{"id":1989,"date":"2019-09-19T13:00:56","date_gmt":"2019-09-19T04:00:56","guid":{"rendered":"http:\/\/141.164.34.82\/?p=1989"},"modified":"2019-09-19T21:18:58","modified_gmt":"2019-09-19T12:18:58","slug":"%ed%94%bc%ec%b2%98-%ec%97%94%ec%a7%80%eb%8b%88%ec%96%b4%eb%a7%81-3-%eb%b2%a0%ec%9d%b4%ec%a7%80%ec%95%88","status":"publish","type":"post","link":"http:\/\/ds.sumeun.org\/?p=1989","title":{"rendered":"\ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 3: \ubca0\uc774\uc9c0\uc548"},"content":{"rendered":"<p>\uc9c0\ub09c \ubc88\uc758 \ubb38\uc81c\ub97c \ub2e4\uc2dc \ud55c \ubc88 \ubcf4\uc790.<\/p>\n<p>\ub2e4\uc74c\uc758 R \ucf54\ub4dc\ub294 \ub370\uc774\ud130 <code>dat<\/code>\ub97c \uc0dd\uc131\ud55c\ub2e4. \uc774\ub54c \uc124\uba85\ubcc0\uc218\ub294 <code>x1<\/code>\uc5d0\uc11c <code>x10<\/code>\uc740 \uc0c1\uad00\uad00\uacc4 0.1\uc778 \ud45c\uc900\uc815\uaddc\ubd84\ud3ec\ub97c \ub530\ub978\ub2e4. \uc2e4\uc81c \ub370\uc774\ud130\uc0dd\uc131\ubaa8\ud615\uc740 \ub2e4\uc74c\uacfc \uac19\ub2e4.<\/p>\n<p>\\[y = 3 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_{10} x_{10} + e,\\ \\ e \\sim \\mathcal{N}(0,1)\\]<\/p>\n<p>\uc815\ud655\ud55c \ubaa8\ud615\uc744 \uc54c\uace0 \uc788\ub2e4\uba74 \uc790\ub8cc\ub97c \ubaa8\ud615\uc5d0 \uc801\ud569\ud558\uc5ec \ubaa8\uc218( \\(\\beta_1, \\beta_2, \\cdots\\) )\uc744 \ucd94\uc815\ud558\uba74 \ub41c\ub2e4. \ud558\uc9c0\ub9cc \ubb38\uc81c\uac00 \uc788\ub2e4. <strong>\ubaa8\uc218\uc758 \uac2f\uc218<\/strong>\uac00 \ub298\uc5b4\ub0a0 \uc218\ub85d \ubaa8\uc218\ub97c \uc815\ud655\ud558\uac8c \ucd94\uc815\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c <strong>\ud45c\ubcf8\uc758 \ud06c\uae30<\/strong>\ub3c4 \uc99d\uac00\ud55c\ub2e4. \uc544\ub798\uc5d0\uc11c  \ud45c\ubcf8\uc758 \ud06c\uae30(<code>N<\/code>)\ub294 40\uc774\ub2e4.<\/p>\n<pre><code class=\"r\">set.seed(102)\r\nlibrary(data.table)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\nN &lt;- 40\r\nN2 &lt;- 2*N; \r\nncolMatX &lt;- 11\r\n\r\nsigma.cor = 0.1\r\n\r\nlibrary(mvtnorm)\r\nsigma &lt;- matrix(\r\n  sigma.cor,\r\n  ncolMatX, ncolMatX)\r\ndiag(sigma) = 1\r\n\r\nmatX &lt;- rmvnorm(N2, mean=rep(0,ncolMatX), sigma=sigma)\r\ncolnames(matX) &lt;- c(paste0(&quot;x&quot;, 1:10), &quot;e&quot;)\r\nX &lt;- data.table(matX)\r\n\r\nparamTrue &lt;- c(3, rnorm(5,1,0.2),\r\n               rnorm(5,-1.4,0.2))\r\nnames(paramTrue) &lt;- paste0(&quot;beta&quot;, 0:10)\r\n\r\nlv &lt;- X[, .(f1 = paramTrue[&#39;beta1&#39;]*x1 + \r\n              paramTrue[&#39;beta2&#39;]*x2 + \r\n              paramTrue[&#39;beta3&#39;]*x3 + \r\n              paramTrue[&#39;beta4&#39;]*x4 + \r\n              paramTrue[&#39;beta5&#39;]*x5,\r\n            f2 = paramTrue[&#39;beta6&#39;]*x6 + \r\n              paramTrue[&#39;beta7&#39;]*x7 + \r\n              paramTrue[&#39;beta8&#39;]*x8 + \r\n              paramTrue[&#39;beta9&#39;]*x9 + \r\n              paramTrue[&#39;beta10&#39;]*x10)]\r\n\r\ny &lt;- 3 + lv$f1 + lv$f2 + X$e\r\n\r\ndfParamTrue &lt;- data.frame(key=factor(names(paramTrue), \r\n                                     levels= paste0(&quot;beta&quot;, 0:10)),\r\n                          value=paramTrue)\r\n\r\ndatAll &lt;- data.table(X,y)\r\ndat &lt;- datAll[1:N]\r\nnewdat &lt;- datAll[(N+1):N2]\r\n<\/code><\/pre>\n<p>\uc55e\uc758 \uae00\uc5d0\uc11c \uc6b0\ub9ac\ub294 \\(\\beta_1, \\beta_2, \\beta_3, \\beta_4, \\beta_5\\) \uac00 \ubaa8\ub450 \ube44\uc2b7\ud558\uace0, \\(\\beta_6, \\cdots, \\beta_{10}\\) \uc774 \ubaa8\ub450 \ube44\uc2b7\ud558\ub2e4\ub294 \uc0ac\uc804 \uc9c0\uc2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ubcc0\uc218 \\(f_1 = \\beta_1 + \\cdots + \\beta_5\\) \uc640 \\(f_2=\\beta_6 + \\cdots +\\beta_{10}\\) \uc744 \ub9cc\ub4e4\uc5c8\ub2e4.<\/p>\n<p>\ud558\uc9c0\ub9cc \uc774 \ubc29\ubc95\uc740 \\(\\beta_1 = \\beta_2 = \\cdots = \\beta_5\\) \ub77c\ub294 \uac00\uc815\uacfc \\(\\beta_6 = \\cdots = \\beta_{10}\\) \uc774\ub77c\ub294 \uac00\uc815\uc774 \ubaa8\ub450 \uc131\ub9bd\ud560 \ub54c \uac00\uc7a5 \uc88b\uc740 \uc131\ub2a5\uc744 \ubcf4\uc77c \uac83\uc774\ub2e4. \uadf8\ub9ac\uace0 \uadf8 \uac00\uc815\uc5d0\uc11c \uc5b4\uae0b\ub098\ub294 \uc815\ub3c4\uc5d0 \ub530\ub77c \\(f_1\\) , \\(f_2\\) \ub9cc\uc758 \uc120\ud615\ubaa8\ud615\uc740 \uc131\ub2a5\uc774 \uc800\ud558\ub420 \uac83\uc774\ub2e4.