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Python to generate nice looking SVG graph
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73 lines
2.1 KiB
73 lines
2.1 KiB
from math import log, sqrt, pi |
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def erfinv(x, a=.147): |
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"""Approximation of the inverse error function |
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https://en.wikipedia.org/wiki/Error_function |
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#Approximation_with_elementary_functions |
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""" |
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lnx = log(1 - x * x) |
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part1 = (2 / (a * pi) + lnx / 2) |
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part2 = lnx / a |
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sgn = 1 if x > 0 else -1 |
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return sgn * sqrt(sqrt(part1 * part1 - part2) - part1) |
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def norm_ppf(x): |
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if not 0 < x < 1: |
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raise ValueError("Can't compute the percentage point for value %d" % x) |
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return sqrt(2) * erfinv(2 * x - 1) |
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def ppf(x, n): |
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try: |
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from scipy import stats |
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except ImportError: |
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stats = None |
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if stats: |
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if n < 30: |
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return stats.t.ppf(x, n) |
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return stats.norm.ppf(x) |
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else: |
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if n < 30: |
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# TODO: implement power series: |
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# http://eprints.maths.ox.ac.uk/184/1/tdist.pdf |
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raise ImportError( |
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'You must have scipy installed to use t-student ' |
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'when sample_size is below 30') |
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return norm_ppf(x) |
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# According to http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/ |
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# BS704_Confidence_Intervals/BS704_Confidence_Intervals_print.html |
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def confidence_interval_continuous( |
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point_estimate, stddev, sample_size, confidence=.95, **kwargs): |
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"""Continuous confidence interval from sample size and standard error""" |
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alpha = ppf((confidence + 1) / 2, sample_size - 1) |
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margin = stddev / sqrt(sample_size) |
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return (point_estimate - alpha * margin, point_estimate + alpha * margin) |
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def confidence_interval_dichotomous( |
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point_estimate, sample_size, confidence=.95, bias=False, |
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percentage=True, **kwargs): |
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"""Dichotomous confidence interval from sample size and maybe a bias""" |
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alpha = ppf((confidence + 1) / 2, sample_size - 1) |
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p = point_estimate |
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if percentage: |
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p /= 100 |
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margin = sqrt(p * (1 - p) / sample_size) |
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if bias: |
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margin += .5 / sample_size |
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if percentage: |
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margin *= 100 |
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return (point_estimate - alpha * margin, point_estimate + alpha * margin) |
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def confidence_interval_manual(point_estimate, low, high): |
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return (low, high)
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