Fit Empirical Distribution

fit_empirical(x)

Arguments

x integer or double vector

Value

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.

Examples

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))#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
#>    1.00    3.00    5.00    4.71    7.00   10.00
empDis$parameters#> 0 1 2 3 4 5 6 7 8 9 10 #> 0.01 0.08 0.14 0.09 0.08 0.24 0.07 0.11 0.07 0.09 0.02 set.seed(562) x <- rexp(100, 1/5) empCont <- fit_empirical(x) # probability density function plot(x = 0:10, y = empCont$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$r(100))#> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 1.394 1.394 4.205 4.871 4.205 32.200 empCont$parameters#> (-0.0217,2.81]     (2.81,5.6]      (5.6,8.4]     (8.4,11.2]      (11.2,14]
#>           0.42           0.27           0.12           0.05           0.06
#>      (14,16.8]    (16.8,19.6]    (19.6,22.4]    (22.4,25.2]      (25.2,28]
#>           0.01           0.04           0.01           0.01           0.00
#>      (28,30.8]    (30.8,33.6]
#>           0.00           0.01