<\/p>\n<p>\ub9cc\uc57d \\(\\beta_1 = \\beta_2 = \\cdots = \\beta_5\\) \uacfc \\(\\beta_6 = \\cdots = \\beta_{10}\\) \uc758 \uac15\ud55c \uac00\uc815\uc744 \uc870\uae08 \ub204\uadf8\ub7ec\ud2b8\ub9b0\ub2e4\uba74 \uc5b4\ub5a8\uae4c?<\/p>\n<p>\ub2e4\uc74c\uc758 \ube14\ub85c\uadf8\ub294 <a href=\"https:\/\/twiecki.io\/blog\/2014\/03\/17\/bayesian-glms-3\/\"><strong>The Best Of Both Worlds<\/strong><\/a>\ub97c \uc598\uae30\ud558\uace0 \uc788\ub2e4. \ub2e4\uc2dc \ub9d0\ud574 \ubaa8\ub4e0 \ubca0\ud0c0\ub97c \uc790\uc720\ub86d\uac8c \ub450\ub294 \ubc29\ubc95\uacfc \ud55c \uadf8\ub8f9\uc5d0 \uc18d\ud558\ub294 \ubaa8\ub4e0 \ubca0\ud0c0\ub97c \ub3d9\uc77c\ud558\uac8c \uc124\uc815\ud558\ub294 \ub450 \uac00\uc9c0 \ubc29\ubc95\uc758 \uc7a5\uc810\uc744 \ubaa8\ub450 \ucde8\ud558\ub294 \ubc29\ubc95\uc774 \uc874\uc7ac\ud55c\ub2e4!<\/p>\n<h2>\ubca0\uc774\uc9c0\uc548 \ubc29\ubc95<\/h2>\n<p>\ub2e4\uc74c\uc758 \ubca0\uc774\uc9c0\uc548 \ubaa8\ud615\uc740 \\(\\beta_1, \\beta_2, \\beta_3, \\beta_4, \\beta_5\\) \uac00 \ubaa8\ub450 \uc11c\ub85c \ube44\uc2b7\ud558\uace0, \\(\\beta_6, \\cdots, \\beta_{10}\\) \uc774 \ubaa8\ub450 \uc11c\ub85c \ube44\uc2b7\ud558\ub2e4\ub294 \uc0ac\uc804 \uc9c0\uc2dd\uc744 \ud65c\uc6a9\ud558\ub294 \ubaa8\ud615\uc774\ub2e4.<\/p>\n<p>\\[y = 3 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_{10} x_{10} + e,\\ \\ e \\sim \\mathcal{N}(0,1)\\]<\/p>\n<ul>\n<li>\uc0ac\uc804 \ubd84\ud3ec\n<ul>\n<li>\\(\\beta_1, \\beta_2, \\cdots, \\beta_5 \\sim \\mathcal{N}(\\mu_1, \\sigma^2_1)\\)<\/li>\n<li>\\(\\beta_6, \\beta_7, \\cdots, \\beta_{10} \\sim \\mathcal{N}(\\mu_2, \\sigma^2_2)\\)<\/li>\n<li>\\(\\mu_1, \\mu_2 \\sim \\textrm{Unif}(-100,100)\\)<\/li>\n<li>\\(\\sigma^2_1, \\sigma^2_2 \\sim \\textrm{Unif}(0, 100)\\)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\ub2e4\uc74c\uc758 \ucf54\ub4dc\ub294 \uc774\ub97c \uad6c\ud604\ud55c\ub2e4. (JAGS\ub85c MCMC \uc0d8\ud50c\ub9c1\uc744 \ud558\uae30 \uc804\uc5d0 \ud6a8\uc728\uc801\uc778 \uc0d8\ud50c\ub9c1\uc744 \uc704\ud574 \ud45c\uc900\ud654(<code>scale<\/code>)\ub97c \uc2e4\uc2dc\ud588\ub2e4.)<\/p>\n<pre><code class=\"r\">jags.model &lt;- &quot;\r\n\r\n\r\nmodel {\r\n  for (i in 1:N) {\r\n    ymean[i] &lt;- beta0 + beta1*x1[i] + beta2*x2[i] + beta3*x3[i] + beta4*x4[i] + beta5*x5[i] + \r\n            beta6*x6[i] + beta7*x7[i] + beta8*x8[i] + beta9*x9[i] + beta10*x10[i]\r\n    y[i] ~ dnorm(ymean[i], tau)\r\n  }\r\n\r\n  # priors\r\n  tau &lt;- pow(sigma2, -2)\r\n  sigma2 ~ dunif(0,100)\r\n  beta0 ~ dnorm(0, 0.01)\r\n  beta1 ~ dnorm(betaAmean, betaAtau)\r\n  beta2 ~ dnorm(betaAmean, betaAtau)\r\n  beta3 ~ dnorm(betaAmean, betaAtau)\r\n  beta4 ~ dnorm(betaAmean, betaAtau)\r\n  beta5 ~ dnorm(betaAmean, betaAtau)\r\n  beta6 ~ dnorm(betaBmean, betaBtau)\r\n  beta7 ~ dnorm(betaBmean, betaBtau)\r\n  beta8 ~ dnorm(betaBmean, betaBtau)\r\n  beta9 ~ dnorm(betaBmean, betaBtau)\r\n  beta10 ~ dnorm(betaBmean, betaBtau)\r\n  betaAmean ~ dunif(-100,100)\r\n  betaAtau &lt;- pow(betaAsigma2, -2)\r\n  betaBmean ~ dunif(-100,100)\r\n  betaBtau &lt;- pow(betaBsigma2, -2)\r\n  betaAsigma2 ~ dunif(0,100)\r\n  betaBsigma2 ~ dunif(0,100)\r\n}\r\n&quot;\r\n\r\ncat(jags.model, file=&#39;currentModel.j&#39;)\r\n#cat(jags.model2, file=&#39;currentModel.j&#39;)\r\n\r\nrequire(rjags)\r\n\r\ndat_ &lt;- (dat_mat &lt;- \r\n  dat %&gt;% select(-e) %&gt;% scale) %&gt;% data.frame\r\n\r\nN = nrow(dat_)\r\n\r\n\r\nmyj &lt;- jags.model(&#39;currentModel.j&#39;,\r\n                  data = c(as.list(dat_), &#39;N&#39;=N),\r\n                  n.chains = 3,\r\n                  quiet = TRUE)\r\n\r\nupdate(myj, n.iter=2000, progress.bar = &#39;none&#39;)\r\n\r\nn.thin=5\r\n\r\n\r\nmcmcsamp &lt;- \r\n  coda.samples(myj, \r\n               c(&#39;beta0&#39;, &#39;beta1&#39;, &#39;beta2&#39;, &#39;beta3&#39;, &#39;beta4&#39;, &#39;beta5&#39;,\r\n                 &#39;beta6&#39;, &#39;beta7&#39;, &#39;beta8&#39;, &#39;beta9&#39;, &#39;beta10&#39;,\r\n                 &#39;betaAmean&#39;, &#39;betaBmean&#39;, &#39;betaAsigma2&#39;, &#39;betaBsigma2&#39;, &#39;sigma2&#39;),\r\n               1000*n.thin, thin=n.thin,\r\n               progress.bar = &#39;none&#39;)  \r\n<\/code><\/pre>\n<p>\uc6b0\uc120 MCMC \uc0d8\ud50c\uc758 \uc218\ub834\uc131\uc744 \ud655\uc778\ud574\uc57c \ud55c\ub2e4. \ubcf4\ud1b5 Rhat\uc774 1.2\ubcf4\ub2e4 \uc791\uc73c\uba74 \uc218\ub834\ud55c \uac83\uc73c\ub85c \ubcf8\ub2e4.