 | -2.546 | 0.016 | 0 | |
210 | 12 | 13 | 210 | 6 | 48 | -0.502 | 0 | 0 | 1 | 0 | -3.758 | 0.209 | 0 | |
211 | 12 | 14 | 211 | 3 | 0 | -0.488 | 0 | 0 | 1 | 0 | -2.588 | 0.039 | 0 | |
212 | 12 | 15 | 212 | 1 | 0 | -0.471 | 0 | 0 | 0 | 0 | -2.739 | 0.006 | 0 | |
213 | 12 | 16 | 213 | 0 | 2 | -0.502 | 0 | 0 | 0 | 0 | -2.828 | 0.007 | 0 | |
214 | 12 | 17 | 214 | 0 | 0 | -0.471 | 0 | 0 | 0 | 0 | -1.767 | 0 | 0 | |
215 | 12 | 18 | 215 | 0 | 0 | -0.502 | 0 | 0 | 0 | 0 | -1.905 | 0 | 0 | |
216 | 12 | 19 | 216 | 0 | 1 | -0.471 | 0 | 0 | 0 | 0 | -1.866 | 0 | 0 | |
217 | 12 | 20 | 217 | 0 | 1 | -0.471 | 0 | 0 | 0 | 1 | -2.106 | 0.019 | 0 | |
218 | 12 | 21 | 218 | 1 | 6 | -0.52 | 0 | 0 | 0 | 0 | -2.419 | 0.005 | 0 | |
219 | 12 | 22 | 219 | 0 | 0 | -0.471 | 0 | 0 | 0 | 0 | -2.207 | 0.015 | 0 | |
220 | 12 | 23 | 220 | 0 | 5 | -0.553 | 0 | 0 | 1 | 0 | -2.305 | 0.006 | 0 | |
221 | 12 | 24 | 221 | 0 | 1 | -0.471 | 0 | 0 | 0 | 0 | -2.023 | 0 | 0 | |
222 | 12 | 25 | 222 | 1 | 0 | -1.236 | 0 | 0 | 0 | 1 | -2.729 | 0 | 0 | |
223 | 13 | 14 | 223 | 51 | 27 | -0.019 | 0 | 1 | 0 | 0 | -2.364 | 0.228 | 0 | |
224 | 13 | 15 | 224 | 5 | 1 | -0.033 | 0 | 0 | 0 | 0 | -2.447 | 0 | 0 | |
225 | 13 | 16 | 225 | 8 | 1 | -0.039 | 0 | 0 | 0 | 0 | -2.62 | 0 | 0 | |
226 | 13 | 17 | 226 | 0 | 0 | -0.039 | 0 | 0 | 0 | 0 | -1.68 | 0 | 0 | |
227 | 13 | 18 | 227 | 0 | 0 | -0.039 | 0 | 0 | 0 | 0 | -1.768 | 0 | 0 | |
228 | 13 | 19 | 228 | 2 | 0 | -0.019 | 0 | 0 | 0 | 0 | -1.726 | 0.173 | 0 | |
229 | 13 | 20 | 229 | 10 | 10 | -0.048 | 0 | 0 | 0 | 1 | -1.931 | 0.036 | 0 | |
230 | 13 | 21 | 230 | 2 | 0 | -0.051 | 0 | 0 | 0 | 0 | -2.193 | 0.034 | 0 | |
231 | 13 | 22 | 231 | 0 | 0 | -0.019 | 0 | 0 | 0 | 0 | -2.033 | 0 | 0 | |
232 | 13 | 23 | 232 | 2 | 2 | -0.113 | 0 | 0 | 1 | 0 | -2.234 | 0 | 0 | |
233 | 13 | 24 | 233 | 1 | 2 | -0.019 | 0 | 0 | 0 | 0 | -1.863 | 0 | 0 | |
234 | 13 | 25 | 234 | 11 | 0 | -0.637 | 0 | 0 | 0 | 1 | -2.984 | 0 | 0 | |
235 | 14 | 15 | 235 | 6 | 2 | -0.025 | 0 | 0 | 0 | 0 | -3.723 | 0.015 | 0 | |
236 | 14 | 16 | 236 | 0 | 2 | -0.031 | 0 | 0 | 0 | 0 | -2.243 | 0.012 | 0 | |
237 | 14 | 17 | 237 | 5 | 7 | -0.031 | 0 | 0 | 0 | 1 | -2.323 | 0.036 | 0 | |
238 | 14 | 18 | 238 | 0 | 2 | -0.031 | 0 | 0 | 0 | 0 | -2.508 | 0 | 0 | |
239 | 14 | 19 | 239 | 2 | 3 | -0.011 | 0 | 0 | 0 | 1 | -2.246 | 0 | 0 | |
240 | 14 | 20 | 240 | 2 | 12 | -0.039 | 0 | 0 | 1 | 0 | -2.616 | 0.058 | 0 | |
