{"id":136,"date":"2022-10-02T18:16:59","date_gmt":"2022-10-02T09:16:59","guid":{"rendered":"https:\/\/stats.ni.tama.ac.jp\/?p=136"},"modified":"2022-10-02T18:34:21","modified_gmt":"2022-10-02T09:34:21","slug":"%e6%8e%a2%e7%b4%a2%e7%9a%84%e5%9b%a0%e5%ad%90%e5%88%86%e6%9e%90r","status":"publish","type":"post","link":"https:\/\/stats.ni.tama.ac.jp\/?p=136","title":{"rendered":"\u63a2\u7d22\u7684\u56e0\u5b50\u5206\u6790[R]"},"content":{"rendered":"\n<div class=\"wp-block-jetpack-markdown\"><p>R\u3067\u63a2\u7d22\u7684\u56e0\u5b50\u5206\u6790\u3092\u3059\u308b\u306b\u306f2\u3064\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u8aad\u307f\u8fbc\u307f\u304c\u5fc5\u8981\u3067\u3042\u308b\u3002<\/p>\n<pre><code class=\"language-{r}\">library( &quot;psych&quot; )\nlibrary( &quot;GPArotation&quot; )\n<\/code><\/pre>\n<h2>\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u30fb\u30c7\u30fc\u30bf<\/h2>\n<p>psych\u30d1\u30c3\u30b1\u30fc\u30b8\u306b\u306fHarman.Holzinger\u3068\u3044\u3046\u8a8d\u77e5\u80fd\u529b\u30c6\u30b9\u30c8(IQ\u30c6\u30b9\u30c8)\u306e\u30c7\u30fc\u30bf\u304c\u542b\u307e\u308c\u3066\u304a\u308a\u3001\u305d\u306e\u30c7\u30fc\u30bf\u3092\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u30fb\u30c7\u30fc\u30bf\u3068\u3057\u3066\u5229\u7528\u3059\u308b\u3002<\/p>\n<p>psych\u306eDocumentation\u306b\u306f\u4e0b\u8a18\u306e\u3088\u3046\u306b\u8a18\u8ff0\u3055\u308c\u3066\u3044\u308b\u3002<\/p>\n<blockquote>\n<p>Harman.Holzinger: \u8a8d\u77e5\u80fd\u529b\u30c6\u30b9\u30c8\u306e9\u00d79\u76f8\u95a2\u884c\u5217\u3001N=696\u3002 Harman.Holzinger\u3002Harman (1967, p 244)\u306e9\u3064\u306e\u5fc3\u7406\u5909\u6570\u306f\u3001K.J. Holzinger\u306e696\u4eba\u306e\u53c2\u52a0\u8005\u306b\u3088\u308b\u672a\u767a\u8868\u306e\u6388\u696d\u30ce\u30fc\u30c8\u304b\u3089\u53d6\u3089\u308c\u305f\u3082\u306e\u3067\u3042\u308b\u3002\u3053\u308c\u306f\u30014\u3064\u306e\u56e0\u5b50\u3092\u6301\u306412\u306e\u30c6\u30b9\u30c8\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u3067\u3042\u308b\u3002\u3053\u308c\u306f\u30012\u56e0\u5b50\u89e3\u306e\u3082\u3046\u3072\u3068\u3064\u306e\u7d20\u6674\u3089\u3057\u3044\u4f8b\u3067\u3042\u308b\u3002Bentler (2007)\u306f\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u3001\u4fe1\u983c\u6027\u5206\u6790\u306b\u3064\u3044\u3066\u8b70\u8ad6\u3057\u3066\u3044\u308b\u3002\u3053\u306e\u30c7\u30fc\u30bf\u306f\u3001\u660e\u78ba\u306a2\u56e0\u5b50\u69cb\u9020\u3092\u793a\u3057\u3001\u30aa\u30e1\u30ac\u95a2\u6570\u306b\u542b\u307e\u308c\u308b\u4fe1\u983c\u6027\u306e\u69d8\u3005\u306a\u63a8\u5b9a\u5024\u306e\u826f\u3044\u4f8b\u3068\u306a\u3063\u3066\u3044\u308b\u3002Bifactor\u306eHolzinger\u3084Holzinger.9\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3068\u6df7\u540c\u3057\u306a\u3044\u3088\u3046\u306b\u3057\u3066\u307b\u3057\u3044\u3002<br>\npsych &#8211; Harman: Five data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt<br>\n<a