<\/p>\n<pre><code class=\"r\">#summary(mcmcsamp)\r\n#plot(mcmcsamp)\r\n\r\n#mcmcr::rhat(mcmcsamp)\r\n#mcmcr::rhat(mcmcsamp, by=&#39;parameter&#39;, as_df=TRUE)\r\n\r\nprint(MCMCvis::MCMCsummary(mcmcsamp, n.eff=TRUE)) # install.packages(&#39;MCMCvis&#39;)\r\n<\/code><\/pre>\n<pre>##                      mean         sd         2.5%           50%\r\n## beta0       -0.0008153451 0.04730933 -0.093582641  0.0000556605\r\n## beta1        0.3002368789 0.03549022  0.237489178  0.2981886062\r\n## beta10      -0.3662336392 0.04543191 -0.455571464 -0.3664033830\r\n## beta2        0.3097945925 0.03735937  0.243492538  0.3062498109\r\n## beta3        0.3044016981 0.03615626  0.235866801  0.3021763979\r\n## beta4        0.2887275983 0.03600711  0.214269698  0.2897465993\r\n## beta5        0.2616867426 0.04282079  0.163398803  0.2679280557\r\n## beta6       -0.2482596931 0.05724510 -0.359905930 -0.2478469606\r\n## beta7       -0.3653164949 0.04829268 -0.459222861 -0.3656849740\r\n## beta8       -0.4928264247 0.05339144 -0.599683460 -0.4940052212\r\n## beta9       -0.4181135511 0.04756135 -0.515210669 -0.4167642616\r\n## betaAmean    0.2925825249 0.03680493  0.223940666  0.2923181691\r\n## betaAsigma2  0.0522688007 0.05154199  0.001763328  0.0384909401\r\n## betaBmean   -0.3777158395 0.08670358 -0.540695126 -0.3791240688\r\n## betaBsigma2  0.1553488341 0.11108145  0.040603461  0.1263819110\r\n## sigma2       0.2924111077 0.03858603  0.227463442  0.2883110324\r\n##                   97.5% Rhat n.eff\r\n## beta0        0.09180819 1.00  3195\r\n## beta1        0.37752391 1.00  1932\r\n## beta10      -0.27775564 1.00  2894\r\n## beta2        0.39141868 1.00  1870\r\n## beta3        0.38345474 1.00  1857\r\n## beta4        0.36016644 1.00  2468\r\n## beta5        0.33151414 1.00  1547\r\n## beta6       -0.13748495 1.00  2113\r\n## beta7       -0.27138717 1.00  2753\r\n## beta8       -0.38564016 1.00  2669\r\n## beta9       -0.32821133 1.00  3512\r\n## betaAmean    0.36672810 1.01  2273\r\n## betaAsigma2  0.18400535 1.01   941\r\n## betaBmean   -0.20465755 1.00  3077\r\n## betaBsigma2  0.46000681 1.00  1265\r\n## sigma2       0.37912164 1.00  2892\r\n<\/pre>\n<pre><code class=\"r\">print(gelman.diag(mcmcsamp)) # coda::gelman.diag\r\n<\/code><\/pre>\n<pre>## Potential scale reduction factors:\r\n## \r\n##             Point est. Upper C.I.\r\n## beta0            1.000       1.00\r\n## beta1            1.001       1.00\r\n## beta10           0.999       1.00\r\n## beta2            1.000       1.00\r\n## beta3            1.001       1.00\r\n## beta4            1.000       1.00\r\n## beta5            1.000       1.00\r\n## beta6            1.000       1.00\r\n## beta7            1.003       1.01\r\n## beta8            1.002       1.01\r\n## beta9            1.000       1.00\r\n## betaAmean        1.005       1.01\r\n## betaAsigma2      1.007       1.01\r\n## betaBmean        1.003       1.00\r\n## betaBsigma2      1.001       1.00\r\n## sigma2           1.001       1.00\r\n## \r\n## Multivariate psrf\r\n## \r\n## 1.01\r\n<\/pre>\n<pre><code class=\"r\">#https:\/\/www.reddit.com\/r\/rstats\/comments\/884rlc\/how_to_calculate_gelmanrubin_convergence_from\/\r\n#https:\/\/avehtari.github.io\/rhat_ess\/rhat_ess.html\r\n<\/code><\/pre>\n<p>\uc774\uc81c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ud558\uc5ec \uc608\uce21 \uc131\ub2a5\uc744 \ube44\uad50\ud574\ubcf4\uc790. <\/p>\n<pre><code class=\"r\">newdat_ &lt;- sweep(newdat %&gt;% select(-y), 