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Commit a3659bd

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Merge pull request #547 from jessica-writes-code/jmoore/gws-paper
Update link to Greenhill, Ward, Sacks paper
2 parents efc9398 + b1f4e6b commit a3659bd

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‎Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb

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"\n",
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"We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [7] than p-value tests. We agree.\n",
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"\n",
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"The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf), but I'll summarize their use here.\n",
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"The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n",
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"\n",
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"For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:"
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‎Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb

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"\n",
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"We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [7] than p-value tests. We agree.\n",
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"\n",
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"The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf), but I'll summarize their use here.\n",
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"The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[8]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n",
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"\n",
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"For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:"
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‎Chapter2_MorePyMC/Ch2_MorePyMC_TFP.ipynb

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"\n",
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"We will be doing this graphically as well, which may seem like an even less objective method. The alternative is to use *Bayesian p-values*. These are still subjective, as the proper cutoff between good and bad is arbitrary. Gelman emphasises that the graphical tests are more illuminating [3] than p-value tests. We agree.\n",
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"\n",
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"The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[4]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf), but I'll summarize their use here.\n",
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"The following graphical test is a novel data-viz approach to logistic regression. The plots are called *separation plots*[4]. For a suite of models we wish to compare, each model is plotted on an individual separation plot. I leave most of the technical details about separation plots to the very accessible [original paper](https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x), but I'll summarize their use here.\n",
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"\n",
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"For each model, we calculate the proportion of times the posterior simulation proposed a value of 1 for a particular temperature, i.e. compute $P( \\;\\text{Defect} = 1 | t, \\alpha, \\beta )$ by averaging. This gives us the posterior probability of a defect at each data point in our dataset. For example, for the model we used above:"
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"def separation_plot( p, y, **kwargs ):\n",
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" \"\"\"\n",
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" This function creates a separation plot for logistic and probit classification. \n",
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" See http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf\n",
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" See https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x\n",
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" \n",
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" p: The proportions/probabilities, can be a nxM matrix which represents M models.\n",
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" y: the 0-1 response variables.\n",

‎Chapter2_MorePyMC/separation_plot.py

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# separation plot
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# Author: Cameron Davidson-Pilon,2013
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# see http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf
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# see https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x
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def separation_plot( p, y, **kwargs ):
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"""
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This function creates a separation plot for logistic and probit classification.
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See http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf
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See https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5907.2011.00525.x
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p: The proportions/probabilities, can be a nxM matrix which represents M models.
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y: the 0-1 response variables.

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