href=\"https:\/\/www.rdocumentation.org\/packages\/psych\/versions\/2.2.5\/topics\/Harman\">https:\/\/www.rdocumentation.org\/packages\/psych\/versions\/2.2.5\/topics\/Harman<\/a><\/p>\n<\/blockquote>\n<p>\u305f\u3060\u3001Harman (1976)\u3092\u898b\u308b\u9650\u308a\u3001\u3053\u306e\u30c7\u30fc\u30bf\u306f4\u56e0\u5b50\u3067\u306f\u306a\u304f\u30013\u56e0\u5b50\u3067\u3042\u308b\u3002\u304a\u305d\u3089\u304f\u3001psych\u30d1\u30c3\u30b1\u30fc\u30b8\u306eDocumentation\u306f\u8aa4\u8a18\u3067\u3042\u308d\u3046\u3002<br>\n3\u56e0\u5b50\u306e\u5177\u4f53\u7684\u306a\u5185\u5bb9\u306f\u4e0b\u8a18\u306e\u3088\u3046\u306b\u793a\u3055\u308c\u3066\u3044\u308b\u3002<\/p>\n<ul>\n<li>Verbal: Word.meaning, Sentence.completion, Odd.words<\/li>\n<li>Arithmetic: Mixed.arithmetic, Remainders, Missing.numbers<\/li>\n<li>Spatial: Gloves, Boots, Hatchets<\/li>\n<\/ul>\n<p>Holzinger\u306b\u3088\u3063\u3066\u63d0\u6848\u3055\u308c\u305f\u77e5\u7684\u80fd\u529b\u306e\u30c6\u30b9\u30c8\u3001\u3064\u307e\u308aIQ\u30c6\u30b9\u30c8\u3067\u3042\u308b\u3002\n\u3053\u306e\u30c7\u30fc\u30bf\u306e\u521d\u51fa\u306fHolzinger &amp; Swineford(1939)\u3067\u3042\u308b\u304c\u3001Harman(1976)\u3067\u3082\u5f15\u7528\u3055\u308c\u3066\u304a\u308a\u3001\u3053\u3053\u304b\u3089\u5f15\u7528\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304b\u3089<code>Harman.Holzinger<\/code>\u3068\u3044\u3046\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u540d\u306b\u306a\u3063\u305f\u3088\u3046\u3067\u3042\u308b\u3002<\/p>\n<h2>\u76f8\u95a2\u884c\u5217<\/h2>\n<p><code>cor.plot()<\/code>\u3092\u4f7f\u3046\u3053\u3068\u3067\u30d2\u30fc\u30c8\u30de\u30c3\u30d7\u3092\u63cf\u304f\u3053\u3068\u304c\u3067\u304d\u308b\u3002<\/p>\n<pre><code class=\"language-{r}\">data(Harman)\ncor.plot(Harman.Holzinger)\n<\/code><\/pre>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"603\" height=\"595\" src=\"https:\/\/stats.ni.tama.ac.jp\/wp-content\/uploads\/2022\/10\/corplot.png\" alt=\"\" class=\"wp-image-137\" srcset=\"https:\/\/stats.ni.tama.ac.jp\/wp-content\/uploads\/2022\/10\/corplot.png 603w, https:\/\/stats.ni.tama.ac.jp\/wp-content\/uploads\/2022\/10\/corplot-300x296.png 300w\" sizes=\"auto, (max-width: 603px) 100vw, 603px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-jetpack-markdown\"><p>\u76f8\u95a2\u884c\u5217\u304b\u3089\u30013\u56e0\u5b50\u3067\u3042\u308b\u3053\u3068\u306f\u3060\u3044\u305f\u3044\u60f3\u5b9a\u3067\u304d\u308b\u3002<\/p>\n<h2>\u63a2\u7d22\u7684\u56e0\u5b50\u5206\u6790<\/h2>\n<p>\u63a2\u7d22\u7684\u56e0\u5b50\u5206\u6790\u306f<code>fa()<\/code>\u3067\u884c\u3046\u3002<\/p>\n<pre><code class=\"language-{r}\">res_Holzinger_n3 &lt;- fa(Harman.Holzinger, nfactors = 3, fm=&quot;minres&quot;,\n                       rotate=&quot;oblimin&quot;, use=&quot;complete.obs&quot;)\nprint(res_Holzinger_n3, digits=3, sort=T)\n<\/code><\/pre>\n<ul>\n<li>nfactors: \u56e0\u5b50\u6570<\/li>\n<li>fm: \u63a8\u5b9a\u6cd5(\u30c7\u30d5\u30a9\u30eb\u30c8\u306f&quot;minres&quot;)<\/li>\n<li>rotate: \u56de\u8ee2\u306e\u7a2e\u985e(&quot;oblimin&quot;)<\/li>\n<li>digits: \u7d50\u679c\u8868\u793a\u306e\u5c0f\u6570\u70b9\u4ee5\u4e0b\u306e\u6841\u6570<\/li>\n<li>sort: \u56e0\u5b50\u306e\u89e3\u91c8\u3092\u3057\u3084\u3059\u3044\u3088\u3046\u306b\u4e26\u3073\u66ff\u3048\u308b\u5834\u5408\u306f<code>sort=T<\/code>\u3001\u3057\u306a\u3044\u5834\u5408\u306f<code>sort=F<\/code><\/li>\n<\/ul>\n<p>\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u51fa\u529b\u3055\u308c\u308b\u3002<\/p>\n<pre><code>Loading required namespace: GPArotation\n\nFactor Analysis using method =  minres\nCall: fa(r = Harman.Holzinger, nfactors = 3, rotate = &quot;oblimin&quot;, fm = &quot;minres&quot;, \n    use = &quot;complete.obs&quot;)\nStandardized loadings (pattern matrix) based upon correlation matrix\n                    item    MR1    MR3    MR2    h2     u2  com\nMixed_Arithmetic       4  1.006 -0.047 -0.044 0.924 0.0757 1.01\nRemainders             5  0.809  0.034  0.068 0.743 0.2571 1.02\nMissing_Numbers        6  0.749  0.139  0.063 0.756 0.2444 1.08\nWord_meaning           1 -0.053  0.948 -0.028 0.822 0.1779 1.01\nOdd_words              3  0.005  0.823  0.085 0.750 0.2497 1.02\nSentence_completion    2  0.149  0.762 -0.032 0.712 0.2882 1.08\nHatchets               9 -0.039  0.002  0.902 0.785 0.2149 1.00\nBoots                  8  0.030  0.040  0.726 0.575 0.4253 1.01\nGloves                 7  0.176 -0.073  0.547 0.371 0.6289 1.24\n\n                        MR1   MR3   MR2\nSS loadings           2.429 2.278 1.730\nProportion Var        0.270 0.253 0.192\nCumulative Var        0.270 0.523 0.715\nProportion Explained  0.377 0.354 0.269\nCumulative Proportion 0.377 0.731 1.000\n\n With factor correlations of \n      MR1   MR3   MR2\nMR1 1.000 0.586 0.443\nMR3 0.586 1.000 0.428\nMR2 0.443 0.428 1.000\n\nMean item complexity =  1.1\nTest of the hypothesis that 3 factors are sufficient.\n\nThe degrees of freedom for the null model are  36  and the objective function was  5.907\nThe degrees of freedom for the model are 12  and the objective function was  0.017 \n\nThe root mean square of the residuals (RMSR) is  0.006 \nThe df corrected root mean square of the residuals is  0.01 \n\nFit based upon off diagonal values = 1\nMeasures of factor score adequacy             \n                                                    MR1   MR3   MR2\nCorrelation of (regression) scores with factors   0.974 0.955 0.925\nMultiple R square of scores with factors          0.950 0.913 0.855\nMinimum correlation of possible factor scores     0.899 0.826 0.711\n<\/code><\/pre>\n<h2>\u7d50\u679c\u306e\u89e3\u91c8&#8211;\u56e0\u5b50<\/h2>\n<pre><code>                    item    MR1    MR3    MR2    h2     u2  com\nMixed_Arithmetic       4  1.006 -0.047 -0.044 0.924 0.0757 1.01\nRemainders             5  0.809  0.034  0.068 0.743 0.2571 1.02\nMissing_Numbers        6  0.749  0.139  0.063 0.756 0.2444 1.08\nWord_meaning           1 -0.053  0.948 -0.028 0.822 0.1779 1.01\nOdd_words              3  0.005  0.823  0.085 0.750 0.2497 1.02\nSentence_completion    2  0.149  0.762 -0.032 0.712 0.2882 1.08\nHatchets               9 -0.039  0.002  0.902 0.785 0.2149 