<\/p>\n
Bu e\u011fitimde, R’deki bir veri k\u00fcmesine gama da\u011f\u0131l\u0131m\u0131n\u0131n<\/strong> nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki yakla\u015f\u0131m\u0131 kullanarak olu\u015fturulan bir z<\/em> veri k\u00fcmeniz oldu\u011funu varsayal\u0131m:<\/span><\/p>\n Bir gama da\u011f\u0131l\u0131m\u0131n\u0131n bu z<\/em> veri k\u00fcmesine ne kadar iyi uydu\u011funu g\u00f6rmek i\u00e7in R’deki fitdistrplus<\/strong> paketini kullanabiliriz:<\/span><\/p>\n Bu paketi kullanarak bir da\u011f\u0131t\u0131m\u0131 uyarlamak i\u00e7in kullan\u0131lacak genel s\u00f6zdizimi \u015f\u00f6yledir:<\/span><\/p>\n fitdist(dataset, distr = \u201cda\u011f\u0131t\u0131m se\u00e7iminiz\u201d, method = \u201cverileri uydurma y\u00f6nteminiz\u201d)<\/span><\/strong><\/p>\n Bu durumda, gama da\u011f\u0131l\u0131m\u0131n\u0131 ve maksimum olas\u0131l\u0131k tahmin yakla\u015f\u0131m\u0131n\u0131 kullanarak daha \u00f6nce olu\u015fturdu\u011fumuz z<\/em> veri k\u00fcmesini verilere uyacak \u015fekilde s\u0131\u011fd\u0131raca\u011f\u0131z:<\/span><\/p>\n Bu, a\u015fa\u011f\u0131daki sonucu \u00fcretir:<\/span><\/p>\n Daha sonra a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanarak gama da\u011f\u0131l\u0131m\u0131n\u0131n veri k\u00fcmesine ne kadar iyi uydu\u011funu g\u00f6steren grafikler \u00fcretebiliriz:<\/span><\/p>\n Bu, a\u015fa\u011f\u0131daki grafikleri \u00fcretir:<\/span><\/p>\n R’deki bir veri k\u00fcmesine gama da\u011f\u0131l\u0131m\u0131n\u0131 s\u0131\u011fd\u0131rmak i\u00e7in kulland\u0131\u011f\u0131m\u0131z kodun tamam\u0131 a\u015fa\u011f\u0131da verilmi\u015ftir:<\/span><\/p>\n Bu e\u011fitimde, R’deki bir veri k\u00fcmesine gama da\u011f\u0131l\u0131m\u0131n\u0131n nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r. R’ye bir gama da\u011f\u0131l\u0131m\u0131 yerle\u015ftirme A\u015fa\u011f\u0131daki yakla\u015f\u0131m\u0131 kullanarak olu\u015fturulan bir z veri k\u00fcmeniz oldu\u011funu varsayal\u0131m: #generate 50 random values that follow a gamma distribution with shape parameter = 3 #and shape parameter = 10 combined with some gaussian noise z <- rgamma(50, 3, 10) […]<\/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":[],"class_list":["post-456","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n R’ye bir gama da\u011f\u0131l\u0131m\u0131 yerle\u015ftirme<\/span><\/strong><\/h2>\n
#generate 50 random values that follow a gamma distribution with shape parameter = 3<\/span>\n#and shape parameter = 10 combined with some gaussian noise<\/span>\nz <- rgamma(50, 3, 10) + rnorm(50, 0, .02)\n\n#view first 6 values<\/span>\nhead(z)\n[1] 0.07730 0.02495 0.12788 0.15011 0.08839 0.09941<\/strong><\/pre>\n #install 'fitdistrplus' package if not already installed<\/span>\ninstall. packages<\/span> ('fitdistrplus')\n\n#load package<\/span>\nlibrary(fitdistrplus)<\/strong><\/pre>\n #fit our dataset to a gamma distribution using mle<\/span>\nfit <- fitdist(z, distr = \"gamma\", method = \"male\")\n\n#view the summary of the fit<\/span>\nsummary(fit)<\/strong><\/pre>\n #produce plots<\/span>\nplot(fit)<\/strong><\/pre>\n #install 'fitdistrplus' package if not already installed\n<\/span>install. packages<\/span> ('fitdistrplus')\n\n#load package\n<\/span>library(fitdistrplus)\n\n#generate 50 random values that follow a gamma distribution with shape parameter = 3\n<\/span>#and shape parameter = 10 combined with some gaussian noise\n<\/span>z <- rgamma(50, 3, 10) + rnorm(50, 0, .02)\n\n#fit our dataset to a gamma distribution using mle\n<\/span>fit <- fitdist(z, distr = \"gamma\", method = \"male\")\n\n#view the summary of the fit\n<\/span>summary(fit)\n\n#produce plots to visualize the fit\n<\/span>plot(fit)\n<\/strong><\/pre>\n","protected":false},"excerpt":{"rendered":"