2, attr(dat_mat, &quot;scaled:center&quot;), &quot;-&quot;)\r\nnewdat_ &lt;- sweep(newdat_ , 2, attr(dat_mat, &quot;scaled:scale&quot;), &quot;\/&quot;)\r\n\r\n#mcmcsamp\r\n#str(mcmcsamp)\r\nbparm &lt;- rbind(mcmcsamp[[1]], mcmcsamp[[2]], mcmcsamp[[3]])[,paste0(&quot;beta&quot;, 0:10)]\r\nAbparm &lt;- array(bparm, c(nrow(bparm), ncol(bparm), nrow(newdat_))) # mcmcsamp, parameter, datasamp\r\n\r\nnewdat_[,&quot;x0&quot;] = 1\r\nnewdat_ &lt;- as.matrix(newdat_)\r\nnewdat_ &lt;- newdat_[,paste0(&quot;x&quot;,0:10)]\r\n\r\nAnewdat_ &lt;- array(newdat_, c(nrow(newdat_), ncol(newdat_), nrow(bparm))) # datasamp, parameter, mcmcsamp\r\nAnewdat_ &lt;- aperm(Anewdat_, c(3,2,1)) # mcmcsamp, parameter, datasamp\r\n\r\nnewpred_ &lt;- apply((Anewdat_*Abparm), c(1,3), sum) # mcmcsamp, datasamp\r\n\r\nnewpred &lt;- attr(dat_mat, &quot;scaled:scale&quot;)[&#39;y&#39;] * newpred_ + attr(dat_mat, &quot;scaled:center&quot;)[&#39;y&#39;] \r\n\r\nnewpredMean &lt;- apply(newpred, 2, mean)\r\n<\/code><\/pre>\n<pre><code class=\"r\">dat2All &lt;- X[, .(f1= x1+x2+x3+x4+x5, \r\n              f2= x6+x7+x8+x9+x10,\r\n              y = y)]\r\ndat2 &lt;- dat2All[1:N]\r\nnewdat2 &lt;- dat2All[(N+1):N2]\r\nfit &lt;- lm(y ~ . -e , dat)\r\nfit2 &lt;- lm(y ~ . , dat2)\r\nprint(mean((predict(fit, newdat)- newdat$y)^2))\r\n<\/code><\/pre>\n<pre>## [1] 1.868142\r\n<\/pre>\n<pre><code class=\"r\">print(mean((predict(fit2, newdat2)-newdat2$y)^2))\r\n<\/code><\/pre>\n<pre>## [1] 1.347253\r\n<\/pre>\n<pre><code class=\"r\">print(mean((newpredMean-newdat2$y)^2))\r\n<\/code><\/pre>\n<pre>## [1] 1.59107\r\n<\/pre>\n<p>\ub2e8 \ud55c\ubc88\uc758 \ubd84\uc11d\uacb0\uacfc\ub85c \ud655\uc2e0\ud560 \uc218 \uc5c6\uc9c0\ub9cc \ubca0\uc774\uc9c0\uc548 \ubd84\uc11d\uc740 \ub450 \uadf9\ub2e8(\ubaa8\ub450 \uacc4\uc218\ub97c \uc790\uc720\ub86d\uac8c \ub450\ub294 \ubaa8\ud615\uacfc \uadf8\ub8f9 \ub0b4\uc758 \ubaa8\ub4e0 \uacc4\uc218\uac00 \uac19\ub2e4\uace0 \ub450\ub294 \ubaa8\ud615)\uc758 <del>\uc911\uac04\ucbe4 \ub41c\ub2e4.<\/del> \uc544\ub2c8, \uc911\uac04 \ucbe4 \ub418\ub294 \ub4ef \ubcf4\uc778\ub2e4. (\ud558\uc9c0\ub9cc \uc704\uc758 \ucf54\ub4dc\ub97c \ub79c\ub364 \uc2dc\ub4dc(<code>set.seed<\/code>)\ub97c \ubc14\uafd4\uac00\uba70 \ub3cc\ub824\ubcf4\uba74 \ud560 \ub54c\ub9c8\ub2e4 \ub9e4\uc6b0 \ub2e4\ub978 \uacb0\uacfc\uac00 \ub098\uc634\uc744 \uc54c \uc218 \uc788\ub2e4. \ud3c9\uade0\uc801 \uacb0\uacfc\ub294 \uc544\ub798 \ubd80\ub85d\uc744 \ud655\uc778\ud558\uc790.)<\/p>\n<h2>\ubca0\uc774\uc9c0\uc548 \ucd94\uac00 \ubd84\uc11d<\/h2>\n<h3>\uacc4\uc218\uc758 \uc0ac\ud6c4 \ubd84\ud3ec<\/h3>\n<p>\uc810\uc120\uc740 1\uacfc -1.4. \ub3d9\uadf8\ub780 \uc810\uc740 \uc2e4\uc81c \ubaa8\uc218. \ubc14\uc774\uc62c\ub9b0 \ud50c\ub86f\uc740 \uacc4\uc218\uc758 \uc0ac\ud6c4 \ubd84\ud3ec(posterior distribution)<\/p>\n<pre><code class=\"r\">library(tidyr)\r\nsdy = attr(dat_mat, &quot;scaled:scale&quot;)[&#39;y&#39;]\r\nmeany = attr(dat_mat, &quot;scaled:center&quot;)[&#39;y&#39;]\r\n\r\nbeta0hat &lt;- meany + sdy*bparm[, &#39;beta0&#39;] -\r\n  apply(sdy*\r\n  sweep(sweep(bparm[, paste0(&quot;beta&quot;, 1:10)],2,attr(dat_mat, &quot;scaled:center&quot;)[paste0(&quot;x&quot;, 1:10)],&quot;*&quot;),\r\n        2,\r\n        attr(dat_mat, &quot;scaled:scale&quot;)[paste0(&quot;x&quot;, 1:10)],&quot;\/&quot;), 1, sum)\r\n\r\ndfBparmOrigScale &lt;- sweep(bparm[, paste0(&quot;beta&quot;, 1:10)]*sdy, 2, \r\n    attr(dat_mat, &quot;scaled:scale&quot;)[paste0(&quot;x&quot;, 1:10)], &quot;\/&quot;) %&gt;% as.data.frame \r\n\r\ndfBparmOrigScale$beta0 &lt;- beta0hat\r\n\r\ndfBparmOrigScale %&gt;%\r\n    gather(key=&#39;key&#39;, value=&#39;value&#39;, beta1:beta0) %&gt;% \r\n#dfBparmOrigScale %&gt;% #mutate(key=factor(key, levels=paste0(&quot;beta&quot;, 0:10))) %&gt;%\r\n  ggplot(aes(x=key, y=value)) + geom_violin(fill=&#39;gray80&#39;) + \r\n  geom_point(data=dfParamTrue, aes(x=key, y=value), size=2) +\r\n  scale_x_discrete(limits=paste0(&quot;beta&quot;, 0:10)) + \r\n  geom_hline(yintercept=1.1, linetype=&#39;dotted&#39;) + \r\n  