1.00\nBoots                  8  0.030  0.040  0.726 0.575 0.4253 1.01\nGloves                 7  0.176 -0.073  0.547 0.371 0.6289 1.24\n<\/code><\/pre>\n<p>MR1\u306f\u3001<code>Mixed_Arithmetic<\/code>\u3001<code>Remainders<\/code>\u3001<code>Missing_Numbers<\/code>\u306e3\u3064\u306e\u89b3\u6e2c\u5909\u6570\u306e\u5024\u304c\u9ad8\u304f\u3001\u4ed6\u306e\u56e0\u5b50\u306e\u5024\u306f\u9ad8\u304f\u306a\u3044\u3053\u3068\u304b\u3089\u3001MR1\u3053\u306e3\u3064\u306e\u5909\u6570\u304b\u3089\u306a\u308b\u56e0\u5b50\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308b\u3002\u89b3\u6e2c\u5909\u6570\u306e\u6027\u8cea\u304b\u3089&quot;Arithmetic&quot;\u3068\u540d\u3065\u3051\u308b\u306e\u304c\u9069\u5f53\u3067\u3042\u308d\u3046\u3002<br>\n\u540c\u3058\u3088\u3046\u306bMR2\u306e\u5024\u304c\u9ad8\u3044\u306e\u306f\u3001<code>Hatchets<\/code>\u3001<code>Boots<\/code>\u3001<code>Gloves<\/code>\u306e3\u3064\u306e\u89b3\u6e2c\u5909\u6570\u306e\u5024\u304c\u9ad8\u3044\u3002\u3053\u306e\u56e0\u5b50\u306f&quot;Spatial&quot;\u3068\u3044\u3046\u540d\u79f0\u304c\u9069\u5f53\u3067\u3042\u308d\u3046\u3002<br>\n\u56e0\u5b50\u306e\u30cd\u30fc\u30df\u30f3\u30b0\u306f\u5206\u6790\u8005\u306b\u3086\u3060\u306d\u3089\u308c\u3066\u304a\u308a\u3001\u542b\u307e\u308c\u308b\u89b3\u6e2c\u5909\u6570\u304b\u3089\u59a5\u5f53\u306a\u56e0\u5b50\u540d\u3092\u8003\u3048\u308b\u4f5c\u696d\u304c\u56e0\u5b50\u5206\u6790\u306e\u4e2d\u3067\u3082\u91cd\u8981\u306a\u4f5c\u696d\u3068\u306a\u308b\u3002<\/p>\n<ul>\n<li>h2: \u5171\u901a\u6027: \u5171\u901a\u56e0\u5b50\u306e\u5f71\u97ff\u306e\u7a0b\u5ea6\u3002<code>Verbal<\/code>\u3001<code>Arithmetic<\/code>\u3001<code>Spatial<\/code>\u306e3\u3064\u3059\u3079\u3066\u306e\u5171\u901a\u56e0\u5b50\u3067\u8aac\u660e\u3055\u308c\u308b\u5272\u5408\u3002<\/li>\n<li>u2: \u72ec\u81ea\u6027: \u72ec\u81ea\u56e0\u5b50\u306e\u5f71\u97ff\u306e\u7a0b\u5ea6\u3002\u7b2c1\u56e0\u5b50\u306e\u5834\u5408\u30013\u3064\u306e\u89b3\u6e2c\u5909\u6570<code>Mixed_Arithmetic<\/code>\u3001<code>Remainders<\/code>\u3001<code>Missing_Numbers<\/code>\u306e\u56e0\u5b50\u3067\u8aac\u660e\u3055\u308c\u308b\u5272\u5408\u3002<\/li>\n<\/ul>\n<p>\u5171\u901a\u6027\u3068\u72ec\u81ea\u6027\u306f\u52a0\u7b97\u3059\u308b\u3068&quot;1&quot;\u306b\u306a\u308b\u3002<\/p>\n<ul>\n<li>com: \u8907\u96d1\u6027(\u30db\u30d5\u30de\u30f3\u306e\u6307\u6570): <img decoding=\"async\" src=\"https:\/\/s0.wp.com\/latex.php?latex=a_i&#038;bg=ffffff&#038;fg=000&#038;s=0&#038;c=20201002\" alt=\"a_i\" class=\"latex\" \/>\u306fi\u756a\u76ee\u306e\u56e0\u5b50\u3078\u306e\u56e0\u5b50\u8ca0\u8377\u3068\u3057\u305f\u5834\u5408\u3001\u4e0b\u8a18\u306e\u8a08\u7b97\u5f0f\u3067\u3042\u30021\u306b\u8fd1\u3044\u65b9\u304c\u5358\u7d14\u69cb\u9020\u3068\u306a\u308b\u3002<\/li>\n<\/ul>\n<p><img decoding=\"async\" src=\"https:\/\/s0.wp.com\/latex.php?latex=%5Cdfrac%7B%28%5Csum+a_%7Bi%7D%5E%7B2%7D%29%5E%7B2%7D%7D+%7B%5Csum+a_%7Bi%7D%5E%7B4%7D%7D&#038;bg=ffffff&#038;fg=000&#038;s=0&#038;c=20201002\" alt=\"&#92;dfrac{(&#92;sum a_{i}^{2})^{2}} {&#92;sum