{"id":1160,"date":"2023-07-27T11:00:03","date_gmt":"2023-07-27T11:00:03","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%91%e1%85%a1%e1%86%ab%e1%84%87%e1%85%a7%e1%86%af-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%89%e1%85%a5%e1%86%a8\/"},"modified":"2023-07-27T11:00:03","modified_gmt":"2023-07-27T11:00:03","slug":"r%e1%84%8b%e1%85%b4-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%91%e1%85%a1%e1%86%ab%e1%84%87%e1%85%a7%e1%86%af-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%89%e1%85%a5%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%91%e1%85%a1%e1%86%ab%e1%84%87%e1%85%a7%e1%86%af-%e1%84%87%e1%85%ae%e1%86%ab%e1%84%89%e1%85%a5%e1%86%a8\/","title":{"rendered":"R\uc758 \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d(\ub2e8\uacc4\ubcc4)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1111\u1161\u11ab\u1107\u1167\u11af-\u1107\u116e\u11ab\u1109\u1165\u11a8\/\" target=\"_blank\" rel=\"noopener noreferrer\">\uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\uc740<\/a> \uc77c\ub828\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc788\uace0 <a href=\"https:\/\/statorials.org\/ko\/\u1107\u1167\u11ab\u1109\u116e-\u1109\u1165\u11af\u1106\u1167\u11bc-\u110b\u1173\u11bc\u1103\u1161\u11b8\/\" target=\"_blank\" rel=\"noopener noreferrer\">\uc751\ub2f5 \ubcc0\uc218\ub97c<\/a> \ub450 \uac1c \uc774\uc0c1\uc758 \ud074\ub798\uc2a4\ub85c \ubd84\ub958\ud558\ub824\ub294 \uacbd\uc6b0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \ub85c\ub4dc<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 \uc774 \uc608\uc81c\uc5d0 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><span style=\"color: #993300;\">library<\/span> (MASS)\n<span style=\"color: #993300;\">library<\/span> (ggplot2)<\/b><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>2\ub2e8\uacc4: \ub370\uc774\ud130 \ub85c\ub4dc<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 R\uc5d0 \ub0b4\uc7a5\ub41c <strong>\ubd93\uaf43<\/strong> \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 \uc774 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub85c\ub4dc\ud558\uace0 \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#attach <em>iris<\/em> dataset to make it easy to work with<\/span>\nattach(iris)\n\n<span style=\"color: #008080;\">#view structure of dataset\n<\/span>str(iris)\n\n'data.frame': 150 obs. of 5 variables:\n $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...\n $ Sepal.Width: num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...\n $Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...\n $Petal.Width: num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...\n $ Species: Factor w\/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 ...\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub370\uc774\ud130 \uc138\ud2b8\uc5d0\ub294 \ucd1d 5\uac1c\uc758 \ubcc0\uc218\uc640 150\uac1c\uc758 \uad00\uce21\uce58\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\ub294 \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 \ud2b9\uc815 \uaf43\uc774 \uc5b4\ub5a4 \uc885\uc5d0 \uc18d\ud558\ub294\uc9c0 \ubd84\ub958\ud558\uae30 \uc704\ud574 \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ubaa8\ub378\uc5d0\uc11c \ub2e4\uc74c \uc608\uce21 \ubcc0\uc218\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uaf43\ubc1b\uce68 \uae38\uc774<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uaf43\ubc1b\uce68.\ud3ed<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uaf43\uc78e.\uae38\uc774<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uaf43\uc78e.