geom_hline(yintercept=-1.4, linetype=&#39;dotted&#39;) + \r\n  theme_light() \r\n<\/code><\/pre>\n<p><img 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\" title=\"plot of chunk unnamed-chunk-6\" alt=\"plot of chunk unnamed-chunk-6\" width=\"80%\" \/><\/p>\n<h3>\uacc4\uc218\uc758 \uc0ac\ud6c4 \uacb0\ud569 \ubd84\ud3ec<\/h3>\n<pre><code class=\"r\">dfBparam &lt;- bparm[, paste0(&quot;beta&quot;, 1:10)] %&gt;% as.data.frame \r\n\r\ng2 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta2), alpha=0.01) + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng3 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta3), alpha=0.01) + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng4 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta4), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng5 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta5), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng6 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta6), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng7 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta7), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng8 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta8), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng9 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta9), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\ng10 &lt;- ggplot(dfBparam) + geom_point(aes(x=beta1, y=beta10), alpha=0.01)  + coord_cartesian(xlim=range(bparm[,&#39;beta1&#39;])) + coord_equal() + theme_light()\r\n\r\nlibrary(patchwork)\r\n(g2 + g3 + g4) \/\r\n  (g5 + g6 + g7) \/\r\n  (g8 + g9 + g10)\r\n<\/code><\/pre>\n<p><img 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\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\" title=\"plot of chunk unnamed-chunk-7\" alt=\"plot of chunk unnamed-chunk-7\" width=\"80%\" \/><\/p>\n<h2>\ub9c8\ubb34\ub9ac<\/h2>\n<p>\ub9cc\uc57d \uacc4\uc218\ub97c \ubaa8\ub450 \ud480\uc5b4\uc9c0\ub294 \ubaa8\ud615\uacfc \uadf8\ub8f9\ub0b4\uc758 \uacc4\uc218\ub97c \ubaa8\ub450 \uac19\uac8c \ub193\ub294 \ub450 \ubaa8\ud615\ub9cc\uc774 \uc874\uc7ac\ud55c\ub2e4\uba74, \uadf8 \ub458 \uc0ac\uc774\uc5d0 \ubb34\uc5c7\uc744 \uc120\ud0dd\ud560\uc9c0\uc5d0 \ub300\ud574 \uace0\ubbfc\ud560 \uc218 \ubc16\uc5d0 \uc5c6\ub2e4. \uc774\ub54c \uac00\uc7a5 \uc88b\uc740 \ubc29\ubc95\uc740 \uac01 \uc9d1\ub2e8 \ub0b4\uc758 \ubaa8\uc218\uac00 \ud3c9\uade0\uacfc \ucc28\uc774\uac00 \ub098\ub294 \uc815\ub3c4, \uadf8\ub9ac\uace0 \ud45c\ubcf8\ud06c\uae30\ub97c \uace0\ub824\ud558\uc5ec \uac00\uc7a5 \uc88b\uc740 \ubaa8\ud615\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774\ub2e4.<\/p>\n<p>\uc774\uc5d0 \ub300\ud55c \uc801\uc808\ud55c \uc815\ubcf4\uac00 \uc5c6\ub2e4\uba74 \ubca0\uc774\uc9c0\uc548\uc73c\ub85c \ubd84\uc11d\ud558\ub294 \uac83\uc774 \uc18d\ud3b8\ud560 \uc218 \uc788\ub2e4.<\/p>\n<h2>\ubd80\ub85d<\/h2>\n<h3>\ubd80\ub85d A.<\/h3>\n<p>\uc120\ud615 \ubaa8\ud615\uc744 \uc704\ud574 \uc8fc\uc5b4\uc9c4 \ubcc0\uc218\ub97c \ubaa8\ub450 \ud45c\uc900\ud654\ud558\uace0, \uc608\uce21\uac12\uc5d0\uc11c \ub2e4\uc2dc \uc6d0\ub798 \uac12\uc744 \uc5bb\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\ub2e4. <\/p>\n<p>\\(y = \\beta_0+ \\beta_1 x_1 + \\beta_2 x_2 + \\cdots\\) \uc5d0\uc11c \\(x_1, x_2, \\cdots, x_{10}\\) \uc744 \ubaa8\ub450 \ud45c\uc900\ud654\ub97c \ud558\uba74 \ub2e4\uc74c\uacfc \uac19\ub2e4. \uc774\ub54c \uacc4\uc218\uac00 \\(\\beta_1\\) \uc5d0\uc11c $\\beta_1&#39;$\ub85c \ubcc0\ud654\ub418\uc5c8\uc74c\uc744 \uc720\uc758\ud55c\ub2e4.