Untungnya, R memudahkan pembuatan point cloud menggunakan fungsi plot()<\/strong> . Misalnya:<\/span> <\/p>\n Menambahkan garis regresi ke scatterplot juga mudah menggunakan fungsi abline()<\/strong> .<\/span><\/p>\n Misalnya:<\/span> <\/p>\n Kita juga dapat menambahkan garis interval kepercayaan ke plot menggunakan fungsi prediksi()<\/strong> :<\/span> <\/p>\n Atau kita bisa menambahkan garis interval prediksi ke plot dengan menentukan tipe interval dalam fungsi prediksi()<\/strong> :<\/span> <\/p>\n Terakhir, kita dapat membuat plot lebih estetis dengan menambahkan judul, mengubah nama sumbu, dan mengubah bentuk masing-masing titik plot.<\/span> <\/p>\n Tutorial berikut menjelaskan cara melakukan tugas umum lainnya di R:<\/span><\/p>\n Cara memberi label titik pada plot sebar di R<\/a> Seringkali saat kita melakukan regresi linier sederhana, kita ingin membuat diagram sebar untuk memvisualisasikan berbagai kombinasi nilai x dan y. Untungnya, R memudahkan pembuatan point cloud menggunakan fungsi plot() . Misalnya: #create some fake data data <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11), y […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\n #create some fake data<\/span>\ndata <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11),\n y = c(13, 14, 17, 12, 23, 24, 25, 25, 24, 28, 32, 33, 35, 40, 41))\n\n#create scatterplot of data<\/span>\nplot(data$x, data$y)\n<\/strong><\/pre>\n![\"Plot](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression1.png\")
<\/p>\n #fit a simple linear regression model<\/span>\nmodel <- lm(y ~ x, data = data)\n\n#add the fitted regression line to the scatterplot<\/span>\nabline(model)<\/strong> <\/pre>\n![\"Plot](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression2.png\")
<\/p>\n #define range of x values<\/span>\nnewx = seq(min(data$x),max(data$x),by = 1)\n\n#find 95% confidence interval for the range of x values<\/span> \nconf_interval <- predict(model, newdata=data.frame(x=newx), interval=\"confidence\",\n level = 0.95)\n\n#create scatterplot of values with regression line<\/span>\nplot(data$x, data$y)\nabline(model)\n\n#add dashed lines (lty=2) for the 95% confidence interval<\/span>\nlines(newx, conf_interval[,2], col=\"blue\", lty=2)\nlines(newx, conf_interval[,3], col=\"blue\", lty=2)\n<\/strong><\/pre>\n![\"Garis](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression3.png\")
<\/p>\n #define range of x values<\/span>\nnewx = seq(min(data$x),max(data$x),by = 1)\n\n#find 95% prediction interval for the range of x values<\/span> \npred_interval <- predict(model, newdata=data.frame(x=newx), interval=\"prediction\"<\/span> ,\n level = 0.95)\n\n#create scatterplot of values with regression line<\/span>\nplot(data$x, data$y)\nabline(model)\n\n#add dashed lines (lty=2) for the 95% confidence interval<\/span>\nlines(newx, pred_interval[,2], col=\"red\", lty=2)\nlines(newx, pred_interval[,3], col=\"red\", lty=2)\n<\/strong><\/pre>\n![\"Garis](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression4.png\")
<\/p>\n plot(data$x, data$y,\n main = \"Scatterplot of x vs. y\", #add title<\/span>\n pch=16, #specify points to be filled in<\/span>\n xlab='x', #change x-axis name\n<\/span>ylab='y') #change y-axis name<\/span>\n\nabline(model, col='steelblue') #specify color of regression line<\/span><\/span>\n<\/strong><\/pre>\n![\"Garis](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression5.png\")
<\/p>\n Sumber daya tambahan<\/strong><\/span><\/h2>\n
Cara menggunakan fungsi Jitter di R untuk point cloud<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"