From 9c03e4d65c419d81cbac1dec49539b2dfdf21ff6 Mon Sep 17 00:00:00 2001 From: Jeremy Teitelbaum Date: Sun, 29 Apr 2018 13:00:00 -0400 Subject: [PATCH] reconciling home and web --- BDA 5.9.3.ipynb | 96 ++++++++++++++++++++++++------------------------- 1 file changed, 48 insertions(+), 48 deletions(-) diff --git a/BDA 5.9.3.ipynb b/BDA 5.9.3.ipynb index abfbe93..850878d 100644 --- a/BDA 5.9.3.ipynb +++ b/BDA 5.9.3.ipynb @@ -128,16 +128,16 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": 355, "metadata": {}, "outputs": [], "source": [ - "answers=sm.sampling(data=dict({'means':p55['effect'],'se':p55['se']}),iter=00)" + "answers=sm.sampling(data=dict({'means':p55['effect'],'se':p55['se']}),iter=5000)" ] }, { "cell_type": "code", - "execution_count": 351, + "execution_count": 356, "metadata": {}, "outputs": [ { @@ -145,31 +145,31 @@ "output_type": "stream", "text": [ "Inference for Stan model: anon_model_1c0a010b4129370aa04f0b4b9f729b4d.\n", - "4 chains, each with iter=500; warmup=250; thin=1; \n", - "post-warmup draws per chain=250, total post-warmup draws=1000.\n", + "4 chains, each with iter=5000; warmup=2500; thin=1; \n", + "post-warmup draws per chain=2500, total post-warmup draws=10000.\n", "\n", " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", - "theta[0] 12.07 0.66 9.38 -3.89 5.93 11.21 17.23 34.12 200 1.01\n", - "theta[1] 7.54 0.39 6.89 -6.07 3.02 7.48 12.15 21.17 312 1.01\n", - "theta[2] 5.45 0.46 8.58 -12.34 0.2 6.07 10.92 20.96 342 1.0\n", - "theta[3] 7.16 0.37 7.21 -7.68 2.72 7.31 11.83 20.44 383 1.01\n", - "theta[4] 4.51 0.43 6.58 -10.0 0.23 5.07 9.18 15.99 235 1.02\n", - "theta[5] 5.53 0.41 7.27 -10.59 1.01 6.16 10.74 18.24 313 1.01\n", - "theta[6] 11.03 0.53 7.5 -2.83 5.64 11.03 15.77 27.15 199 1.01\n", - "theta[7] 8.3 0.39 7.93 -6.88 3.08 8.28 13.26 24.5 414 1.0\n", - "mu 7.66 0.41 5.62 -3.36 4.06 7.92 11.47 18.73 192 1.02\n", - "tau 7.74 0.71 6.11 0.4 3.91 6.29 9.9 22.49 75 1.05\n", - "results[0] 11.81 0.57 12.82 -9.18 3.71 10.54 17.23 43.38 513 1.01\n", - "results[1] 7.26 0.52 12.33 -16.66 0.94 7.04 13.57 33.52 563 1.01\n", - "results[2] 5.43 0.42 12.64 -22.88 -1.07 5.99 12.55 29.09 902 1.0\n", - "results[3] 7.01 0.41 11.92 -16.05 1.03 7.18 13.39 29.57 861 1.0\n", - "results[4] 4.62 0.46 11.55 -23.93 -1.48 5.45 11.65 26.17 638 1.0\n", - "results[5] 5.83 0.44 11.71 -20.83 0.05 6.61 11.82 28.91 724 1.0\n", - "results[6] 10.76 0.56 11.87 -11.69 3.67 10.23 17.43 36.29 443 1.0\n", - "results[7] 8.01 0.54 12.75 -16.27 1.62 7.82 14.62 31.63 561 1.0\n", - "lp__ -18.65 1.21 5.8 -27.82 -22.04 -19.13 -16.31 0.06 23 1.18\n", + "theta[0] 11.66 0.17 8.25 -2.16 6.27 10.51 16.07 30.71 2223 1.0\n", + "theta[1] 8.02 0.1 6.27 -4.44 4.18 7.82 11.76 21.02 3674 1.0\n", + "theta[2] 6.36 0.12 7.68 -10.59 2.17 6.78 11.15 20.84 3806 1.0\n", + "theta[3] 7.79 0.11 6.43 -5.29 4.02 7.62 11.79 20.79 3689 1.0\n", + "theta[4] 5.19 0.13 6.38 -8.66 1.46 5.72 9.44 16.84 2357 1.0\n", + "theta[5] 6.3 0.12 6.62 -7.68 2.38 6.54 10.57 18.98 3277 1.0\n", + "theta[6] 10.99 0.15 6.78 -0.97 6.41 10.4 14.99 25.86 2181 1.0\n", + "theta[7] 8.61 0.13 7.97 -7.29 4.13 8.21 12.91 25.66 3946 1.0\n", + "mu 8.2 0.11 5.3 -1.85 5.01 7.96 11.32 18.88 2354 1.0\n", + "tau 6.84 0.2 5.58 0.93 2.99 5.46 9.11 20.69 