{"id":441,"date":"2019-05-12T09:28:13","date_gmt":"2019-05-12T00:28:13","guid":{"rendered":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/fleiss-kappa-in-r\/"},"modified":"2025-01-06T15:10:40","modified_gmt":"2025-01-06T06:10:40","slug":"fleiss-kappa-in-r","status":"publish","type":"post","link":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/fleiss-kappa-in-r\/","title":{"rendered":"R \u3067 3 \u4eba\u4ee5\u4e0a\u306e\u8a55\u4fa1\u8005\u306e\u30ab\u30c3\u30d1\u4fc2\u6570 \u30d5\u30e9\u30a4\u30b9\u306e\u30ab\u30c3\u30d1\u4fc2\u6570\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"\n

\u30d5\u30e9\u30a4\u30b9\uff08Fleiss\uff09\u306e\u30ab\u30c3\u30d1\u4fc2\u6570\uff08kappa\uff09\u306f\u30013\u4eba\u4ee5\u4e0a\u306e\u8a55\u4fa1\u8005\u306e\u8a55\u4fa1\u304c\u4e00\u81f4\u3057\u3066\u3044\u308b\u5ea6\u5408\u3044\u3092\u6e2c\u5b9a\u3059\u308b\u4fc2\u6570\u3002<\/p>\n\n\n\n\n\n\n\n

\u30ab\u30c3\u30d1\u4fc2\u6570\u306b\u306f 2 \u4eba\u306e\u5834\u5408\u3068 3 \u4eba\u4ee5\u4e0a\u306e\u5834\u5408\u306e 2 \u7a2e\u985e\u304c\u3042\u308b<\/h2>\n\n\n\n

Cohen’s kappa \u304c\u4e8c\u4eba\u306e\u8a55\u4fa1\u8005\u306e\u4e00\u81f4\u5ea6\u3092\u5224\u65ad\u3059\u308b\u306e\u306b\u5bfe\u3057\u3066\u3001Fleiss’ kappa \u306f\u4e09\u4eba\u4ee5\u4e0a\u306e\u8a55\u4fa1\u8005\u306e\u4e00\u81f4\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002<\/p>\n\n\n\n

\u3044\u308f\u3086\u308b\u30ab\u30c3\u30d1\u4fc2\u6570\uff08Cohen’s kappa\uff09\u306f\u3053\u3061\u3089\u3002<\/p>\n\n\n

\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\u3042\u308f\u305b\u3066\u8aad\u307f\u305f\u3044<\/span>\n\t\t\t\t\t
\"\"<\/figure><\/div>\t\t\t\t\t
\n\t\t\t\t\t\tR \u3067\u30ab\u30c3\u30d1\u4fc2\u6570\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a>\n\t\t\t\t\t\t\u4e8c\u4eba\u306e\u8a55\u4fa1\u8005\u306e\u30ab\u30c6\u30b4\u30ea\u8a55\u4fa1\u306e\u4e00\u81f4\u5ea6\u3092\u898b\u308b\u306e\u304c\u3044\u308f\u3086\u308b\u30ab\u30c3\u30d1\u4fc2\u6570\u3060\u3002 \u30ab\u30c3\u30d1\u306f\u30ae\u30ea\u30b7\u30e3\u6587\u5b57\u306ek\u306e\u30ab\u30c3\u30d1\uff08\u03ba\uff09\u306e\u3053\u3068\u3002 Jacob Cohen\u5148\u751f\u304c\u767a\u660e\u3057\u305f\u306e\u3067\u3001Cohen’s Kappa…<\/span>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\n\n

\u30ab\u30c3\u30d1\u4fc2\u6570 3 \u4eba\u4ee5\u4e0a\u306e\u5834\u5408 \u30d5\u30e9\u30a4\u30b9\u306e\u30ab\u30c3\u30d1\u4fc2\u6570 Fleiss’ kappa \u306e\u8a08\u7b97\u5f0f<\/h2>\n\n\n\n

