# tomroh/fitur

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 --- title: "Introduction to fitur" author: "Thomas Roh" date: "February 13, 2017" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Introduction to fitur} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r setup, include=FALSE} knitr::opts_chunk\$set(echo = TRUE, fig.height = 5, fig.width = 7) library(fitur) ``` ## Basic Function fitur is a package to provide wrapper functions for fitting univariate distributions. The main function is `fit_univariate` where you can supply numeric data to the function along with the desired attributes of the distribution you want to fit. It returns a list object with the density, distribution, quantile, and random deviates functions based on the calculated parameters from the given numeric vector. The parameter estimation is done with MLE. ## Discrete Distributions ```{r discrete} set.seed(42) x <- rpois(1000, 3) fitted <- fit_univariate(x, 'pois', type = 'discrete') # density function plot(fitted\$dpois(x=0:10), xlab = 'x', ylab = 'dpois') # distribution function plot(fitted\$ppois(seq(0, 10, 1)), xlab= 'x', ylab = 'ppois') # quantile function plot(fitted\$qpois, xlab= 'x', ylab = 'qpois') # sample from theoretical distribution summary(fitted\$rpois(100)) # estimated parameters from MLE fitted\$parameters ``` ## Continuous Distributions ```{r continuous} set.seed(24) x <- rweibull(1000, shape = .5, scale = 2) fitted <- fit_univariate(x, 'weibull') # density function plot(fitted\$dweibull, xlab = 'x', ylab = 'dweibull') # distribution function plot(fitted\$pweibull, xlab = 'x', ylab = 'pweibull') # quantile function plot(fitted\$qweibull, xlab = 'x', ylab = 'qweibull') # sample from theoretical distribution summary(fitted\$rweibull(100)) # estimated parameters from MLE fitted\$parameters ``` ## Empirical Distributions The package also allows users to specify empirical distributions. For discrete distributions, the function will not truncate any integer values with the given input. For continuous distributions, the function will create bins using the Freedman-Diaconis rule. ### Discrete ```{r empiricalDiscrete} set.seed(562) x <- rpois(100, 5) empDis <- fit_empirical(x) # probability density function plot(empDis\$dempDis(0:10), xlab = 'x', ylab = 'dempDis') # cumulative distribution function plot(x = 0:10, y = empDis\$pempDis(0:10), #type = 'l', xlab = 'x', ylab = 'pempDis') # quantile function plot(x = seq(.1, 1, .1), y = empDis\$qempDis(seq(.1, 1, .1)), type = 'p', xlab = 'x', ylab = 'qempDis') # random sample from fitted distribution summary(empDis\$r(100)) empDis\$parameters ``` ### Continuous ```{r empiricalContinous} set.seed(562) x <- rexp(100, 1/5) empCont <- fit_empirical(x) # probability density function plot(x = 0:10, y = empCont\$dempCont(0:10), xlab = 'x', ylab = 'dempCont') # cumulative distribution function plot(x = 0:10, y = empCont\$pempCont(0:10), #type = 'l', xlab = 'x', ylab = 'pempCont') # quantile function plot(x = seq(.1, 1, by = .1), y = empCont\$qempCont(seq(.1, 1, by = .1)), type = 'p', xlab = 'x', ylab = 'qempCont') # random sample from fitted distribution summary(empCont\$r(100)) empCont\$parameters ```