<\/p>\n<p>\\[\\frac{y &#8211; \\bar{y}}{\\textrm{sd}(y)} = \\beta_0&#8242; + \\beta_1&#8217;\\frac{x_1-\\bar{x_1}}{\\textrm{sd}(x_1)} + \\beta_2&#8217;\\frac{x_2-\\bar{x_2}}{\\textrm{sd}(x_2)} + \\cdots\\]<\/p>\n<p>\uc774\ub97c \uc544\ub798\uc640 \uac19\uc774 \uc798 \uc815\ub9ac\ud558\uba74 \\(\\beta_1&#8217;\\) \uacfc \\(\\beta_1\\) \uc758 \uad00\uacc4\ub97c \uc720\ucd94\ud574\ub0bc \uc218 \uc788\ub2e4. <\/p>\n<p>\\[ y = \\bar{y} +  \\textrm{sd}(y)\\beta&#8217;_0  + \\textrm{sd}(y)\\beta_1&#8217;\\frac{x_1-\\bar{x_1}}{\\textrm{sd}(x_1)} + \\textrm{sd}(y)\\beta_2&#8217;\\frac{x_2-\\bar{x_2}}{\\textrm{sd}(x_2)} \\]<\/p>\n<p>\\(\\beta_0&#8217;\\) \uc5d0\uc11c \\(\\beta_0\\) \uc744 \uacc4\uc0b0\ud574\ub0b4\ub294 \uc2dd\ub3c4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc720\ub3c4\ud560 \uc218 \uc788\ub2e4. <\/p>\n<p>\\[ y =\\left( \\bar{y} +  \\textrm{sd}(y)\\beta&#8217;_0  &#8211; \\frac{\\textrm{sd}(y)\\beta&#8217;_1\\bar{x_1}}{\\textrm{sd}(x_1)} &#8211; \\frac{\\textrm{sd}(y)\\beta&#8217;_2\\bar{x_2}}{\\textrm{sd}(x_2)} + \\cdots  \\right) +  \\frac{\\textrm{sd}(y)\\beta_1&#8242;}{\\textrm{sd}(x_1)}x_1 + \\frac{\\textrm{sd}(y)\\beta_2&#8242;}{\\textrm{sd}(x_2)}x_2 + \\cdots \\]<\/p>\n<h3>\ubd80\ub85d B. \uc2dc\ubbac\ub808\uc774\uc158 \uc5f0\uad6c<\/h3>\n<pre><code class=\"r\">func = function(f1,f2, e) {\r\n  return(2 + f1 - 1.4*f2 + e)\r\n  #return(1 + 2*sin(f1\/2) + 3 * cos(f2\/2))\r\n  #return(1 + sin(f1\/2) + cos(f2\/2))\r\n  #return(1 + 2 * sin(f1\/2) * cos(f2\/3))\r\n  #return(1 + sin(f1) * cos(f2))\r\n}\r\n\r\njags.model &lt;- &quot;\r\n\r\n\r\nmodel {\r\n  for (i in 1:N) {\r\n    ymean[i] &lt;- beta0 + beta1*x1[i] + beta2*x2[i] + beta3*x3[i] + beta4*x4[i] + beta5*x5[i] + \r\n            beta6*x6[i] + beta7*x7[i] + beta8*x8[i] + beta9*x9[i] + beta10*x10[i]\r\n    y[i] ~ dnorm(ymean[i], tau)\r\n  }\r\n\r\n  # priors\r\n  tau &lt;- pow(sigma2, -2)\r\n  sigma2 ~ dunif(0,100)\r\n  beta0 ~ dnorm(0, 0.01)\r\n  beta1 ~ dnorm(betaAmean, betaAtau)\r\n  beta2 ~ dnorm(betaAmean, betaAtau)\r\n  beta3 ~ dnorm(betaAmean, betaAtau)\r\n  beta4 ~ dnorm(betaAmean, betaAtau)\r\n  beta5 ~ dnorm(betaAmean, betaAtau)\r\n  beta6 ~ dnorm(betaBmean, betaBtau)\r\n  beta7 ~ dnorm(betaBmean, betaBtau)\r\n  beta8 ~ dnorm(betaBmean, betaBtau)\r\n  beta9 ~ dnorm(betaBmean, betaBtau)\r\n  beta10 ~ dnorm(betaBmean, betaBtau)\r\n  betaAmean ~ dunif(-100,100)\r\n  betaAtau &lt;- pow(betaAsigma2, -2)\r\n  betaBmean ~ dunif(-100,100)\r\n  betaBtau &lt;- pow(betaBsigma2, -2)\r\n  betaAsigma2 ~ dunif(0,100)\r\n  betaBsigma2 ~ dunif(0,100)\r\n}\r\n&quot;\r\n\r\ncat(jags.model, file=&#39;currentModel.j&#39;)\r\n\r\nsamplesize = c(20,40,60,80,100,200,300)\r\nnrep = 100\r\nresultSim &lt;- expand.grid(irep=1:nrep, samplesize=samplesize)\r\n\r\nresultSim &lt;- resultSim %&gt;% mutate(mod_full=NA, mod_factor=NA, mod_bayes=NA,\r\n                                  t_mod_full = NA, t_mod_factor = NA, t_mod_bayes = NA,\r\n                                  m_mod_full = NA, m_mod_factor = NA, m_mod_bayes = NA)\r\n# 1. MSE\r\n# 2. time\r\n# 3. memory\r\n\r\nfor (isim in 1:nrow(resultSim)) {\r\n  N &lt;- resultSim[isim, &quot;samplesize&quot;]\r\n  rm(dat,newdat, dat2, newdat2)\r\n\r\n  # Training set\r\n  ncolMatX &lt;- 11\r\n  sigma.cor = 0.1\r\n  sd.paramTrue = 0.2\r\n\r\n  sigma &lt;- matrix(\r\n    sigma.cor,\r\n    ncolMatX, ncolMatX)\r\n  diag(sigma) = 1\r\n\r\n  matX &lt;- rmvnorm(N, mean=rep(0,ncolMatX), sigma=sigma)\r\n  colnames(matX) &lt;- c(paste0(&quot;x&quot;, 1:10), &quot;e&quot;)\r\n  X &lt;- data.table(matX)\r\n\r\n  paramTrue &lt;- c(3, rnorm(5,1,sd.paramTrue),\r\n                 rnorm(5,-1.4,sd.paramTrue))\r\n  names(paramTrue) &lt;- paste0(&quot;beta&quot;, 0:10)\r\n\r\n  lv &lt;- X[, .(f1 = paramTrue[&#39;beta1&#39;]*x1 + \r\n                paramTrue[&#39;beta2&#39;]*x2 + \r\n                paramTrue[&#39;beta3&#39;]*x3 + \r\n                paramTrue[&#39;beta4&#39;]*x4 + \r\n                paramTrue[&#39;beta5&#39;]*x5,\r\n              f2 = paramTrue[&#39;beta6&#39;]*x6 + \r\n                paramTrue[&#39;beta7&#39;]*x7 + \r\n                paramTrue[&#39;beta8&#39;]*x8 + \r\n                paramTrue[&#39;beta9&#39;]*x9 + \r\n                paramTrue[&#39;beta10&#39;]*x10)]\r\n\r\n  y &lt;- func(lv$f1, lv$f2, X$e)\r\n\r\n  dfParamTrue &lt;- data.frame(key=factor(names(paramTrue), \r\n                                       levels= paste0(&quot;beta&quot;, 0:10)),\r\n                            value=paramTrue)\r\n\r\n  dat &lt;- data.table(X,y)\r\n  dat2 &lt;- X[, .