Fleiss’ kappa \u306f\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n

$ \\bar{P} $ \u306f\u88ab\u9a13\u8005\u5185\u306e\u8a55\u4fa1\u8005\u4e00\u81f4\u5ea6\u306e\u5e73\u5747\u3001 $ \\bar{P_e} $ \u306f\u5404\u8a55\u4fa1\u30ab\u30c6\u30b4\u30ea\u3078\u306e\u96c6\u4e2d\u5ea6\u5408\u3044\u3068\u89e3\u91c8\u3067\u304d\u308b\u3002<\/p>\n\n\n\n

$$ \\kappa = \\frac{\\bar{P} – \\bar{P_e}}{1 – \\bar{P_e}} $$<\/p>\n\n\n\n\n\n\n\n

\u3053\u3053\u3067\u3001$ \\bar{P} $ \u3068 $ \\bar{P_e} $ \u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8a08\u7b97\u3067\u304d\u308b\u3002<\/p>\n\n\n\n

i \u306f\u88ab\u9a13\u8005\u756a\u53f7\u3001N \u306f\u88ab\u9a13\u8005\u6570\u3001n \u306f\u8a55\u4fa1\u8005\u6570\u3001j \u306f\u8a55\u4fa1\u30ab\u30c6\u30b4\u30ea\u756a\u53f7\u3001k \u306f\u8a55\u4fa1\u30ab\u30c6\u30b4\u30ea\u6570\u3002<\/p>\n\n\n\n

$$ \\bar{P} = \\frac{1}{N} \\sum_{i=1}^N P_i $$<\/p>\n\n\n\n

$$ \\bar{P_e} = \\sum_{j=1}^k p_i^2 $$<\/p>\n\n\n\n\n\n\n\n

\u3055\u3089\u306b\u3001$ P_i $ \u3068 $ p_j $ \u306f\u4ee5\u4e0b\u306e\u901a\u308a\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n

$$ P_i = \\frac{1}{n(n – 1)} \\sum_{j=1}^k n_{ij} (n_{ij} – 1) $$<\/p>\n\n\n\n

$$ p_j = \\frac{1}{Nn} \\sum_{i=1}^N n_{ij} $$<\/p>\n\n\n\n\n\n\n\n

\uff1e\uff1e\u3082\u3046\u7d71\u8a08\u3067\u60a9\u3080\u306e\u306f\u7d42\u308f\u308a\u306b\u3057\u307e\u305b\u3093\u304b\uff1f\u00a0<\/a><\/strong><\/span><\/p>\r\n\"\"<\/a>\r\n

\u21911\u4e07\u4eba\u4ee5\u4e0a\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u8cfc\u8aad\u4e2d<\/span><\/strong><\/span><\/p><\/div>

\u30ab\u30c3\u30d1\u4fc2\u6570 3 \u4eba\u4ee5\u4e0a\u306e\u5834\u5408 Fleiss\u8ad6\u6587\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u8a08\u7b97\u3057\u3066\u307f\u308b<\/h2>\n\n\n\n

\u7d71\u8a08\u30bd\u30d5\u30c8R\u306eirr\u30d1\u30c3\u30b1\u30fc\u30b8\u306bdiagnoses\u3068\u3044\u3046\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308b\u3002<\/p>\n\n\n\n

\u3053\u308c\u306fFleiss’ kappa\u3092\u767a\u660e\u3057\u305f\u6642\u306e\u30c7\u30fc\u30bf\u3067\u3042\u308b\u3002<\/p>\n\n\n\n

\u3053\u308c\u3092\u4f7f\u3046\u3053\u3068\u306b\u3059\u308b\u3002<\/p>\n\n\n\n

\u307e\u305a\u3001irr \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002<\/p>\n\n\n\n

