\u8ba1\u7b97KMO\u503c\uff1a<\/strong> \u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\u8ba1\u7b97KMO\u503c\uff1a<\/p>\n[<\/p>\n
KMO = \\frac{\\sum_{i \\neq j} r_{ij}^2}{\\sum_{i \\neq j} r_{ij}^2 + \\sum_{i \\neq j} p_{ij}^2}<\/p>\n
]<\/p>\n
\u5176\u4e2d\uff0c( r_{ij} )\u662f\u7b80\u5355\u76f8\u5173\u7cfb\u6570\uff0c( p_{ij} )\u662f\u504f\u76f8\u5173\u7cfb\u6570\u3002<\/li>\n<\/p>\n<\/ol>\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684Python\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\nimport pandas as pd<\/p>\n
def calculate_kmo(corr_matrix):<\/p>\n
inv_corr_matrix = np.linalg.inv(corr_matrix)<\/p>\n
partial_corr_matrix = -1 * inv_corr_matrix \/ np.sqrt(np.outer(np.diag(inv_corr_matrix), np.diag(inv_corr_matrix)))<\/p>\n
np.fill_diagonal(partial_corr_matrix, 0)<\/p>\n
simple_corr_sum = np.sum(corr_matrix<strong>2) - np.sum(np.diag(corr_matrix<\/strong>2))<\/p>\n
partial_corr_sum = np.sum(partial_corr_matrix2)<\/p>\n
kmo_value = simple_corr_sum \/ (simple_corr_sum + partial_corr_sum)<\/p>\n
return kmo_value<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\ndata = np.array([[1, 0.8, 0.6],<\/p>\n
[0.8, 1, 0.7],<\/p>\n
[0.6, 0.7, 1]])<\/p>\n
kmo = calculate_kmo(data)<\/p>\n
print(f"KMO\u503c\u4e3a: {kmo}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Python\u5e93\u8ba1\u7b97KMO\u503c<\/p>\n<\/p>\n
Python\u6709\u591a\u4e2a\u7edf\u8ba1\u5206\u6790\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u7b80\u5316KMO\u68c0\u9a8c\u7684\u8ba1\u7b97\uff0c\u5982factor_analyzer<\/code>\u5e93\u3002\u8fd9\u4e2a\u5e93\u63d0\u4f9b\u4e86\u4e13\u95e8\u7684\u51fd\u6570\u6765\u8ba1\u7b97KMO\u503c\u3002<\/p>\n<\/p>\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5factor_analyzer<\/code>\u5e93\uff1a<\/p>\n<\/p>\npip install factor-analyzer<\/p>\n<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8ba1\u7b97KMO\u503c\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\nfrom factor_analyzer.factor_analyzer import calculate_kmo<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\ndata = pd.DataFrame({<\/p>\n
'Variable1': [1, 0.8, 0.6],<\/p>\n
'Variable2': [0.8, 1, 0.7],<\/p>\n
'Variable3': [0.6, 0.7, 1]<\/p>\n
})<\/p>\n
kmo_all, kmo_model = calculate_kmo(data)<\/p>\n
print(f"\u5404\u53d8\u91cf\u7684KMO\u503c: {kmo_all}")<\/p>\n
print(f"\u603b\u4f53KMO\u503c: {kmo_model}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u89e3\u91caKMO\u68c0\u9a8c\u7ed3\u679c<\/p>\n<\/p>\n
KMO\u68c0\u9a8c\u7ed3\u679c\u7528\u4e8e\u5224\u65ad\u6570\u636e\u662f\u5426\u9002\u5408\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u3002\u901a\u5e38\uff0cKMO\u503c\u8d8a\u9ad8\uff0c\u6570\u636e\u8d8a\u9002\u5408\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u3002\u5982\u679cKMO\u503c\u8f83\u4f4e\uff08\u901a\u5e38\u4f4e\u4e8e0.6\uff09\uff0c\u5219\u8bf4\u660e\u6570\u636e\u4e0d\u9002\u5408\u8fdb\u884c\u56e0\u5b50\u5206\u6790\uff0c\u53ef\u80fd\u9700\u8981\u8003\u8651\u5bf9\u6570\u636e\u8fdb\u884c\u53d8\u6362\u6216\u9009\u62e9\u5176\u4ed6\u53d8\u91cf\u3002<\/p>\n<\/p>\n
