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The method is robust to outliers with an asymptotic breakdown point of 50%." } ] } ], "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_mstats_common.py", "item_line": 208, "item_type": "function", "aliases": [ "scipy.stats.siegelslopes" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "x = np.linspace(-5, 5, num=150)\ny = x + np.random.normal(size=x.size)\ny[11:15] += 10 # add outliers\ny[-5:] -= 7\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nCompute the slope and intercept. For comparison, also compute the\nleast-squares fit with `linregress`:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "res = stats.siegelslopes(y, x)\nlsq_res = stats.linregress(x, y)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nPlot the results. The Siegel regression line is shown in red. The green\nline shows the least-squares fit for comparison.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "fig = plt.figure()\nax = fig.add_subplot(111)\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "ax.plot(x, y, 'b.')\nax.plot(x, res[1] + res[0] * x, 'r-')\nax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-')\n", "execution_status": "failure" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" }, { "__type": "Figure", "__tag": 4024, "value": { "__type": "RefInfo", "__tag": 4000, "module": "scipy", "version": "1.17.1", "kind": "assets", "path": "fig-31a8715527c70664.png" } } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "theilslopes", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "theilslopes" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "a similar technique without repeated medians" } ] } ], "type": "func" } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "y", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "x", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "method", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "hierarchical" }, { "__type": "SigParam", "__tag": 4030, "name": "axis", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "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": "siegelslopes" }, "references": [ ".. [1] A. Siegel, \"Robust Regression Using Repeated Medians\",", " Biometrika, Vol. 69, pp. 242-244, 1982.", "", ".. [2] A. Stein and M. Werman, \"Finding the repeated median regression", " line\", Proceedings of the Third Annual ACM-SIAM Symposium on", " Discrete Algorithms, pp. 409-413, 1992." ], "qa": "scipy.stats._stats_mstats_common:siegelslopes", "arbitrary": [], "local_refs": [ "axis", "keepdims", "method", "nan_policy", "result", "x", "y" ] }