fit_empirical | R Documentation |
Fit Empirical Distribution
fit_empirical(x)
x |
integer or double vector |
if integer vector then list of family functions for d, p, q, r, and parameters based on each integer value. if it is a double vector then list of family functions for d, p, q, r, and parameters based on Freedman-Diaconis rule for optimal number of histogram bins.
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$parameters set.seed(562) x <- rexp(100, 1/5) empCont <- fit_empirical(x) # probability density function plot(x = 0:10, y = empCont$dempCont(0:10), xlab = 'x', ylab = 'dempCont') # cumulative distribution function plot(x = 0:10, y = empCont$pempCont(0:10), #type = 'l', xlab = 'x', ylab = 'pempCont') # quantile function plot(x = seq(.5, 1, .1), y = empCont$qempCont(seq(.5, 1, .1)), type = 'p', xlab = 'x', ylab = 'qempCont') # random sample from fitted distribution summary(empCont$r(100)) empCont$parameters
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