\u4e00\u56de\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308c\u3070\u3001\u305d\u306e\u6b21\u304b\u3089\u306f\u3044\u3089\u306a\u3044\u3002<\/p>\n\n\n\n

install.packages<\/span>(<\/span>\"irr\"<\/span>)<\/span>\n<\/code><\/pre>\n\n\n\n
library()\u3067\u547c\u3073\u51fa\u3057\u3001data()\u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3048\u308b\u3088\u3046\u306b\u3059\u308b\u3002\u3053\u308c\u3067\u6e96\u5099\u5b8c\u4e86\u3002<\/p>\n\n\n\n
library<\/span>(<\/span>irr)<\/span>\ndata<\/span>(<\/span>diagnoses)<\/span>\n<\/code><\/pre>\n\n\n\n
diagnoses\u306e\u69cb\u9020\u3092str()\u3067\u898b\u3066\u307f\u308b\u3068\u30016\u4eba\u306e\u8a55\u4fa1\u8005\u306e30\u4eba\u306e\u60a3\u8005\u3055\u3093\u306b\u5bfe\u3059\u308b\u8a55\u4fa1\u304c\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308b\u3002<\/p>\n\n\n\n
><\/span> str<\/span>(<\/span>diagnoses)<\/span>\n'data.frame'<\/span>:<\/span>   30<\/span> obs. of  6<\/span> variables:<\/span>\n$<\/span> rater1:<\/span> Factor w\/<\/span> 5<\/span> levels \"1. Depression\"<\/span>,<\/span>..:<\/span> 4<\/span> 2<\/span> 2<\/span> 5<\/span> 2<\/span> 1<\/span> 3<\/span> 1<\/span> 1<\/span> 5<\/span> ...\n$<\/span> rater2:<\/span> Factor w\/<\/span> 5<\/span> levels \"1. Depression\"<\/span>,<\/span>..:<\/span> 4<\/span> 2<\/span> 3<\/span> 5<\/span> 2<\/span> 1<\/span> 3<\/span> 1<\/span> 1<\/span> 5<\/span> ...\n$<\/span> rater3:<\/span> Factor w\/<\/span> 5<\/span> levels \"1. Depression\"<\/span>,<\/span>..:<\/span> 4<\/span> 2<\/span> 3<\/span> 5<\/span> 2<\/span> 3<\/span> 3<\/span> 3<\/span> 4<\/span> 5<\/span> ...\n$<\/span> rater4:<\/span> Factor w\/<\/span> 5<\/span> levels \"1. Depression\"<\/span>,<\/span>..:<\/span> 4<\/span> 5<\/span> 3<\/span> 5<\/span> 4<\/span> 3<\/span> 3<\/span> 3<\/span> 4<\/span> 5<\/span> ...\n$<\/span> rater5:<\/span> Factor w\/<\/span> 5<\/span> levels \"1. Depression\"<\/span>,<\/span>..:<\/span> 4<\/span> 5<\/span> 3<\/span> 5<\/span> 4<\/span> 3<\/span> 5<\/span> 3<\/span> 4<\/span> 5<\/span> ...\n$<\/span> rater6:<\/span> Factor w\/<\/span> 4<\/span> levels \"2. Personality Disorder\"<\/span>,<\/span>..:<\/span> 3<\/span> 4<\/span> 4<\/span> 4<\/span> 3<\/span> 2<\/span> 4<\/span> 3<\/span> 3<\/span> 4<\/span> ...\n<\/code><\/pre>\n\n\n\n
\u30c7\u30fc\u30bf\u306e\u69cb\u9020\u3092\u5909\u3048\u3066\u300130\u4eba\u5206\u306e id\u30016\u4eba\u306e\u8a55\u4fa1\u8005 rater\u3001\u305d\u308c\u305e\u308c\u306e\u8a55\u4fa1 rating1 \u3068\u3044\u3046\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3059\u308b\u3002<\/p>\n\n\n\n
id <- rep(1:30,6)\nrater <- sort(rep(1:6,30))\nrating <- matrix(rep(0,180),nc=6)\nrating\nfor (i in 1:6){\n  rating[,i] <- as.numeric(strtrim(diagnoses[,i],1))\n}\nrating\nrating1 <- c(rating[,1],rating[,2],rating[,3],rating[,4],rating[,5],rating[,6])\nrating1\ndat <- data.frame(id,rater,rating1)\nhead(dat)\n<\/code><\/pre>\n\n\n\n