\ud3ed<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uadf8\ub9ac\uace0 \uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc74c \uc138 \uac00\uc9c0 \uc7a0\uc7ac\uc801 \ud074\ub798\uc2a4\ub97c \uc9c0\uc6d0\ud558\ub294 <em>\uc885<\/em> \uc751\ub2f5 \ubcc0\uc218\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc138\ud1a0\uc0ac<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ubca0\ub974\uc2dc\uceec\ub7ec<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc5ec\uc790 \uc774\ub984<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \ub370\uc774\ud130 \ud06c\uae30 \uc870\uc815<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\uc758 \uc8fc\uc694 \uac00\uc815 \uc911 \ud558\ub098\ub294 \uac01 \uc608\uce21 \ubcc0\uc218\uc758 \ubd84\uc0b0\uc774 \ub3d9\uc77c\ud558\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uac00\uc815\uc774 \ucda9\uc871\ub418\ub294\uc9c0 \ud655\uc778\ud558\ub294 \uac04\ub2e8\ud55c \ubc29\ubc95\uc740 \ud3c9\uade0\uc774 0\uc774\uace0 \ud45c\uc900\ud3b8\ucc28\uac00 1\uc774 \ub418\ub3c4\ub85d \uac01 \ubcc0\uc218\uc758 \ud06c\uae30\ub97c \uc870\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>scale()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c \uc774 \uc791\uc5c5\uc744 \ube60\ub974\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#scale each predictor variable (ie first 4 columns)\n<\/span>iris[1:4] &lt;- scale(iris[1:4])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/r\u110b\u1166-lapply-sapply-\u1106\u1175\u11be-tapply\u1105\u1173\u11af-\u110c\u1165\u11a8\u110b\u116d\u11bc\u1112\u1161\u1102\u1173\u11ab-\u1107\u1161\u11bc\u1107\u1165\u11b8\u110b\u1166-\u1103\u1162\u1112\u1161\u11ab-\u110b\u1161\u11ab\u1102\u1162\/\" target=\"_blank\" rel=\"noopener noreferrer\">apply() \ud568\uc218\ub97c<\/a> \uc0ac\uc6a9\ud558\uc5ec \uac01 \uc608\uce21 \ubcc0\uc218\uc758 \ud3c9\uade0\uc774 0\uc774\uace0 <a href=\"https:\/\/statorials.org\/ko\/r\u110b\u1174-\u1111\u116d\u110c\u116e\u11ab\u1111\u1167\u11ab\u110e\u1161\/\" target=\"_blank\" rel=\"noopener noreferrer\">\ud45c\uc900 \ud3b8\ucc28\uac00<\/a> 1\uc778\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find mean of each predictor variable\n<\/span>apply(iris[1:4], 2, mean)\n\n Sepal.Length Sepal.Width Petal.Length Petal.Width \n-4.484318e-16 2.034094e-16 -2.895326e-17 -3.663049e-17 \n\n<span style=\"color: #008080;\">#find standard deviation of each predictor variable\n<\/span>apply(iris[1:4], 2, sd) \n\nSepal.Length Sepal.Width Petal.Length Petal.Width \n           1 1 1 1\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>4\ub2e8\uacc4: \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \uc0d8\ud50c \uc0dd\uc131<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uae30 \uc704\ud55c \ud6c8\ub828 \uc138\ud2b8\uc640 \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud55c \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(1)\n\n<span style=\"color: #008080;\">#Use 70% of dataset as training set and remaining 30% as testing set\n<\/span>sample &lt;- sample(c( <span style=\"color: #008000;\">TRUE<\/span> , <span style=\"color: #008000;\">FALSE<\/span> ), <span style=\"color: #3366ff;\">nrow<\/span> (iris), <span style=\"color: #3366ff;\">replace<\/span> = <span style=\"color: #008000;\">TRUE<\/span> , <span style=\"color: #3366ff;\">prob<\/span> =c(0.7,0.3))\ntrain &lt;- iris[sample, ]\ntest &lt;- iris[!sample, ] \n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>5\ub2e8\uacc4: LDA \ubaa8\ub378 \uc870\uc815<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c <strong>MASS<\/strong> \ud328\ud0a4\uc9c0\uc758 <a href=\"https:\/\/www.rdocumentation.org\/packages\/MASS\/versions\/7.3-53\/topics\/lda\" target=\"_blank\" rel=\"noopener noreferrer\">lda() \ud568\uc218\ub97c<\/a> \uc0ac\uc6a9\ud558\uc5ec LDA \ubaa8\ub378\uc744 \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit LDA model\n<\/span>model &lt;- lda(Species~., data=train)\n\n<span style=\"color: #008080;\">#view model output<\/span>\nmodel\n\nCall:\nlda(Species ~ ., data = train)\n\nPrior probabilities of groups:\n    setosa versicolor virginica \n 0.3207547 0.3207547 0.3584906 \n\nGroup means:\n           Sepal.Length Sepal.Width Petal.Length Petal.Width\nsetosa -1.0397484 