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Add the optimal lmbda as a\nhorizontal line to check that that's really the optimum:\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(lmbdas, llf, 'b.-')\nax.axhline(stats.yeojohnson_llf(lmbda_optimal, x), color='r')\nax.set_xlabel('lmbda parameter')\nax.set_ylabel('Yeo-Johnson log-likelihood')\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nNow add some probability plots to show that where the log-likelihood is\nmaximized the data transformed with `yeojohnson` looks closest to normal:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "locs = [3, 10, 4] # 'lower left', 'center', 'lower right'\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "for lmbda, loc in zip([-1, lmbda_optimal, 9], locs):\n xt = stats.yeojohnson(x, lmbda=lmbda)\n (osm, osr), (slope, intercept, r_sq) = stats.probplot(xt)\n ax_inset = inset_axes(ax, width=\"20%\", height=\"20%\", loc=loc)\n ax_inset.plot(osm, osr, 'c.', osm, slope*osm + intercept, 'k-')\n ax_inset.set_xticklabels([])\n ax_inset.set_yticklabels([])\n ax_inset.set_title(r'$\\lambda=%1.2f$' % lmbda)\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__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-dec038fdb4e73ee5.png" } } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "probplot", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:probplot" }, "kind": "module" }, "descriptions": [], "type": "func" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "yeojohnson", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:yeojohnson" }, "kind": "module" }, "descriptions": [], "type": "func" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "yeojohnson_normmax", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:yeojohnson_normmax" }, "kind": "module" }, "descriptions": [], "type": "func" }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "yeojohnson_normplot", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.stats._morestats:yeojohnson_normplot" }, "kind": "module" }, "descriptions": [], "type": "func" } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "lmb", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "data", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "axis", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "0" }, { "__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": "yeojohnson_llf" }, "references": null, "qa": "scipy.stats._morestats:yeojohnson_llf", "arbitrary": [], "local_refs": [ "axis", "data", "keepdims", "llf", "lmb", "nan_policy" ] }