\u51fa\u6765\u4e0a\u304c\u308a\u306e dat \u306e\u5148\u982d 10 \u884c\u3092\u898b\u3066\u307f\u308b\u3068\u3001\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3002<\/p>\n\n\n\n
\u5168\u90e8\u3067 180 \u884c\u306e\u30c7\u30fc\u30bf\u306b\u306a\u308b\u3002<\/p>\n\n\n\n
> head(dat)\n  id rater rating1\n1  1     1       4\n2  2     1       2\n3  3     1       2\n4  4     1       5\n5  5     1       2\n6  6     1       1<\/code><\/pre>\n\n\n\n
180\u884c\u306e\u30c7\u30fc\u30bf\u3092\u3001\u60a3\u8005\u3055\u3093 x \u8a55\u4fa1\u30ab\u30c6\u30b4\u30ea\u306e30 x 6 \u306e\u5206\u5272\u8868\u306b\u3059\u308b\u3002<\/p>\n\n\n\n
> (tab <- table(id,rating1))\n    rating1\nid   1 2 3 4 5\n  1  0 0 0 6 0\n  2  0 3 0 0 3\n  3  0 1 4 0 1\n  4  0 0 0 0 6\n  5  0 3 0 3 0\n  6  2 0 4 0 0\n  7  0 0 4 0 2\n  8  2 0 3 1 0\n  9  2 0 0 4 0\n  10 0 0 0 0 6\n  11 1 0 0 5 0\n  12 1 1 0 4 0\n  13 0 3 3 0 0\n  14 1 0 0 5 0\n  15 0 2 0 3 1\n  16 0 0 5 0 1\n  17 3 0 0 1 2\n  18 5 1 0 0 0\n  19 0 2 0 4 0\n  20 1 0 2 0 3\n  21 0 0 0 0 6\n  22 0 1 0 5 0\n  23 0 2 0 1 3\n  24 2 0 0 4 0\n  25 1 0 0 4 1\n  26 0 5 0 1 0\n  27 4 0 0 0 2\n  28 0 2 0 4 0\n  29 1 0 5 0 0\n  30 0 0 0 0 6<\/code><\/pre>\n\n\n\n
\u3053\u306e\u5206\u5272\u8868\u3092\u4f7f\u3063\u3066\u3001$ \\bar{P} $ \u3068 $ \\bar{P_e} $ \u3092\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n
N <- 30\nn <- 6\nk <- 5\npj <- rep(0,k)\npj\nfor (j in 1:k){\n  pj[j] <- 1\/(N*n)*sum(tab[,j])\n}\npj\nPi <- rep(0,N)\nPi\nPij <- rep(0,k)\nPij\nfor (i in 1:N){\n  for (j in 1:k){\n    Pij[j] <- tab[i,j]*(tab[i,j]-1)\n  }\n  Pi[i] <- 1\/(n*(n-1))*sum(Pij)\n}\nPi\n(P.bar <- sum(Pi)\/N)\n(Pe.bar <- sum(pj^2))\n<\/code><\/pre>\n\n\n\n
\u7d50\u679c\u306f\u3001\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n\n\n\n
> (P.bar <- sum(Pi)\/N)\n[1] 0.5555556\n> (Pe.bar <- sum(pj^2))\n[1] 0.2199383<\/code><\/pre>\n\n\n\n
\u3088\u3063\u3066\u3001$ \\kappa $ \u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b0.430\u3068\u8a08\u7b97\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
><\/span> (<\/span>fleiss.kappa <-<\/span> (<\/span>P.bar -<\/span> Pe.bar)<\/span>\/<\/span>(<\/span>1<\/span> -<\/span> Pe.bar))<\/span>\n[<\/span>1<\/span>]<\/span> 0.4302445<\/span>\n<\/code><\/pre>\n\n\n\n
\u30ab\u30c3\u30d1\u4fc2\u6570 3 \u4eba\u4ee5\u4e0a\u306e\u5834\u5408 irr \u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u95a2\u6570\u3092\u4f7f\u3046\u5834\u5408<\/h2>\n\n\n\n
\u3053\u306e\u5834\u5408\u306f\u3068\u3066\u3082\u7c21\u5358\u306b\u8a08\u7b97\u3067\u304d\u308b\u3002<\/p>\n\n\n\n