KMO\u68c0\u9a8c\u7684\u7ed3\u679c\u4e0d\u4ec5\u53ef\u4ee5\u6307\u5bfc\u56e0\u5b50\u5206\u6790\u7684\u9002\u7528\u6027\uff0c\u8fd8\u53ef\u4ee5\u5e2e\u52a9\u7814\u7a76\u8005\u8bc6\u522b\u6f5c\u5728\u7684\u95ee\u9898\uff0c\u4f8b\u5982\u53d8\u91cf\u95f4\u7684\u591a\u91cd\u5171\u7ebf\u6027\u3002\u901a\u8fc7KMO\u68c0\u9a8c\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7ed3\u6784\uff0c\u5e76\u505a\u51fa\u76f8\u5e94\u7684\u5206\u6790\u51b3\u7b56\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5728\u5b9e\u9645\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u5206\u6790\u4e2d\uff0cKMO\u68c0\u9a8c\u901a\u5e38\u4f5c\u4e3a\u56e0\u5b50\u5206\u6790\u7684\u524d\u7f6e\u6b65\u9aa4\u4e4b\u4e00\u3002\u5728\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u4e4b\u524d\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u4f7f\u7528KMO\u68c0\u9a8c\u6765\u8bc4\u4f30\u6570\u636e\u7684\u9002\u5408\u6027\uff0c\u5e76\u6839\u636e\u68c0\u9a8c\u7ed3\u679c\u8c03\u6574\u5206\u6790\u7b56\u7565\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5b9e\u9645\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
\n- \u5e02\u573a\u8c03\u67e5\uff1a<\/strong> \u5728\u5206\u6790\u6d88\u8d39\u8005\u95ee\u5377\u6570\u636e\u65f6\uff0cKMO\u68c0\u9a8c\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u6f5c\u5728\u7684\u56e0\u5b50\u7ed3\u6784\uff0c\u5982\u6d88\u8d39\u8005\u504f\u597d\u6216\u8d2d\u4e70\u52a8\u673a\u3002<\/li>\n
- \u5fc3\u7406\u5b66\u7814\u7a76\uff1a<\/strong> \u5728\u5206\u6790\u5fc3\u7406\u6d4b\u91cf\u6570\u636e\u65f6\uff0cKMO\u68c0\u9a8c\u53ef\u4ee5\u5e2e\u52a9\u786e\u5b9a\u6d4b\u91cf\u5de5\u5177\u7684\u6548\u5ea6\u548c\u7ed3\u6784\u3002<\/li>\n
- \u6559\u80b2\u7814\u7a76\uff1a<\/strong> \u5728\u5206\u6790\u5b66\u751f\u6210\u7ee9\u6216\u95ee\u5377\u6570\u636e\u65f6\uff0cKMO\u68c0\u9a8c\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u6f5c\u5728\u7684\u5b66\u4e60\u56e0\u7d20\u6216\u6559\u5b66\u6548\u679c\u3002<\/li>\n<\/ol>\n
\u901a\u8fc7KMO\u68c0\u9a8c\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u6f5c\u5728\u7ed3\u6784\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u6709\u6548\u7684\u5206\u6790\u548c\u51b3\u7b56\u3002<\/p>\n<\/p>\n
\u516d\u3001\u6ce8\u610f\u4e8b\u9879\u548c\u603b\u7ed3<\/p>\n<\/p>\n
\u5728\u4f7f\u7528KMO\u68c0\u9a8c\u65f6\uff0c\u6709\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\u9700\u8981\u8003\u8651\uff1a<\/p>\n<\/p>\n
\n- \u6570\u636e\u8d28\u91cf\uff1a<\/strong> \u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\uff0c\u907f\u514d\u7f3a\u5931\u503c\u548c\u5f02\u5e38\u503c\u5bf9KMO\u68c0\u9a8c\u7684\u5f71\u54cd\u3002<\/li>\n
- \u53d8\u91cf\u9009\u62e9\uff1a<\/strong> \u9009\u62e9\u5408\u9002\u7684\u53d8\u91cf\u8fdb\u884cKMO\u68c0\u9a8c\uff0c\u907f\u514d\u591a\u91cd\u5171\u7ebf\u6027\u5bf9\u7ed3\u679c\u7684\u5f71\u54cd\u3002<\/li>\n
- \u7ed3\u679c\u89e3\u91ca\uff1a<\/strong> \u7406\u89e3KMO\u68c0\u9a8c\u7684\u7ed3\u679c\uff0c\u5e76\u7ed3\u5408\u5176\u4ed6\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\u8fdb\u884c\u7efc\u5408\u8bc4\u4f30\u3002<\/li>\n<\/ul>\n
\u603b\u4e4b\uff0cKMO\u68c0\u9a8c\u662f\u6570\u636e\u5206\u6790\u4e2d\u4e00\u4e2a\u91cd\u8981\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7814\u7a76\u8005\u8bc4\u4f30\u6570\u636e\u7684\u56e0\u5b50\u5206\u6790\u9002\u5408\u6027\u3002\u901a\u8fc7Python\u7684\u5b9e\u73b0\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884cKMO\u68c0\u9a8c\uff0c\u5e76\u7ed3\u5408\u5b9e\u9645\u5e94\u7528\u8fdb\u884c\u6df1\u5165\u5206\u6790\u3002\u5e0c\u671b\u672c\u6587\u63d0\u4f9b\u7684\u5185\u5bb9\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528KMO\u68c0\u9a8c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884cKMO\u68c0\u9a8c\uff1f<\/strong>