0.8131654 -1.2891006 -1.2570316\nversicolor 0.1820921 -0.6038909 0.3403524 0.2208153\nvirginica 0.9582674 -0.1919146 1.0389776 1.1229172\n\nCoefficients of linear discriminants:\n                    LD1 LD2\nSepal.Length 0.7922820 0.5294210\nSepal.Width 0.5710586 0.7130743\nPetal.Length -4.0762061 -2.7305131\nPetal.Width -2.0602181 2.6326229\n\nProportion of traces:\n   LD1 LD2 \n0.9921 0.0079 \n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ubaa8\ub378 \uacb0\uacfc\ub97c \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uadf8\ub8f9 \uc0ac\uc804 \ud655\ub960:<\/strong> \ud6c8\ub828 \uc138\ud2b8\uc5d0 \uc788\ub294 \uac01 \uc885\uc758 \ube44\uc728\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ud6c8\ub828 \uc138\ud2b8\uc758 \ubaa8\ub4e0 \uad00\uce21\uce58 \uc911 35.8%\ub294 <em>virginica<\/em> \uc885\uc5d0 \ub300\ud55c \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uadf8\ub8f9 \ud3c9\uade0:<\/strong> \uac01 \uc885\uc5d0 \ub300\ud55c \uac01 \uc608\uce21 \ubcc0\uc218\uc758 \ud3c9\uade0 \uac12\uc744 \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uc120\ud615 \ud310\ubcc4 \uacc4\uc218:<\/strong> LDA \ubaa8\ub378 \uacb0\uc815 \uaddc\uce59\uc744 \ud6c8\ub828\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \uc608\uce21 \ubcc0\uc218\uc758 \uc120\ud615 \uc870\ud569\uc744 \ud45c\uc2dc\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>LD1:<\/strong> 0.792 * \uaf43\ubc1b\uce68 \uae38\uc774 + 0.571 * \uaf43\ubc1b\uce68 \ub108\ube44 \u2013 4.076 * \uaf43\uc78e \uae38\uc774 \u2013 2.06 * \uaf43\uc78e \ub108\ube44<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>LD2:<\/strong> 0.529 * \uaf43\ubc1b\uce68 \uae38\uc774 + 0.713 * \uaf43\ubc1b\uce68 \ub108\ube44 \u2013 2.731 * \uaf43\uc78e \uae38\uc774 + 2.63 * \uaf43\uc78e \ub108\ube44<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>\ucd94\uc801 \ube44\uc728:<\/strong> \uac01 \uc120\ud615 \ud310\ubcc4 \ud568\uc218\uc5d0 \uc758\ud574 \ub2ec\uc131\ub41c \ubd84\ub9ac \ube44\uc728\uc744 \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>6\ub2e8\uacc4: \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\ud558\uae30<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ud6c8\ub828 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ub9de\ucd98 \ud6c4\uc5d0\ub294 \uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#use LDA model to make predictions on test data\n<\/span>predicted &lt;- <span style=\"color: #3366ff;\">predict<\/span> (model, test)\n\nnames(predicted)\n\n[1] \"class\" \"posterior\" \"x\"   \n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uadf8\ub7ec\uba74 \uc138 \uac00\uc9c0 \ubcc0\uc218\uac00 \ud3ec\ud568\ub41c \ubaa9\ub85d\uc774 \ubc18\ud658\ub429\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\ud074\ub798\uc2a4:<\/strong> \uc608\uce21\ub41c \ud074\ub798\uc2a4<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\uc0ac\ud6c4:<\/strong> \uad00\uce21\uce58\uac00 \uac01 \ud074\ub798\uc2a4\uc5d0 \uc18d\ud560 <a href=\"https:\/\/statorials.org\/ko\/\u1109\u1161\u1112\u116e-\u1112\u116a\u11a8\u1105\u1172\u11af\/\" target=\"_blank\" rel=\"noopener noreferrer\">\uc0ac\ud6c4 \ud655\ub960<\/a><\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x:<\/strong> \uc120\ud615 \ud310\ubcc4<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \ucc98\uc74c 6\uac1c \uad00\ucc30\uc5d0 \ub300\ud55c \uac01 \uacb0\uacfc\ub97c \ube60\ub974\uac8c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view predicted class for first six observations in test set\n<\/span>head(predicted$class)\n\n[1] setosa setosa setosa setosa setosa setosa\nLevels: setosa versicolor virginica\n\n<span style=\"color: #008080;\">#view posterior probabilities for first six observations in test set<\/span>\nhead(predicted$posterior)\n\n   setosa versicolor virginica\n4 1 2.425563e-17 1.341984e-35\n6 1 1.400976e-21 4.482684e-40\n7 1 3.345770e-19 