lavaan\u7b80\u660e\u6559\u7a0b [\u4e2d\u6587\u7ffb\u8bd1\u7248]<\/h1> \n
\u8bd1\u8005\u6ce8\uff1a\u6b64\u6587\u6863\u539f\u4f5c\u8005\u4e3a\u6bd4\u5229\u65f6Ghent\u5927\u5b66\u7684Yves Rosseel\u535a\u58eb\uff0clavaan\u4ea6\u4e3a\u5176\u5f00\u53d1\uff0c\u5b8c\u5168\u5f00\u6e90\u3001\u514d\u8d39\u3002\u6211\u5728\u5b66\u4e60\u7684\u65f6\u5019\u987a\u624b\u7ffb\u8bd1\u4e86\u4e00\u4e0b\uff0c\u5411Yves\u7684\u5f00\u6e90\u7cbe\u795e\u81f4\u656c\u3002\u6b64\u7ffb\u8bd1\u56e0\u5077\u61d2\u90e8\u5206\u5220\u51cf\uff0c\u4f46\u4e5f\u6709\u589e\u52a0\uff0c\u6709\u9519\u8bef\u8bf7\u7559\u8a00<\/strong><\/p> \n \u300c\u8f6c\u8f7d\u8bf7\u6ce8\u660e\u51fa\u5904\u300d<\/strong><\/p> \n lavaan\u7b80\u660e\u6559\u7a0b [\u4e2d\u6587\u7ffb\u8bd1\u7248] \u6b64\u6559\u7a0b\u9996\u5148\u4ecb\u7ecdlavaan\u7684\u57fa\u672c\u7ec4\u6210\u90e8\u5206\uff1a\u6a21\u578b\u8bed\u6cd5\uff0c\u62df\u5408\u65b9\u7a0b(CFA, SEM\u548cgrowth)\uff0c\u7528\u6765\u5448\u73b0\u7ed3\u679c\u7684\u4e3b\u8981\u51fd\u6570(summary, coef, fitted, inspect)\uff1b \u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u6709\u4ee5\u4e0b\u51e0\u70b9\u9700\u8981\u6ce8\u610f\uff1a<\/p> \n \u542f\u52a8R\uff0c\u5e76\u8f93\u5165\uff1a<\/p> \n \u51fa\u73b0\u4ee5\u4e0b\u63d0\u793a\uff0c\u8868\u793a\u8f7d\u5165\u6210\u529f\u3002 lavaan\u5305\u7684\u6838\u5fc3\u662f\u63cf\u8ff0\u6574\u4e2a\u6a21\u578b\u7684\u201c\u6a21\u578b\u8bed\u6cd5\u201d\u3002\u8fd9\u90e8\u5206\u7b80\u5355\u4ecb\u7ecd\u8bed\u6cd5\uff0c\u66f4\u591a\u7ec6\u8282\u5728\u63a5\u4e0b\u6765\u7684\u793a\u4f8b\u4e2d\u53ef\u89c1\u3002<\/p> \n R\u73af\u5883\u4e0b\u7684\u56de\u5f52\u65b9\u7a0b\u6709\u5982\u4e0b\u5f62\u5f0f\uff1a<\/p> \n \u5728lavaan\u4e2d\uff0c\u4e00\u4e2a\u5178\u578b\u6a21\u578b\u662f\u4e00\u4e2a\u56de\u5f52\u65b9\u7a0b\u7ec4\uff0c\u5176\u4e2d\u53ef\u4ee5\u5305\u542b\u6f5c\u53d8\u91cf\uff0c\u4f8b\u5982\uff1a<\/p> \n \u6211\u4eec\u5fc5\u987b\u901a\u8fc7\u6307\u793a\u7b26 \u65b9\u5dee\u548c\u534f\u65b9\u5dee\u8868\u793a\u5982\u4e0b\uff1a<\/p> \n \u53ea\u6709\u622a\u8ddd\u9879\u7684\u56de\u5f52\u65b9\u7a0b\u8868\u8fbe\u5982\u4e0b\uff1a<\/p> \n \u4ee5\u4e0a4\u79cd\u516c\u5f0f\u79cd\u7c7b( \u5982\u679c\u6a21\u578b\u5f88\u957f\uff0c\u53ef\u4ee5\u5c06\u6a21\u578b(\u5355\u5f15\u53f7\u4e4b\u95f4\u7684\u5185\u5bb9)\u50a8\u5b58\u5165myModel.lav\u7684txt\u6587\u6863\u4e2d\uff0c\u7528\u4ee5\u4e0b\u547d\u4ee4\u8bfb\u53d6\uff1a<\/p> \n