(f1= x1+x2+x3+x4+x5, \r\n                f2= x6+x7+x8+x9+x10,\r\n                y = y)]\r\n  ###\r\n\r\n  ### Training\r\n  t1 &lt;- Sys.time()\r\n  # full linear model\r\n  fit &lt;- lm(y ~ . - e, dat)\r\n  t2 &lt;- Sys.time()\r\n  # factor linear model\r\n  fit2 &lt;- lm(y ~ ., dat2)\r\n  t3 &lt;- Sys.time()\r\n  # bayesian linear model\r\n  dat_ &lt;- (dat_mat &lt;- \r\n             dat %&gt;% select(-e) %&gt;% scale) %&gt;% data.frame\r\n\r\n  N = nrow(dat_)\r\n\r\n  myj &lt;- jags.model(&#39;currentModel.j&#39;,\r\n                    data = c(as.list(dat_), &#39;N&#39;=N),\r\n                    n.chains = 3)\r\n\r\n  update(myj, n.iter=2000)\r\n\r\n  n.thin=5\r\n\r\n  mcmcsamp &lt;- \r\n    coda.samples(myj, \r\n                 c(&#39;beta0&#39;, &#39;beta1&#39;, &#39;beta2&#39;, &#39;beta3&#39;, &#39;beta4&#39;, &#39;beta5&#39;,\r\n                   &#39;beta6&#39;, &#39;beta7&#39;, &#39;beta8&#39;, &#39;beta9&#39;, &#39;beta10&#39;,\r\n                   &#39;betaAmean&#39;, &#39;betaBmean&#39;, &#39;betaAsigma2&#39;, &#39;betaBsigma2&#39;, &#39;sigma2&#39;),\r\n                 1000*n.thin, thin=n.thin)  # if there no betaAmean, warning will be issue\r\n\r\n  t4 &lt;- Sys.time()\r\n\r\n  rm(dat, dat2)\r\n\r\n  resultSim[isim, &quot;t_mod_full&quot;] = t2 - t1\r\n  resultSim[isim, &quot;t_mod_factor&quot;] = t3 - t2\r\n  resultSim[isim, &quot;t_mod_bayes&quot;] = t4 - t3\r\n\r\n  resultSim[isim, &quot;m_mod_full&quot;] = as.numeric(object.size(fit))\/1024\/1024 # MB\r\n  resultSim[isim, &quot;m_mod_factor&quot;] = as.numeric(object.size(fit2))\/1024\/1024 # MB\r\n  resultSim[isim, &quot;m_mod_bayes&quot;] = as.numeric(object.size(mcmcsamp))\/1024\/1024 # MB\r\n\r\n  #########################\r\n  ## NEW DATA\r\n\r\n  N &lt;- 500\r\n\r\n  ncolMatX &lt;- 11; sigma.cor = 0.1\r\n\r\n  sigma &lt;- matrix(\r\n    sigma.cor,\r\n    ncolMatX, ncolMatX)\r\n  diag(sigma) = 1\r\n\r\n  matX &lt;- rmvnorm(N, mean=rep(0,ncolMatX), sigma=sigma)\r\n  colnames(matX) &lt;- c(paste0(&quot;x&quot;, 1:10), &quot;e&quot;)\r\n  X &lt;- data.table(matX)\r\n\r\n  lv &lt;- X[, .(f1 = paramTrue[&#39;beta1&#39;]*x1 + \r\n                paramTrue[&#39;beta2&#39;]*x2 + \r\n                paramTrue[&#39;beta3&#39;]*x3 + \r\n                paramTrue[&#39;beta4&#39;]*x4 + \r\n                paramTrue[&#39;beta5&#39;]*x5,\r\n              f2 = paramTrue[&#39;beta6&#39;]*x6 + \r\n                paramTrue[&#39;beta7&#39;]*x7 + \r\n                paramTrue[&#39;beta8&#39;]*x8 + \r\n                paramTrue[&#39;beta9&#39;]*x9 + \r\n                paramTrue[&#39;beta10&#39;]*x10)]\r\n\r\n  y &lt;- func(lv$f1, lv$f2, X$e)\r\n\r\n  newdat &lt;- data.table(X,y)\r\n  newdat2 &lt;- X[, .(f1= x1+x2+x3+x4+x5, \r\n                   f2= x6+x7+x8+x9+x10,\r\n                   y = y)]\r\n\r\n  ####\r\n\r\n  ## Bayesian model prediction\r\n\r\n  newdat_ &lt;- sweep(newdat %&gt;% select(-y), 2, attr(dat_mat, &quot;scaled:center&quot;), &quot;-&quot;)\r\n  newdat_ &lt;- sweep(newdat_ , 2, attr(dat_mat, &quot;scaled:scale&quot;), &quot;\/&quot;)\r\n\r\n  #mcmcsamp\r\n  #str(mcmcsamp)\r\n  bparm &lt;- rbind(mcmcsamp[[1]], mcmcsamp[[2]], mcmcsamp[[3]])[,paste0(&quot;beta&quot;, 0:10)]\r\n  Abparm &lt;- array(bparm, c(nrow(bparm), ncol(bparm), nrow(newdat_))) # mcmcsamp, parameter, datasamp\r\n\r\n  newdat_[,&quot;x0&quot;] = 1\r\n  newdat_ &lt;- as.matrix(newdat_)\r\n  newdat_ &lt;- newdat_[,paste0(&quot;x&quot;,0:10)]\r\n\r\n  Anewdat_ &lt;- array(newdat_, c(nrow(newdat_), ncol(newdat_), nrow(bparm))) # datasamp, parameter, mcmcsamp\r\n  Anewdat_ &lt;- aperm(Anewdat_, c(3,2,1)) # mcmcsamp, parameter, datasamp\r\n\r\n  newpred_ &lt;- apply((Anewdat_*Abparm), c(1,3), sum) # mcmcsamp, datasamp\r\n\r\n  newpred &lt;- attr(dat_mat, &quot;scaled:scale&quot;)[&#39;y&#39;] * newpred_ + attr(dat_mat, &quot;scaled:center&quot;)[&#39;y&#39;] \r\n\r\n  newpredMean &lt;- apply(newpred, 2, mean)\r\n  ####\r\n\r\n  resultSim[isim, &quot;mod_full&quot;] &lt;- mean((predict(fit, newdat)- newdat$y)^2)\r\n  