samplesize \u30d1\u30c3\u30b1\u30fc\u30b8\u306en.wilcox.ord()\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n
\u30de\u30f3\u30db\u30a4\u30c3\u30c8\u30cb\u30fc\u306e U \u691c\u5b9a\u306f\u3001\u30a6\u30a3\u30eb\u30b3\u30af\u30bd\u30f3\u306e\u9806\u4f4d\u548c\u691c\u5b9a\u3068\u6570\u5b66\u7684\u306b\u540c\u3058\u306a\u305f\u3081\u3001\u30a6\u30a3\u30eb\u30b3\u30af\u30bd\u30f3\u30fb\u30de\u30f3\u30fb\u30db\u30a4\u30c3\u30c8\u30cb\u30fc\u691c\u5b9a\u3068\u3082\u8a00\u308f\u308c\u308b\u305f\u3081\u3001n.wilcox.ord() \u3068\u3044\u3046\u540d\u524d\u306e\u95a2\u6570\u306b\u306a\u3063\u3066\u3044\u308b\u3002<\/p>\n\n\n\n
\u3053\u308c\u306f\u3001\u6b63\u898f\u8fd1\u4f3c\u3092\u7528\u3044\u305f\u7c21\u6613\u7684\u306a\u65b9\u6cd5\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
samplesize\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u3001library()\u3067\u547c\u3073\u51fa\u3059\u3002<\/p>\n\n\n\n
install.packages<\/span>(<\/span>\"samplesize\"<\/span>)<\/span>\nlibrary<\/span>(<\/span>samplesize)<\/span>\n<\/code><\/pre>\n\n\n\n\u691c\u51fa\u529b80%\u3001\u6709\u610f\u6c34\u6e965%\u3001\u4e21\u7fa4\u540c\u6570\u30671:1\uff08t=0.5)\u3001\u30b0\u30eb\u30fc\u30d71\u306f33\uff05\u300133%\u300134\uff05\u3068\u4e09\u5206\u5272\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u304c\u540c\u6570\u306b\u5bfe\u3057\u3001\u30b0\u30eb\u30fc\u30d72\u306f\u300166%\u300120\uff05\u300114\uff05\u3060\u3068\u3059\u308b\u3068\u3001\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u306f36\u4f8b\u305a\u3064\u5fc5\u8981\u3068\u8a08\u7b97\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
> n.wilcox.ord(power=0.8, alpha=0.05, t=0.5, \n+ p=c(0.33, 0.33, 0.34),\n+ q=c(0.66, 0.20, 0.14))\n$`total sample size`\n[1] 72\n\n$m\n[1] 36\n\n$n\n[1] 36<\/code><\/pre>\n\n\n\n\u30de\u30f3\u30db\u30a4\u30c3\u30c8\u30cb\u30fc\u306e U \u691c\u5b9a \u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u8a08\u7b97 \u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u4e2d\u8eab\u3092\u898b\u3066\u307f\u308b<\/h2>\n\n\n\n
n.wilcox.ord() \u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u4e2d\u306e\u4e3b\u8981\u306a\u90e8\u5206\u3092\u629c\u304d\u51fa\u3057\u3066\u307f\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308b\u3002<\/p>\n\n\n\n
\u8a08\u7b97\u7d50\u679c\u304c\u898b\u3084\u3059\u3044\u3088\u3046\u306b\u6539\u9020\u3057\u3066\u3042\u308b\u3002<\/p>\n\n\n\n
n.wilcox.ord.abs <- function(power=0.8, sig.level=0.05,\n t, p, q, alternative=c(\"two.sided\",\"one.sided\")){\n alternative <- match.arg(alternative)\n side <- switch(alternative, one.sided=1, two.sided=2)\n Za <- qnorm(sig.level\/side, lower.tail=FALSE)\n Zb <- qnorm(power)\n pq1 <- function(p,q){\n D <- length(p)\n PQ1 <- 0\n for (i in 2:D){\n PQ1 <- PQ1 + p[i] * sum(q[1:(i-1)])\n }\n return(PQ1)\n }\n p.t <- (1-t)*p\n q.t <- t*q\n pq.t <- p.t+q.t\n pq.t.3 <- pq.t^3\n t.sum <- sum(pq.t.3)\n pq <- cbind(p,q)\n pq.sum <- sum(apply(pq,1,prod))\n N <- (((Za+Zb)^2)*(1-t.sum))\/(12*t*(1-t)*(pq1(p=p,q=q)+0.5*pq.sum-0.5)^2)\n samplesize <- ceiling(N)\n m <- round(ceiling(N)*(1-t),0)\n n <- round(ceiling(N)*t,0)\n NOTE <- \"m or n means sample size for each group\"\n METHOD <- \"Wilcoxon-Mann-Whitney test sample size\"\n structure(list(`total sample size` = samplesize, m=m, n=n,\n t=t, p=p, q=q, sig.level=sig.level, power=power,\n alternative=alternative, note=NOTE, method=METHOD),\n class=\"power.htest\")\n}\n<\/code><\/pre>\n\n\n\n\u540c\u3058\u8a2d\u5b9a\u3067\u8a08\u7b97\u3057\u3066\u307f\u308b\u3068\u3001\u4e00\u7fa436\u4f8b\u3068\u8a08\u7b97\u3055\u308c\u3066\u3001\u540c\u4e00\u306e\u7d50\u679c\u306b\u306a\u308b\u3002<\/p>\n\n\n\n
> n.wilcox.ord.abs(t=0.5, p=c(0.33, 0.33, 0.34), q=c(0.66, 0.20, 0.14))\n\n Wilcoxon-Mann-Whitney test sample size \n\ntotal sample size = 72\n m = 36\n n = 36\n t = 0.5\n p = 0.33, 0.33, 0.34\n q = 0.66, 0.20, 0.14\n sig.level = 0.05\n power = 0.8\n alternative = two.sided\n\nNOTE: m or n means sample size for each group<\/code><\/pre>\n\n\n\n\u7247\u7fa4\u3092\u5c0f\u3055\u304f\u3057\u30662\uff1a1\u306e\u5272\u308a\u4ed8\u3051\uff08t=0.33\uff09\u306b\u3059\u308b\u3068\u300156\u4f8b\u306828\u4f8b\u3068\u8a08\u7b97\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
> n.wilcox.ord.abs(t=0.33, p=c(0.33, 0.33, 0.34), q=c(0.66, 0.20, 0.14))\n\n Wilcoxon-Mann-Whitney test sample size \n\ntotal sample size = 84\n m = 56\n n = 28\n t = 0.33\n p = 0.33, 0.33, 0.34\n q = 0.66, 0.20, 0.14\n sig.level = 0.05\n power = 0.8\n alternative = two.sided\n\nNOTE: m or n means sample size for each group<\/code><\/pre>\n\n\n\n\u7247\u5074\u691c\u5b9a\u306b\u3082\u5bfe\u5fdc\u3067\u304d\u308b\u3088\u3046\u306b\u6539\u9020\u3057\u305f\u3002<\/p>\n\n\n\n
\u4e21\u7fa4\u540c\u3058\u4f8b\u6570\u3067\u7247\u5074\u691c\u5b9a\u306e\u5834\u5408\u300128\u4f8b\u305a\u3064\u3068\u8a08\u7b97\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
> n.wilcox.ord.abs(t=0.5, p=c(0.33, 0.33, 0.34), q=c(0.66, 0.20, 0.14), alternative=\"one.sided\")\n\n Wilcoxon-Mann-Whitney test sample size \n\ntotal sample size = 57\n m = 28\n n = 28\n t = 0.5\n p = 0.33, 0.33, 0.34\n q = 0.66, 0.20, 0.14\n sig.level = 0.05\n power = 0.8\n alternative = one.sided\n\nNOTE: m or n means sample size for each group<\/code><\/pre>\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![\"\"](\"https:\/\/best-biostatistics.com\/wp\/wp-content\/uploads\/2023\/11\/bn_r_03.png\")
<\/a>\r\n\u21911\u4e07\u4eba\u4ee5\u4e0a\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u8cfc\u8aad\u4e2d<\/span><\/strong><\/span><\/p><\/div>\u30de\u30f3\u30db\u30a4\u30c3\u30c8\u30cb\u30fc\u306e U \u691c\u5b9a\u306e\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u8a08\u7b97\u3092\u30a8\u30af\u30bb\u30eb\u3067<\/h2>\n\n\n\n
\u30a8\u30af\u30bb\u30eb\u30b7\u30fc\u30c8\u3067\u30de\u30f3\u30db\u30a4\u30c3\u30c8\u30cb\u30fc\u306e U \u691c\u5b9a\u306e\u305f\u3081\u306e\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u8a08\u7b97\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u3057\u305f\u3002<\/p>\n\n\n\n
\u3088\u3051\u308c\u3070\u305c\u3072\u3002<\/p>\n\n\n\n