lavaan\u5305\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5185\u7f6e\u6570\u636e\u96c6\u53eb\u505a \u6570\u636e\u5f62\u5f0f\u662f\u8fd9\u6837\u7684\uff1a \u6b64\u6570\u636e\u96c6\u5305\u542b\u4e86\u6765\u81ea\u4e24\u4e2a\u5b66\u6821\u7684\u4e03\u3001\u516b\u5e74\u7ea7\u5b69\u5b50\u7684\u667a\u529b\u80fd\u529b\u6d4b\u9a8c\u5206\u6570\u3002\u5728\u6211\u4eec\u7684\u7248\u672c\u91cc\uff0c\u53ea\u5305\u542b\u539f\u670926\u4e2a\u6d4b\u8bd5\u4e2d\u76849\u4e2a\uff0c\u8fd99\u4e2a\u6d4b\u8bd5\u5206\u6570\u4f5c\u4e3a9\u4e2a\u6d4b\u91cf\u53d8\u91cf\u5206\u522b\u5bf9\u5e943\u4e2a\u6f5c\u53d8\u91cf\uff1a<\/p> \n \u6a21\u578b\u5982\u4e0b\u56fe\u6240\u793a\uff1a \u5efa\u7acb\u6a21\u578b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p> \n \u7ed3\u679c\u5c55\u793a\u5982\u4e0b\uff1a<\/p> \n
\n \u76ee\u5f55<\/h1> \n
\u76ee\u5f55
\u6458\u8981<\/p> \n \n
6.1 \u56fa\u5b9a\u53c2\u6570
6.2 \u521d\u503c
6.3 \u53c2\u6570\u6807\u7b7e
6.4 \u4fee\u6539\u5668
6.5 \u7b80\u5355\u76f8\u7b49\u7ea6\u675f
6.6 \u975e\u7ebf\u6027\u76f8\u7b49\u548c\u4e0d\u76f8\u7b49\u7ea6\u675f<\/li> \n
8.1 \u5728\u90e8\u5206\u7ec4\u4e2d\u56fa\u5b9a\u53c2\u6570
8.2 \u7ea6\u675f\u4e00\u4e2a\u53c2\u6570\u4f7f\u5176\u5728\u5404\u7ec4\u4e2d\u76f8\u7b49
8.3 \u7ea6\u675f\u4e00\u7ec4\u53c2\u6570\u4f7f\u5176\u5728\u5404\u7ec4\u4e2d\u76f8\u7b49
8.4 \u6d4b\u91cf\u4e0d\u53d8\u6027<\/li> \n
12.1 \u4f30\u8ba1\u65b9\u6cd5
12.2 \u6700\u5927\u4f3c\u7136\u4f30\u8ba1
12.3 \u7f3a\u5931\u503c<\/li> \n
\n \u6458\u8981<\/h1> \n
\u7136\u540e\u63d0\u4f9b\u4e24\u4e2a\u5b9e\u4f8b\uff1b
\u6700\u540e\u518d\u8ba8\u8bba\u4e00\u4e9b\u91cd\u8981\u8bdd\u9898\uff1a\u5747\u503c\u7ed3\u6784\u6a21\u578b(meanstructures)\uff0c\u591a\u7ec4\u6a21\u578b(multiple groups)\uff0c\u589e\u957f\u66f2\u7ebf\u6a21\u578b(growth curve models)\uff0c\u4e2d\u4ecb\u5206\u6790(mediation analysis)\uff0c\u5206\u7c7b\u6570\u636e(categorial data)\u7b49\u3002<\/p> \n
\n 1. \u5728\u5f00\u59cb\u4e4b\u524d<\/h2> \n
\n
\n 2. \u5b89\u88c5lavaan\u5305<\/h2> \n
install.packages("lavaan", dependencies = TRUE) # \u5b89\u88c5lavaan\u5305\nlibrary(lavaan) # \u8f7d\u5165lavaan\u5305<\/code><\/pre> \n
<\/p> \n
\n 3. \u6a21\u578b\u8bed\u6cd5<\/h2> \n