1.511748e-37\n15 1 6.389105e-31 7.361660e-53\n17 1 1.193282e-25 2.238696e-45\n18 1 6.445594e-22 4.894053e-41\n\n<span style=\"color: #008080;\">#view linear discriminants for first six observations in test set\n<\/span>head(predicted$x)\n\n         LD1 LD2\n4 7.150360 -0.7177382\n6 7.961538 1.4839408\n7 7.504033 0.2731178\n15 10.170378 1.9859027\n17 8.885168 2.1026494\n18 8.113443 0.7563902\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec LDA \ubaa8\ub378\uc774 \uc885\uc744 \uc62c\ubc14\ub974\uac8c \uc608\uce21\ud55c \uad00\uce21\uce58\uc758 \ube44\uc728\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find accuracy of model\n<\/span>mean(predicted$class==test$Species)\n\n[1] 1<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ubaa8\ub378\uc740 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uad00\uce21\uce58 \uc911 <strong>100%<\/strong> \uc5d0 \ub300\ud574 \uc885\uc744 \uc815\ud655\ud558\uac8c \uc608\uce21\ud55c \uac83\uc73c\ub85c \ub098\ud0c0\ub0ac\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc2e4\uc81c \uc138\uacc4\uc5d0\uc11c LDA \ubaa8\ub378\uc740 \uac01 \ud074\ub798\uc2a4\uc758 \uacb0\uacfc\ub97c \uc815\ud655\ud558\uac8c \uc608\uce21\ud558\ub294 \uacbd\uc6b0\uac00 \uac70\uc758 \uc5c6\uc9c0\ub9cc, \uc774 \ubd93\uaf43 \ub370\uc774\ud130\uc138\ud2b8\ub294 \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc774 \ub9e4\uc6b0 \uc798 \uc218\ud589\ub418\ub294 \uacbd\ud5a5\uc774 \uc788\ub294 \ubc29\uc2dd\uc73c\ub85c \uac04\ub2e8\ud788 \uad6c\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>7\ub2e8\uacc4: \uacb0\uacfc \uc2dc\uac01\ud654<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub9c8\uc9c0\ub9c9\uc73c\ub85c LDA \ud50c\ub86f\uc744 \uc0dd\uc131\ud558\uc5ec \ubaa8\ub378\uc758 \uc120\ud615 \ud310\ubcc4\uc744 \uc2dc\uac01\ud654\ud558\uace0 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c \uc138 \uac00\uc9c0 \ub2e4\ub978 \uc885\uc744 \uc5bc\ub9c8\ub098 \uc798 \ubd84\ub9ac\ud558\ub294\uc9c0 \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data to plot\n<\/span>lda_plot &lt;- cbind(train, predict(model)$x)\n\n<span style=\"color: #008080;\">#createplot\n<\/span>ggplot(lda_plot, <span style=\"color: #3366ff;\">aes<\/span> (LD1, LD2)) +\n  geom_point( <span style=\"color: #3366ff;\">aes<\/span> (color=Species))\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11639 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lda_r1.png\" alt=\"R\uc758 \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\" width=\"431\" height=\"427\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc0ac\uc6a9\ub41c \uc804\uccb4 R \ucf54\ub4dc\ub294 <a href=\"https:\/\/github.com\/Statorials\/R-Guides\/blob\/main\/linear_discriminant_analysis\" target=\"_blank\" rel=\"noopener noreferrer\">\uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\uc740 \uc77c\ub828\uc758 \uc608\uce21 \ubcc0\uc218\uac00 \uc788\uace0 \uc751\ub2f5 \ubcc0\uc218\ub97c \ub450 \uac1c \uc774\uc0c1\uc758 \ud074\ub798\uc2a4\ub85c \ubd84\ub958\ud558\ub824\ub294 \uacbd\uc6b0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. 1\ub2e8\uacc4: \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \ub85c\ub4dc \uba3c\uc800 \uc774 \uc608\uc81c\uc5d0 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4. library (MASS) library (ggplot2) 2\ub2e8\uacc4: \ub370\uc774\ud130 \ub85c\ub4dc \uc774 \uc608\uc5d0\uc11c\ub294 R\uc5d0 \ub0b4\uc7a5\ub41c \ubd93\uaf43 \ub370\uc774\ud130 \uc138\ud2b8\ub97c [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-1160","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R\uc758 \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d(\ub2e8\uacc4\ubcc4)<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e8\uacc4\ubcc4 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec R\uc5d0\uc11c \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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