Na szcz\u0119\u015bcie R u\u0142atwia tworzenie chmur punkt\u00f3w za pomoc\u0105 funkcji plot()<\/strong> . Na przyk\u0142ad:<\/span> <\/p>\n Mo\u017cna r\u00f3wnie\u017c \u0142atwo doda\u0107 lini\u0119 regresji do wykresu rozrzutu za pomoc\u0105 funkcji abline()<\/strong> .<\/span><\/p>\n Na przyk\u0142ad:<\/span> <\/p>\n Mo\u017cemy r\u00f3wnie\u017c doda\u0107 do wykresu linie przedzia\u0142u ufno\u015bci za pomoc\u0105 funkcji przewidywania()<\/strong> :<\/span> <\/p>\n Mo\u017cemy te\u017c zamiast tego doda\u0107 do wykresu linie przedzia\u0142\u00f3w przewidywania, okre\u015blaj\u0105c typ przedzia\u0142u w funkcji przewidywania()<\/strong> :<\/span> <\/p>\n Wreszcie mo\u017cemy uatrakcyjni\u0107 fabu\u0142\u0119, dodaj\u0105c tytu\u0142, zmieniaj\u0105c nazwy osi i zmieniaj\u0105c kszta\u0142t poszczeg\u00f3lnych punkt\u00f3w dzia\u0142ki.<\/span> <\/p>\n Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe zadania w j\u0119zyku R:<\/span><\/p>\n Jak oznaczy\u0107 punkty na wykresie rozrzutu w R<\/a> Cz\u0119sto, gdy przeprowadzamy prost\u0105 regresj\u0119 liniow\u0105, chcemy utworzy\u0107 wykres rozrzutu, aby zwizualizowa\u0107 r\u00f3\u017cne kombinacje warto\u015bci x i y. Na szcz\u0119\u015bcie R u\u0142atwia tworzenie chmur punkt\u00f3w za pomoc\u0105 funkcji plot() . Na przyk\u0142ad: #create some fake data data <- data.frame(x = c(1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 11, 11), […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-682","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"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![\"Wykres](\"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![\"Wykres](\"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![\"Linia](\"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![\"Linia](\"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![\"Linia](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/scatterregression5.png\")
<\/p>\n Dodatkowe zasoby<\/strong><\/span><\/h2>\n
Jak korzysta\u0107 z funkcji Jitter w R dla chmur punkt\u00f3w<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"