diagnoses\u306e\u5f62\u5f0f\uff08\u8a55\u4fa1\u8005\u3054\u3068\u306e\u88ab\u9a13\u8005\u306e\u8a55\u4fa1\u304c\u5217\u306b\u306a\u3063\u305f\u5f62\u5f0f\uff09\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092kappam.fleiss()\u306b\u6295\u5165\u3059\u308b\u3060\u3051\u3067\u8a08\u7b97\u7d50\u679c\u304c\u51fa\u529b\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
diagnoses\u306e\u5f62\u5f0f\u3092\u3082\u3046\u5c11\u3057\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u5148\u982d\u3068\u6700\u5f8c\u306e\u90e8\u5206\u3092\u8868\u793a\u3057\u3066\u307f\u308b\u3068\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n\n\n\n
\u5148\u982d 10 \u884c<\/p>\n\n\n\n
> head(diagnoses)\n                   rater1                  rater2                  rater3           rater4           rater5           rater6\n1             4. Neurosis             4. Neurosis             4. Neurosis      4. Neurosis      4. Neurosis      4. Neurosis\n2 2. Personality Disorder 2. Personality Disorder 2. Personality Disorder         5. Other         5. Other         5. Other\n3 2. Personality Disorder        3. Schizophrenia        3. Schizophrenia 3. Schizophrenia 3. Schizophrenia         5. Other\n4                5. Other                5. Other                5. Other         5. Other         5. Other         5. Other\n5 2. Personality Disorder 2. Personality Disorder 2. Personality Disorder      4. Neurosis      4. Neurosis      4. Neurosis\n6           1. Depression           1. Depression        3. Schizophrenia 3. Schizophrenia 3. Schizophrenia 3. Schizophrenia<\/code><\/pre>\n\n\n\n
\u6700\u5f8c 10 \u884c<\/p>\n\n\n\n
> tail(diagnoses)\n                    rater1                  rater2                  rater3                  rater4                  rater5           rater6\n25           1. Depression             4. Neurosis             4. Neurosis             4. Neurosis             4. Neurosis         5. Other\n26 2. Personality Disorder 2. Personality Disorder 2. Personality Disorder 2. Personality Disorder 2. Personality Disorder      4. Neurosis\n27           1. Depression           1. Depression           1. Depression           1. Depression                5. Other         5. Other\n28 2. Personality Disorder 2. Personality Disorder             4. Neurosis             4. Neurosis             4. Neurosis      4. Neurosis\n29           1. Depression        3. Schizophrenia        3. Schizophrenia        3. Schizophrenia        3. Schizophrenia 3. Schizophrenia\n30                5. Other                5. Other                5. Other                5. Other                5. Other         5. Other<\/code><\/pre>\n\n\n\n
Fleiss’ kappa\u3092\u8a08\u7b97\u3059\u308b\u30680.43\u3068\u8868\u793a\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
> kappam.fleiss(diagnoses)\n Fleiss' Kappa for m Raters\n\n Subjects = 30 \n   Raters = 6 \n    Kappa = 0.43 \n\n        z = 17.7 \n  p-value = 0 <\/code><\/pre>\n\n\n\n