a_{i}^{4}}\" class=\"latex\" \/><\/p>\n<h2>\u4ed6\u306e\u51fa\u529b\u306e\u89e3\u91c8<\/h2>\n<pre><code>                        MR1   MR3   MR2\nSS loadings           2.429 2.278 1.730\nProportion Var        0.270 0.253 0.192\nCumulative Var        0.270 0.523 0.715\nProportion Explained  0.377 0.354 0.269\nCumulative Proportion 0.377 0.731 1.000\n<\/code><\/pre>\n<p>SS loadings: \u56e0\u5b50\u5bc4\u4e0e\nProportion Var: \u56e0\u5b50\u5bc4\u4e0e\u7387\nCumulative Var: \u7d2f\u7a4d\u56e0\u5b50\u5bc4\u4e0e\u7387\nProportion Explained: \u8aac\u660e\u7387\nCumulative Proportion: \u7d2f\u7a4d\u8aac\u660e\u7387<\/p>\n<h3>\u56e0\u5b50\u5bc4\u4e0e\u7387<\/h3>\n<p>\u56e0\u5b50\u5bc4\u4e0e\u306f\u5404\u56e0\u5b50\u3054\u3068\u306b\u56e0\u5b50\u8ca0\u8377\u91cf\u306e\u4e8c\u4e57\u548c\u3057\u305f\u3082\u306e\u3002<\/p>\n<h3>\u56e0\u5b50\u5bc4\u4e0e\u7387<\/h3>\n<p>\u56e0\u5b50\u5bc4\u4e0e\u3092\u89b3\u6e2c\u5909\u6570\u306e\u6570\u3067\u9664\u3057\u305f\u3082\u306e\u3002<br>\n\u5171\u901a\u6027\u306e\u6700\u5927\u5024\u306f1 (\u72ec\u81ea\u6027\u304c0) \u3067\u3042\u308a\u3001\u56e0\u5b50\u6570\u304c\u3072\u3068\u3064\u3067\u3042\u308c\u3070\u305d\u306e\u56e0\u5b50\u8ca0\u8377\u91cf\u306fl\u306b\u306a\u308b\u3002<\/p>\n<h3>\u8aac\u660e\u7387<\/h3>\n<p>\u56e0\u5b50\u5bc4\u4e0e\u3092\u56e0\u5b50\u5bc4\u4e0e\u306e\u5408\u8a08\u3067\u9664\u3057\u305f\u3082\u306e\u3002<\/p>\n<h2>\u30d1\u30b9\u56f3\u306e\u63cf\u753b<\/h2>\n<pre><code class=\"language-{r}\">fa.diagram(res_Holzinger_n3)\n<\/code><\/pre>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"603\" height=\"595\" src=\"https:\/\/stats.ni.tama.ac.jp\/wp-content\/uploads\/2022\/10\/PathDiagram.png\" alt=\"\" class=\"wp-image-138\" srcset=\"https:\/\/stats.ni.tama.ac.jp\/wp-content\/uploads\/2022\/10\/PathDiagram.png 603w, https:\/\/stats.ni.tama.ac.jp\/wp-content\/uploads\/2022\/10\/PathDiagram-300x296.png 300w\" sizes=\"auto, (max-width: 603px) 100vw, 603px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-jetpack-markdown\"><h2>\u53c2\u8003\u6587\u732e<\/h2>\n<ul>\n<li>Holzinger, K. J., &amp; Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. Chicago University Press.<\/li>\n<li>Harman, H. H. (1976). Modern factor analysis. 3rd ed. Illinois; The University of Chicago.<\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-136","post","type-post","status-publish","format-standard","hentry","category-r"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=\/wp\/v2\/posts\/136","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=136"}],"version-history":[{"count":15,"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=\/wp\/v2\/posts\/136\/revisions"}],"predecessor-version":[{"id":154,"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=\/wp\/v2\/posts\/136\/revisions\/154"}],"wp:attachment":[{"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=136"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=136"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stats.ni.tama.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}