y ~ x1 + x2 + x3 + x4 # ~\u5de6\u8fb9\u4e3a\u56e0\u53d8\u91cfy<\/code><\/pre> \n
y ~ f1 + f2 + x1 + x2\nf1 ~ f2 + f3\nf2 ~ f3 + x1 + x2<\/code><\/pre> \n
=~<\/code>(measured by)\u6765\u201c\u5b9a\u4e49\u201d\u6f5c\u53d8\u91cf\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u6765\u5b9a\u4e49\u6f5c\u53d8\u91cff1, f2\u548cf3:<\/p> \n
f1 =~ y1 + y2 + y3\nf2 =~ y4 + y5 + y6\nf3 =~ y7 + y8 + y9 + y10<\/code><\/pre> \n
y1 ~~ y1 # \u65b9\u5dee\ny1 ~~ y2 # \u534f\u65b9\u5dee\nf1 ~~ f2 # \u534f\u65b9\u5dee<\/code><\/pre> \n
y1 ~ 1\nf1 ~ 1<\/code><\/pre> \n
~<\/code>,
~~<\/code>,
=~<\/code>,
~ 1<\/code>)\u7ec4\u5408\u6210\u5b8c\u6574\u7684\u6a21\u578b\u8bed\u6cd5\uff0c\u7528\u5355\u5f15\u53f7\u8868\u793a\u5982\u4e0b\uff1a<\/p> \n
myModel <- ' # \u4e3b\u8981\u56de\u5f52\u65b9\u7a0b\n y1 + y2 ~ f1 + f2 + x1 + x2\n f1 ~ f2 + f3\n f2 ~ f3 + x1 + x2\n \n # \u5b9a\u4e49\u6f5c\u53d8\u91cf\n f1 =~ y1 + y2 + y3\n f2 =~ y4 + y5 + y6\n f3 =~ y7 + y8 + y9 + y10\n\n # \u65b9\u5dee\u548c\u534f\u65b9\u5dee\n y1 ~~ y1\n y1 ~~ y2\n f1 ~~ f2\n \n # \u622a\u8ddd\u9879\n y1 ~ 1\n f1 ~ 1<\/code><\/pre> \n
myModel <- readLines("\/mydirectory\/myModel.lav") # \u8fd9\u91cc\u9700\u8981\u7edd\u5bf9\u8def\u5f84<\/code><\/pre> \n
\n 4. \u4f8b1\uff1a\u9a8c\u8bc1\u6027\u56e0\u5b50\u5206\u6790(CFA)<\/h2> \n
HolzingerSwineford<\/code>\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\u67e5\u770b\u6570\u636e\u96c6\u63cf\u8ff0\uff1a<\/p> \n
?HolzingerSwineford<\/code><\/pre> \n
<\/p> \n \n
<\/p> \n HS.model <- ' visual =~ x1 + x2 + x3\n textual =~ x4 + x5 + x6\n speed =~ x7 + x8 + x9'\n# \u7136\u540e\u62df\u5408cfa\u51fd\u6570\uff0c\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u6a21\u578b\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f\u6570\u636e\u96c6\nfit <- cfa(HS.model, data = HolzingerSwineford1939)\n# \u518d\u901a\u8fc7summary\u51fd\u6570\u7ed9\u51fa\u7ed3\u679c\nsummary(fit, fit.measure = TRUE)<\/code><\/pre> \n