751 1.01\n", + "results[0] 11.6 0.18 12.19 -8.79 4.97 9.96 16.83 40.26 4692 1.0\n", + "results[1] 7.97 0.13 10.69 -14.26 2.73 7.74 13.36 29.84 6547 1.0\n", + "results[2] 6.4 0.15 11.84 -18.96 1.18 6.95 12.45 28.63 5970 1.0\n", + "results[3] 7.79 0.14 11.1 -14.73 2.66 7.66 13.29 30.0 6438 1.0\n", + "results[4] 5.24 0.16 11.09 -19.44 0.35 6.13 11.03 24.67 4830 1.0\n", + "results[5] 6.36 0.15 10.98 -17.1 1.36 6.66 11.93 28.06 5695 1.0\n", + "results[6] 10.93 0.16 11.19 -8.59 4.94 9.77 16.05 35.59 4800 1.0\n", + "results[7] 8.64 0.14 11.63 -13.75 2.97 8.25 14.03 33.19 6700 1.0\n", + "lp__ -17.68 0.41 5.52 -27.62 -21.48 -18.16 -14.18 -5.46 179 1.04\n", "\n", - "Samples were drawn using NUTS at Sat Apr 21 16:45:21 2018.\n", + "Samples were drawn using NUTS at Sat Apr 21 16:45:42 2018.\n", "For each parameter, n_eff is a crude measure of effective sample size,\n", "and Rhat is the potential scale reduction factor on split chains (at \n", "convergence, Rhat=1).\n" @@ -182,12 +182,12 @@ }, { "cell_type": "code", - "execution_count": 352, + "execution_count": 357, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 353, + "execution_count": 358, "metadata": {}, "outputs": [], "source": [ @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 354, + "execution_count": 359, "metadata": {}, "outputs": [ { @@ -227,14 +227,14 @@ "Chance is empirical probability that given school is best\n", " effect se Chance\n", "school \n", - "A 28 15 0.191\n", - "B 8 10 0.109\n", - "C -3 16 0.105\n", - "D 7 11 0.124\n", - "E -1 9 0.077\n", - "F 1 11 0.088\n", - "G 18 10 0.186\n", - "H 12 18 0.120\n", + "A 28 15 0.2047\n", + "B 8 10 0.1160\n", + "C -3 16 0.1010\n", + "D 7 11 0.1075\n", + "E -1 9 0.0766\n", + "F 1 11 0.0854\n", + "G 18 10 0.1761\n", + "H 12 18 0.1327\n", "Only thing that worries me is that Gelman has A best with prob=10%\n" ] } @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 360, "metadata": {}, "outputs": [ { @@ -259,14 +259,14 @@ " as good as or better than corresponding column\n", " \n", " A B C D E F G H\n", - "A 1.00 0.61 0.64 0.60 0.68 0.66 0.53 0.58 \n", - "B 0.39 1.00 0.53 0.50 0.59 0.56 0.41 0.47 \n", - "C 0.36 0.47 1.00 0.47 0.55 0.52 0.37 0.44 \n", - "D 0.40 0.50 0.53 1.00 0.58 0.56 0.42 0.47 \n", - "E 0.32 0.41 0.45 0.42 1.00 0.47 0.33 0.38 \n", - "F 0.34 0.44 0.48 0.44 0.53 1.00 0.35 0.42 \n", - "G 0.47 0.59 0.63 0.58 0.67 0.65 1.00 0.57 \n", - "H 0.42 0.53 0.56 0.53 0.62 0.58 0.43 1.00 \n" + "A 1.00 0.59 0.62 0.60 0.66 0.64 0.52 0.58 \n", + "B 0.41 1.00 0.54 0.51 0.57 0.56 0.42 0.49 \n", + "C 0.38 0.46 1.00 0.47 0.54 0.51 0.39 0.45 \n", + "D 0.40 0.49 0.53 1.00 0.56 0.54 0.41 0.48 \n", + "E 0.34 0.43 0.46 0.44 1.00 0.48 0.35 0.41 \n", + "F 0.36 0.44 0.49 0.46 0.52 1.00 0.38 0.43 \n", + "G 0.48 0.58 0.61 0.59 0.65 0.62 1.00 0.57 \n", + "H 0.42 0.51 0.55 0.52 0.59 0.57 0.43 1.00 \n" ] } ], @@ -336,7 +336,7 @@ " }\n", "}\n", "'''\n", - "sm=pystan.StanModel(model_code=stan_code_2)" + "sm2=pystan.StanModel(model_code=stan_code_2)" ] }, { @@ -345,7 +345,7 @@ "metadata": {}, "outputs": [], "source": [ - "answers=sm.sampling(data=dict({'means':p55['effect'],'se':p55['se']}),iter=5000)\n" + "answers=sm2.sampling(data=dict({'means':p55['effect'],'se':p55['se']}),iter=5000)\n" ] }, {