{ "__type": "IngestedDoc", "__tag": 4010, "_content": { "Notes": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Strong", "__tag": 4048, "children": [ { "__type": "Text", "__tag": 4046, "value": "Array API Standard Support" } ] } ] }, { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "CrossRef", "__tag": 4002, "value": "obrientransform", "reference": { "__type": "RefInfo", "__tag": 4000, "module": null, "version": null, "kind": "local", "path": "obrientransform" }, "kind": "local" }, { "__type": "Text", "__tag": 4046, "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable " }, { "__type": "InlineCode", "__tag": 4051, "value": "SCIPY_ARRAY_API=1" }, { "__type": "Text", "__tag": 4046, "value": " and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported." } ] }, { "__type": "Code", "__tag": 4050, "value": "==================== ==================== ====================\nLibrary CPU GPU\n==================== ==================== ====================\nNumPy ✅ n/a \nCuPy n/a ⛔ \nPyTorch ⛔ ⛔ \nJAX ⛔ ⛔ \nDask ⛔ n/a \n==================== ==================== ====================", "execution_status": null }, { "__type": "Blockquote", "__tag": 4059, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "See " }, { "__type": "InlineRole", "__tag": 4003, "value": "dev-arrayapi", "domain": null, "role": "ref", "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " for more information." } ] } ] } ], "title": [], "level": 0, "target": null }, "Warns": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Raises": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "", "annotation": "ValueError", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "If the mean of the transformed data is not equal to the original variance, indicating a lack of convergence in the O'Brien transform." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Yields": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Methods": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Returns": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "obrientransform", "annotation": "ndarray", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Transformed data for use in an ANOVA. The first dimension of the result corresponds to the sequence of transformed arrays. If the arrays given are all 1-D of the same length, the return value is a 2-D array; otherwise it is a 1-D array of type object, with each element being an ndarray." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Compute the O'Brien transform on input data (any number of arrays)." } ] } ], "title": [], "level": 0, "target": null }, "Receives": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Warnings": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Attributes": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null }, "Parameters": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Parameters", "__tag": 4026, "children": [ { "__type": "DocParam", "__tag": 4016, "name": "sample1, sample2, ...", "annotation": "array_like", "desc": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Any number of arrays." } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Extended Summary": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Used to test for homogeneity of variance prior to running one-way stats. Each array in " }, { "__type": "InlineCode", "__tag": 4051, "value": "*samples" }, { "__type": "Text", "__tag": 4046, "value": " is one level of a factor. If " }, { "__type": "InlineRole", "__tag": 4003, "value": "f_oneway", "domain": null, "role": null, "inventory": null }, { "__type": "Text", "__tag": 4046, "value": " is run on the transformed data and found significant, the variances are unequal. From Maxwell and Delaney " }, { "__type": "FootnoteReference", "__tag": 4066, "label": "1" }, { "__type": "Text", "__tag": 4046, "value": ", p.112." } ] } ], "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": 2505, "item_type": "function", "aliases": [ "scipy.stats.obrientransform" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "We'll test the following data sets for differences in their variance.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "x = [10, 11, 13, 9, 7, 12, 12, 9, 10]\ny = [13, 21, 5, 10, 8, 14, 10, 12, 7, 15]\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nApply the O'Brien transform to the data.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "from scipy.stats import obrientransform\ntx, ty = obrientransform(x, y)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nUse `scipy.stats.f_oneway` to apply a one-way ANOVA test to the\ntransformed data.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "from scipy.stats import f_oneway\nF, p = f_oneway(tx, ty)\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "p\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nIf we require that ``p < 0.05`` for significance, we cannot conclude\nthat the variances are different." } ], "title": [], "level": 0, "target": null }, "see_also": [], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "samples", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "VAR_POSITIONAL", "default": { "__type": "Empty", "__tag": 4031 } } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "obrientransform" }, "references": [ ".. [1] S. E. Maxwell and H. D. Delaney, \"Designing Experiments and", " Analyzing Data: A Model Comparison Perspective\", Wadsworth, 1990." ], "qa": "scipy.stats._stats_py:obrientransform", "arbitrary": [], "local_refs": [ "...", "obrientransform", "sample1", "sample2" ] }