resultSim[isim, &quot;mod_factor&quot;] &lt;- mean((predict(fit2, newdat2)-newdat2$y)^2)\r\n  resultSim[isim, &quot;mod_bayes&quot;] &lt;- mean((newpredMean-newdat2$y)^2)\r\n\r\n  if (isim %% 10 == 0) print(isim)\r\n}\r\n<\/code><\/pre>\n<p>\uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\ub2e4. (\ub450 \ubc88\uc9f8 \uadf8\ub798\ud504\ub294 \ubca0\uc774\uc9c0\uc548 \ubaa8\ud615\uacfc \ub2e4\ub978 \ubaa8\ud615\uacfc\uc758 \ube44\uad50\ub97c \uc704\ud55c \uadf8\ub798\ud504\uc774\uace0, \uc138 \ubc88\uc9f8 \uadf8\ub798\ud504\ub294 \uac01 \ubaa8\ud615\uc758 \uacb0\uacfc\ub97c \ub530\ub85c \uadf8\ub9b0 \uadf8\ub9bc. \uacb0\uacfc\uc801\uc740 \uc14b\uc740 \ubaa8\ub450 \uac19\uc740 \uacb0\uacfc\ub97c \ub098\ud0c0\ub0b8\ub2e4.)<\/p>\n<ul>\n<li><code>mod_full<\/code>: \uc804\uccb4 \uc120\ud615<\/li>\n<li><code>mod_factor<\/code>: \uc694\uc778 \uc120\ud615<\/li>\n<li><code>mod_bayes<\/code>: \ubca0\uc774\uc9c0\uc5b8 \uc120\ud615<\/li>\n<\/ul>\n<p>\uc5b8\uc81c\ub098 \ubca0\uc774\uc9c0\uc548\uc774 \ucd5c\uace0\ub294 \uc544\ub2c8\uc9c0\ub9cc, <strong>\ud3c9\uade0<\/strong>\uc801\uc73c\ub85c <strong>\ubca0\uc774\uc9c0\uc548\uc774 \ucd5c\uace0\ub2e4!<\/strong><\/p>\n<p><img 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title=\"plot of chunk unnamed-chunk-10\" alt=\"plot of chunk unnamed-chunk-10\" width=\"80%\" \/><img 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title=\"plot of chunk unnamed-chunk-10\" alt=\"plot of chunk unnamed-chunk-10\" width=\"80%\" \/><\/p>\n<p>\ubb3c\ub860 \uc120\ud615 \ubaa8\ud615\uc774\ub77c\ub294 \ud55c\uacc4\uac00 \uc788\uc9c0\ub9cc, \ud2b8\ub9ac\ubaa8\ud615\uc5d0\ub3c4 <strong>BART<\/strong>(<strong>B<\/strong>ayesian <strong>A<\/strong>dditive <strong>R<\/strong>egression <strong>T<\/strong>rees)\ub77c\ub294 \ubca0\uc774\uc9c0\uc548 \ubaa8\ud615\uc774 \uc874\uc7ac\ud55c\ub2e4! (\ud45c\ubcf8 \ud06c\uae30\uac00 \uc791\uac70\ub098, \ucef4\ud4e8\ud130\uc5d0 \ub0a8\uc544 \ub3c4\ub294 core\uac00 \ub9ce\uc744 \ub54c \ud55c \ubc88 \uc2dc\ub3c4\ub97c&hellip;)<\/p>\n<ul>\n<li>BART: Bayesian Additive Regression Trees, <a href=\"https:\/\/arxiv.org\/abs\/0806.3286\">https:\/\/arxiv.org\/abs\/0806.3286<\/a><\/li>\n<li><a href=\"https:\/\/blog.zakjost.com\/post\/bart\/\">https:\/\/blog.zakjost.com\/post\/bart\/<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\uc9c0\ub09c \ubc88\uc758 \ubb38\uc81c\ub97c \ub2e4\uc2dc \ud55c \ubc88 \ubcf4\uc790. \ub2e4\uc74c\uc758 R \ucf54\ub4dc\ub294 \ub370\uc774\ud130 dat\ub97c \uc0dd\uc131\ud55c\ub2e4. \uc774\ub54c \uc124\uba85\ubcc0\uc218\ub294 x1\uc5d0\uc11c x10\uc740 \uc0c1\uad00\uad00\uacc4 0.1\uc778 \ud45c\uc900\uc815\uaddc\ubd84\ud3ec\ub97c \ub530\ub978\ub2e4. \uc2e4\uc81c \ub370\uc774\ud130\uc0dd\uc131\ubaa8\ud615\uc740 \ub2e4\uc74c\uacfc \uac19\ub2e4. \\[y = 3 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_{10} x_{10} + e,\\ \\ e \\sim \\mathcal{N}(0,1)\\] \uc815\ud655\ud55c \ubaa8\ud615\uc744 \uc54c\uace0 \uc788\ub2e4\uba74 \uc790\ub8cc\ub97c \ubaa8\ud615\uc5d0 \uc801\ud569\ud558\uc5ec \ubaa8\uc218( \\(\\beta_1, \\beta_2, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1997,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[390,146,319],"tags":[389,368,321,388,387],"jetpack_featured_media_url":"http:\/\/ds.sumeun.org\/wp-content\/uploads\/2019\/09\/featureEngineering_bayesian.png","_links":{"self":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1989"}],"collection":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1989"}],"version-history":[{"count":7,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1989\/revisions"}],"predecessor-version":[{"id":1996,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/posts\/1989\/revisions\/1996"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=\/wp\/v2\/media\/1997"}],"wp:attachment":[{"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1989"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1989"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/ds.sumeun.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1989"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}