241 | 14 | 21 | 241 | 2 | 13 | -0.042 | 0 | 0 | 0 | 1 | -3.272 | 0.004 | 0 | |
242 | 14 | 22 | 242 | 0 | 1 | -0.011 | 0 | 0 | 0 | 1 | -3.224 | 0.008 | 0 | |
243 | 14 | 23 | 243 | 1 | 10 | -0.104 | 0 | 1 | 0 | 0 | -2.808 | 0.067 | 0 | |
244 | 14 | 24 | 244 | 1 | 1 | -0.011 | 0 | 0 | 0 | 0 | -2.249 | 0 | 0 | |
245 | 14 | 25 | 245 | 0 | 0 | -0.637 | 0 | 0 | 1 | 0 | -1.963 | 0.033 | 0 | |
246 | 15 | 16 | 246 | 11 | 35 | -0.033 | 1 | 0 | 0 | 0 | -2.472 | 0.464 | 0 | |
247 | 15 | 17 | 247 | 0 | 7 | -0.033 | 0 | 0 | 0 | 0 | -2.135 | 0.059 | 0 | |
248 | 15 | 18 | 248 | 0 | 2 | -0.033 | 0 | 0 | 0 | 1 | -2.475 | 0.01 | 0 | |
249 | 15 | 19 | 249 | 1 | 1 | -0.014 | 0 | 0 | 0 | 1 | -2.339 | 0 | 0 | |
250 | 15 | 20 | 250 | 1 | 13 | -0.031 | 0 | 1 | 0 | 0 | -2.775 | 0.048 | 0 | |
251 | 15 | 21 | 251 | 2 | 39 | -0.045 | 0 | 0 | 0 | 1 | -3.647 | 0.052 | 0 | |
252 | 15 | 22 | 252 | 2 | 8 | -0.014 | 0 | 0 | 0 | 0 | -3.073 | 0.059 | 0 | |
253 | 15 | 23 | 253 | 0 | 3 | -0.107 | 0 | 0 | 0 | 0 | -2.5 | 0.01 | 0 | |
254 | 15 | 24 | 254 | 0 | 3 | -0.014 | 0 | 0 | 0 | 0 | -2.437 | 0.013 | 0 | |
255 | 15 | 25 | 255 | 0 | 2 | -0.637 | 0 | 0 | 0 | 0 | -2.055 | 0.044 | 0 | |
256 | 16 | 17 | 256 | 2 | 3 | -0.039 | 0 | 0 | 0 | 0 | -1.596 | 0.074 | 0 | |
257 | 16 | 18 | 257 | 1 | 3 | -0.039 | 0 | 0 | 1 | 0 | -1.863 | 0 | 0 | |
258 | 16 | 19 | 258 | 3 | 7 | -0.019 | 0 | 0 | 1 | 0 | -1.955 | 0 | 0 | |
259 | 16 | 20 | 259 | 2 | 8 | -0.048 | 0 | 0 | 1 | 0 | -2.16 | 0.055 | 0 | |
260 | 16 | 21 | 260 | 2 | 39 | -0.051 | 0 | 0 | 1 | 0 | -2.334 | 0.049 | 0 | |
261 | 16 | 22 | 261 | 0 | 7 | -0.019 | 0 | 0 | 0 | 0 | -2.067 | 0.055 | 0 | |
262 | 16 | 23 | 262 | 1 | 9 | -0.113 | 0 | 0 | 0 | 0 | -1.895 | 0.044 | 0 | |
263 | 16 | 24 | 263 | 0 | 2 | -0.019 | 0 | 0 | 0 | 0 | -2.248 | 0 | 0 | |
264 | 16 | 25 | 264 | 2 | 1 | -0.637 | 0 | 0 | 0 | 1 | -2.582 | 0 | 0 | |
265 | 17 | 18 | 265 | 4 | 2 | -0.039 | 0 | 0 | 0 | 0 | -2.477 | 0.056 | 0 | |
266 | 17 | 19 | 266 | 8 | 10 | -0.019 | 0 | 1 | 0 | 0 | -2.01 | 0.016 | 0 | |
267 | 17 | 20 | 267 | 4 | 16 | -0.048 | 0 | 0 | 0 | 0 | -2.117 | 0.018 | 0 | |
268 | 17 | 21 | 268 | 3 | 9 | -0.051 | 0 | 0 | 0 | 1 | -2.196 | 0.007 | 0 | |
269 | 17 | 22 | 269 | 8 | 19 | -0.019 | 0 | 1 | 0 | 0 | -2.511 | 0.145 | 0 | |
270 | 17 | 23 | 270 | 1 | 3 | -0.113 | 0 | 0 | 0 | 1 | -2.376 | 0 | 0 | |
