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The default value of " }, { "__type": "ParamRef", "__tag": 4071, "name": "ddof" }, { "__type": "Text", "__tag": 4046, "value": " is 0." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "axis", "annotation": "int or None, default: 0", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If " }, { "__type": "InlineCode", "__tag": 4051, "value": "None" }, { "__type": "Text", "__tag": 4046, "value": ", the input will be raveled before computing the statistic." } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "lambda_", "annotation": "float or str, optional", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "The power in the Cressie-Read power divergence statistic. 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If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN." } ] } ] }, { "__type": "ListItem", "__tag": 4054, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "InlineCode", "__tag": 4051, "value": "raise" }, { "__type": "Text", "__tag": 4046, "value": ": if a NaN is present, a " }, { "__type": "InlineCode", "__tag": 4051, "value": "ValueError" }, { "__type": "Text", "__tag": 4046, "value": " will be raised." } ] } ] } ] } ] }, { "__type": "DocParam", "__tag": 4016, "name": "keepdims", "annotation": "bool, default: False", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This function tests the null hypothesis that the categorical data has the given frequencies, using the Cressie-Read power divergence statistic." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/stats/_stats_py.py", "item_line": 6977, "item_type": "function", "aliases": [ "scipy.stats.power_divergence" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "(See `chisquare` for more examples.)\n\nWhen just `f_obs` is given, it is assumed that the expected frequencies\nare uniform and given by the mean of the observed frequencies. Here we\nperform a G-test (i.e. use the log-likelihood ratio statistic):\n\n" }, { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nfrom scipy.stats import power_divergence\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence([16, 18, 16, 14, 12, 12], lambda_='log-likelihood')\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nThe expected frequencies can be given with the `f_exp` argument:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence([16, 18, 16, 14, 12, 12],\n f_exp=[16, 16, 16, 16, 16, 8],\n lambda_='log-likelihood')\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nWhen `f_obs` is 2-D, by default the test is applied to each column.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T\nobs.shape\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence(obs, lambda_=\"log-likelihood\")\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nBy setting ``axis=None``, the test is applied to all data in the array,\nwhich is equivalent to applying the test to the flattened array.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence(obs, axis=None)\npower_divergence(obs.ravel())\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\n`ddof` is the change to make to the default degrees of freedom.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence([16, 18, 16, 14, 12, 12], ddof=1)\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nThe calculation of the p-values is done by broadcasting the\ntest statistic with `ddof`.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence([16, 18, 16, 14, 12, 12], ddof=[0,1,2])\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\n`f_obs` and `f_exp` are also broadcast. In the following, `f_obs` has\nshape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting\n`f_obs` and `f_exp` has shape (2, 6). To compute the desired chi-squared\nstatistics, we must use ``axis=1``:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "power_divergence([16, 18, 16, 14, 12, 12],\n f_exp=[[16, 16, 16, 16, 16, 8],\n [8, 20, 20, 16, 12, 12]],\n axis=1)\n", "execution_status": "failure" } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "chisquare", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._stats_py:chisquare" }, "kind": "module" }, "descriptions": [], "type": "func" } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "f_obs", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "f_exp", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "ddof", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "0" }, { "__type": "SigParam", "__tag": 4030, "name": "axis", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "0" }, { "__type": "SigParam", "__tag": 4030, "name": "lambda_", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "nan_policy", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "propagate" }, { "__type": "SigParam", "__tag": 4030, "name": "keepdims", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "False" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "power_divergence" }, "references": [ ".. [1] Lowry, Richard. \"Concepts and Applications of Inferential", " Statistics\". Chapter 8.", " https://web.archive.org/web/20171015035606/http://faculty.vassar.edu/lowry/ch8pt1.html", ".. [2] \"Chi-squared test\", https://en.wikipedia.org/wiki/Chi-squared_test", ".. [3] \"G-test\", https://en.wikipedia.org/wiki/G-test", ".. [4] Sokal, R. R. and Rohlf, F. J. \"Biometry: the principles and", " practice of statistics in biological research\", New York: Freeman", " (1981)", ".. [5] Cressie, N. and Read, T. R. C., \"Multinomial Goodness-of-Fit", " Tests\", J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984),", " pp. 440-464." ], "qa": "scipy.stats._stats_py:power_divergence", "arbitrary": [], "local_refs": [ "axis", "ddof", "f_exp", "f_obs", "keepdims", "lambda_", "nan_policy" ] }