\u30ab\u30c3\u30d1\u4fc2\u6570 3 \u4eba\u4ee5\u4e0a\u306e\u5834\u5408 \u30d5\u30e9\u30a4\u30b9\u306e\u30ab\u30c3\u30d1\u4fc2\u6570\u306e\u89e3\u91c8<\/h2>\n\n\n\n
\u4f8b\u3048\u3070\u3001Landis and Koch (1977)<\/a> \u306b\u793a\u3055\u308c\u305f\u57fa\u6e96\u306b\u3088\u3063\u3066\u89e3\u91c8\u3059\u308b\u3002<\/p>\n\n\n\n
$ \\kappa $<\/th>Interpretation<\/th><\/tr><\/thead>
< 0<\/td>Poor<\/td><\/tr>
0.01 \u2013 0.20<\/td>Slight<\/td><\/tr>
0.21 \u2013 0.40<\/td>Fair<\/td><\/tr>
0.41 \u2013 0.60<\/td>Moderate<\/td><\/tr>
0.61 \u2013 0.80<\/td>Substantial<\/td><\/tr>
0.81 \u2013 1.00<\/td>Almost perfect<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
\u4e09\u4eba\u4ee5\u4e0a\u306e\u8a55\u4fa1\u8005\u306b\u3088\u308b\u8a55\u4fa1\u306e\u4e00\u81f4\u5ea6\u3092\u6e2c\u308b\u4fc2\u6570\u306f\u30d5\u30e9\u30a4\u30b9\u306e\u30ab\u30c3\u30d1\u4fc2\u6570 Fleiss’ kappa \u3068\u547c\u3070\u308c\u308b\u6570\u5024\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
\u7d71\u8a08\u30bd\u30d5\u30c8R\u306eirr \u30d1\u30c3\u30b1\u30fc\u30b8\u306b\u542b\u307e\u308c\u308bkappam.fleiss()\u3092\u4f7f\u3048\u3070\u7c21\u5358\u306b\u8a08\u7b97\u3067\u304d\u308b\u3002<\/p>\n\n\n\n
\u7d50\u679c\u306fLandis and Koch (1977)<\/a> \u3067\u793a\u3055\u308c\u3066\u3044\u308b\u3088\u3046\u306a\u6163\u4f8b\u7684\u306a\u57fa\u6e96\u3092\u7528\u3044\u3066\u89e3\u91c8\u3059\u308b\u3002<\/p>\n\n\n\n
\u53c2\u8003\u30b5\u30a4\u30c8<\/h2>\n\n\n\n
https:\/\/en.wikipedia.org\/wiki\/Fleiss%27_kappa#Interpretation<\/a><\/p>\n\n\n\n\n\n\n\n
\u53c2\u8003\u6587\u732e<\/h2>\n\n\n\n
Landis and Koch. The Measurement of Observer Agreement for Categorical Data
<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u30d5\u30e9\u30a4\u30b9\uff08Fleiss\uff09\u306e\u30ab\u30c3\u30d1\u4fc2\u6570\uff08kappa\uff09\u306f\u30013\u4eba\u4ee5\u4e0a\u306e\u8a55\u4fa1\u8005\u306e\u8a55\u4fa1\u304c\u4e00\u81f4\u3057\u3066\u3044\u308b\u5ea6\u5408\u3044\u3092\u6e2c\u5b9a\u3059\u308b\u4fc2\u6570\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"swell_btn_cv_data":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[5,128],"tags":[],"class_list":["post-441","post","type-post","status-publish","format-standard","hentry","category-r","category-128"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/441","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/comments?post=441"}],"version-history":[{"count":3,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/441\/revisions"}],"predecessor-version":[{"id":3388,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/441\/revisions\/3388"}],"wp:attachment":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media?parent=441"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/categories?post=441"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/tags?post=441"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}