271 | 17 | 24 | 271 | 5 | 5 | -0.019 | 0 | 0 | 1 | 0 | -1.816 | 0.082 | 0 | |
272 | 17 | 25 | 272 | 3 | 0 | -0.674 | 0 | 0 | 0 | 1 | -1.446 | 0 | 0 | |
273 | 18 | 19 | 273 | 3 | 1 | -0.019 | 0 | 0 | 0 | 1 | -2.996 | 0.035 | 0 | |
274 | 18 | 20 | 274 | 2 | 1 | -0.048 | 0 | 0 | 0 | 0 | -3.101 | 0 | 0 | |
275 | 18 | 21 | 275 | 15 | 62 | -0.042 | 1 | 0 | 0 | 0 | -2.797 | 0.447 | 0 | |
276 | 18 | 22 | 276 | 1 | 1 | -0.019 | 0 | 0 | 1 | 0 | -3.18 | 0.065 | 0 | |
277 | 18 | 23 | 277 | 0 | 1 | -0.113 | 0 | 0 | 0 | 0 | -2.085 | 0 | 0 | |
278 | 18 | 24 | 278 | 4 | 3 | -0.019 | 0 | 0 | 0 | 0 | -2.482 | 0.053 | 0 | |
279 | 18 | 25 | 279 | 1 | 0 | -0.674 | 0 | 0 | 0 | 1 | -1.555 | 0.045 | 0 | |
280 | 19 | 20 | 280 | 1 | 15 | -0.028 | 0 | 0 | 0 | 0 | -3.38 | 0.082 | 0 | |
281 | 19 | 21 | 281 | 5 | 17 | -0.031 | 0 | 0 | 0 | 1 | -2.649 | 0.14 | 0 | |
282 | 19 | 22 | 282 | 10 | 15 | 0 | 0 | 1 | 0 | 0 | -2.615 | 0.109 | 0 | |
283 | 19 | 23 | 283 | 0 | 3 | -0.092 | 0 | 0 | 0 | 1 | -1.829 | 0.131 | 0 | |
284 | 19 | 24 | 284 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | -3.127 | 0 | 0 | |
285 | 19 | 25 | 285 | 3 | 2 | -0.637 | 0 | 0 | 0 | 1 | -1.563 | 0 | 0 | |
286 | 20 | 21 | 286 | 9 | 12 | -0.059 | 0 | 0 | 0 | 0 | -3.302 | 0.015 | 0 | |
287 | 20 | 22 | 287 | 3 | 2 | -0.028 | 0 | 0 | 0 | 0 | -3.079 | 0.053 | 0 | |
288 | 20 | 23 | 288 | 3 | 8 | -0.122 | 0 | 1 | 0 | 0 | -2.038 | 0.119 | 0 | |
289 | 20 | 24 | 289 | 13 | 3 | -0.028 | 0 | 0 | 0 | 1 | -3.161 | 0.074 | 0 | |
290 | 20 | 25 | 290 | 0 | 0 | -0.637 | 0 | 0 | 0 | 1 | -1.721 | 0.032 | 0 | |
291 | 21 | 22 | 291 | 18 | 5 | -0.031 | 0 | 0 | 0 | 1 | -3.486 | 0.019 | 0 | |
292 | 21 | 23 | 292 | 9 | 1 | -0.125 | 0 | 0 | 0 | 1 | -2.326 | 0.015 | 0 | |
293 | 21 | 24 | 293 | 0 | 0 | -0.031 | 0 | 0 | 0 | 0 | -2.69 | 0.01 | 0 | |
294 | 21 | 25 | 294 | 9 | 1 | -0.696 | 0 | 0 | 1 | 0 | -1.895 | 0.043 | 0 | |
295 | 22 | 23 | 295 | 1 | 1 | -0.092 | 0 | 0 | 0 | 1 | -2.434 | 0.076 | 0 | |
296 | 22 | 24 | 296 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | -2.442 | 0 | 0 | |
297 | 22 | 25 | 297 | 2 | 0 | -0.637 | 0 | 0 | 0 | 1 | -1.747 | 0.026 | 0 | |
298 | 23 | 24 | 298 | 2 | 0 | -0.092 | 0 | 0 | 0 | 0 | -1.798 | 0 | 0 | |
299 | 23 | 25 | 299 | 4 | 0 | -0.818 | 0 | 0 | 1 | 0 | -1.848 | 0.031 | 0 | |
300 | 24 | 25 | 300 | 2 | 0 | -0.637 | 0 | 0 | 0 | 0 | -1.723 | 0.042 | 0 |
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