KMO\uff08Kaiser-Meyer-Olkin\uff09\u68c0\u9a8c\u662f\u4e00\u79cd\u8bc4\u4f30\u53d8\u91cf\u9002\u5408\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u7684\u7edf\u8ba1\u65b9\u6cd5\u3002\u8981\u5728Python\u4e2d\u8fdb\u884cKMO\u68c0\u9a8c\uff0c\u53ef\u4ee5\u4f7f\u7528factor_analyzer<\/code>\u5e93\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u8be5\u5e93\uff0c\u5e76\u4f7f\u7528calculate_kmo<\/code>\u51fd\u6570\u6765\u8ba1\u7b97KMO\u7edf\u8ba1\u91cf\u548c\u6bcf\u4e2a\u53d8\u91cf\u7684KMO\u503c\u3002\u4ee5\u4e0b\u662f\u57fa\u672c\u7684\u6b65\u9aa4\uff1a<\/p>\n\n- \u5bfc\u5165\u5e93\u5e76\u52a0\u8f7d\u6570\u636e\u3002<\/li>\n
- \u4f7f\u7528
calculate_kmo<\/code>\u51fd\u6570\u6765\u8ba1\u7b97KMO\u503c\u3002<\/li>\n- \u89e3\u91ca\u7ed3\u679c\uff0cKMO\u503c\u63a5\u8fd11\u8868\u793a\u9002\u5408\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u3002<\/li>\n<\/ol>\n
KMO\u503c\u7684\u89e3\u91ca\u6807\u51c6\u662f\u4ec0\u4e48\uff1f<\/strong>
KMO\u503c\u7684\u8303\u56f4\u57280\u52301\u4e4b\u95f4\u3002\u4e00\u822c\u6765\u8bf4\uff0cKMO\u503c\u7684\u89e3\u91ca\u6807\u51c6\u5982\u4e0b\uff1a<\/p>\n\n- 0.90\u53ca\u4ee5\u4e0a\uff1a\u975e\u5e38\u9002\u5408<\/li>\n
- 0.80 – 0.89\uff1a\u9002\u5408<\/li>\n
- 0.70 – 0.79\uff1a\u4e00\u822c<\/li>\n
- 0.60 – 0.69\uff1a\u8fb9\u7f18<\/li>\n
- 0.50 – 0.59\uff1a\u4e0d\u9002\u5408<\/li>\n
- 0.50\u4ee5\u4e0b\uff1a\u6781\u4e0d\u9002\u5408<\/li>\n<\/ul>\n
\u6839\u636e\u8fd9\u4e9b\u6807\u51c6\uff0c\u7814\u7a76\u8005\u53ef\u4ee5\u5224\u65ad\u662f\u5426\u9700\u8981\u8fdb\u884c\u56e0\u5b50\u5206\u6790\u3002<\/p>\n
KMO\u68c0\u9a8c\u4e0eBartlett\u7403\u5f62\u68c0\u9a8c\u6709\u4ec0\u4e48\u533a\u522b\uff1f<\/strong>
KMO\u68c0\u9a8c\u548cBartlett\u7403\u5f62\u68c0\u9a8c\u90fd\u662f\u7528\u4e8e\u8bc4\u4f30\u56e0\u5b50\u5206\u6790\u9002\u7528\u6027\u7684\u65b9\u6cd5\u3002KMO\u68c0\u9a8c\u8861\u91cf\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u7a0b\u5ea6\uff0c\u800cBartlett\u7403\u5f62\u68c0\u9a8c\u5219\u68c0\u9a8c\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u662f\u5426\u663e\u8457\u3002KMO\u503c\u8d8a\u9ad8\uff0c\u8868\u660e\u6570\u636e\u8d8a\u9002\u5408\u8fdb\u884c\u56e0\u5b50\u5206\u6790\uff0c\u800cBartlett\u68c0\u9a8c\u7684P\u503c\u5219\u8d8a\u5c0f\uff0c\u8868\u793a\u76f8\u5173\u6027\u8d8a\u663e\u8457\u3002\u8fd9\u4e24\u8005\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528\uff0c\u5e2e\u52a9\u7814\u7a76\u8005\u5224\u65ad\u6570\u636e\u662f\u5426\u9002\u5408\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u8fdb\u884cKMO\uff08KAIser-Meyer-Olkin\uff09\u68c0\u9a8c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5176\u4e2d\u5305\u62ec\u4f7f\u7528\u4e13\u95e8\u7684\u7edf\u8ba1\u5305\u3001\u624b […]","protected":false},"author":3,"featured_media":968107,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/968096"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=968096"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/968096\/revisions"}],"predecessor-version":[{"id":968111,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/968096\/revisions\/968111"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/968107"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=968096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=968096"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=968096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\u5982\u4f55\u7528python\u505akmo\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24183040\/2c825907-979d-4b98-ab65-b71ac0624a21.webp\")
<\/p>\n