# \u524d6\u884c\u4e3a\u5934\u90e8\uff0c\u5305\u542b\u7248\u672c\u53f7\uff0c\u6536\u655b\u60c5\u51b5\uff0c\u8fed\u4ee3\u6b21\u6570\uff0c\u89c2\u6d4b\u6570\uff0c\u7528\u6765\u8ba1\u7b97\u53c2\u6570\u7684\u4f30\u8ba1\u91cf\uff0c\u6a21\u578b\u68c0\u9a8c\u7edf\u8ba1\u91cf\uff0c\u81ea\u7531\u5ea6\u548c\u76f8\u5173\u7684p\u503c\nlavaan (0.5-23.1097) converged normally after 35 iterations \n\n Number of observations 301\n\n Estimator ML\n Minimum Function Test Statistic 85.306\n Degrees of freedom 24\n P-value (Chi-square) 0.000\n\n#\u53c2\u6570fit.measure = TRUE\u4f1a\u663e\u793a\u4e0b\u9762\u4ecemodel test baseline model\u5230SRMR\u7684\u90e8\u5206\nModel test baseline model:\n\n Minimum Function Test Statistic 918.852\n Degrees of freedom 36\n P-value 0.000\n\nUser model versus baseline model:\n\n Comparative Fit Index (CFI) 0.931\n Tucker-Lewis Index (TLI) 0.896\n\nLoglikelihood and Information Criteria:\n\n Loglikelihood user model (H0) -3737.745\n Loglikelihood unrestricted model (H1) -3695.092\n\n Number of free parameters 21\n Akaike (AIC) 7517.490\n Bayesian (BIC) 7595.339\n Sample-size adjusted Bayesian (BIC) 7528.739\n\nRoot Mean Square Error of Approximation:\n\n RMSEA 0.092\n 90 Percent Confidence Interval 0.071 0.114\n P-value RMSEA <= 0.05 0.001\n\nStanda","orderid":"0","title":"\u57fa\u4e8eR\u8bed\u8a00\u7684\u7ed3\u6784\u65b9\u7a0b\uff1alavaan\u7b80\u660e\u6559\u7a0b [\u4e2d\u6587\u7ffb\u8bd1\u7248](\u4e00)","smalltitle":"","mid":"0","fname":"R\u8bed\u8a00","special_id":"0","bak_id":"0","info":"0","hits":"718","pages":"9","comments":"0","posttime":"2019-09-03 02:41:29","list":"1567449689","username":"admin","author":"","copyfrom":"","copyfromurl":"","titlecolor":"","fonttype":"0","titleicon":"0","picurl":"https:\/\/www.cppentry.com\/upload_files\/","ispic":"0","yz":"1","yzer":"","yztime":"0","levels":"0","levelstime":"0","keywords":"\u57fa\u4e8e<\/A> \u8bed\u8a00<\/A> \u7ed3\u6784<\/A> \u65b9\u7a0b<\/A> lavaan<\/A> \u7b80\u660e\u6559\u7a0b<\/A> \u4e2d\u6587<\/A> \u7ffb\u8bd1<\/A>","jumpurl":"","iframeurl":"","style":"","template":"a:3:{s:4:\"head\";s:0:\"\";s:4:\"foot\";s:0:\"\";s:8:\"bencandy\";s:0:\"\";}","target":"0","ip":"120.229.33.54","lastfid":"0","money":"0","buyuser":"","passwd":"","allowdown":"","allowview":"","editer":"","edittime":"0","begintime":"0","endtime":"0","description":"\u57fa\u4e8eR\u8bed\u8a00\u7684\u7ed3\u6784\u65b9\u7a0b\uff1alavaan\u7b80\u660e\u6559\u7a0b [\u4e2d\u6587\u7ffb\u8bd1\u7248]","lastview":"1714080709","digg_num":"0","digg_time":"0","forbidcomment":"0","ifvote":"0","heart":"","htmlname":"","city_id":"0"},"page":"1"}