| Type: | Package |
| Title: | Creating Empirical Distribution Functions |
| Version: | 0.2.0 |
| Date: | 2016-08-27 |
| Depends: | R (≥ 3.0.0) |
| Imports: | stats |
| Suggests: | knitr, rmarkdown |
| Description: | Easily creating empirical distribution functions from data: 'dfun', 'pfun', 'qfun' and 'rfun'. |
| VignetteBuilder: | knitr |
| License: | GPL-2 | GPL-3 |
| URL: | https://cran.r-project.org/package=edfun, https://github.com/talgalili/edfun/, https://www.r-statistics.com/tag/edfun/ |
| BugReports: | https://github.com/talgalili/edfun/issues |
| LazyData: | TRUE |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-08-27 10:36:51 UTC; junior |
| Author: | Tal Galili [aut, cre, cph] (https://www.r-statistics.com) |
| Maintainer: | Tal Galili <tal.galili@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2016-08-27 14:24:35 |
Creating Empirical Distribution Functions
Description
A function for creating a set of (one dimensional) empirical distribution functions (density, CDF, inv-CDF, and random number generator). This is either based on a vector of observations from the distribution, or a density function.
Usage
edfun(x, support = range(x), dfun, qfun_method = NULL, ...)
Arguments
x |
numeric vector of data or (in case density is not NULL) a sequance of values for which to evaluate the density function for creating the inv-CDF. Also, the rfun will be based on the inverse CDF on uniform distribution (inv-CDF(U[0,1]) - which is "better" than using sample, if we have the density). |
support |
a 2d numeric vector giving the boundaries of the distribution. Default is the range of x. This is used in qfun to decide how to work with extreme cases of q->0|1. |
dfun |
a density function. If supplied, this creates a different pfun (which now relies on integrate) and rfun (which will now rely on inv-CDF(U[0,1])). If missing, then it is created using density. If NULL then it is not created. |
qfun_method |
can get a quantile function to use (for example "quantile"), with the first parameter accepts the data (x) and the second accepts probs (numeric vector of probabilities with values in [0,1]). If it is NULL (the default) then the quantiles are estimated using approxfun from predicting the x values from the pfun(x) values. |
... |
ignored |
Value
A list with 4+ components: dfun, pfun, qfun and rfun. The 5th componont is pfun_integrate_dfun which is NUNLL if dfun is not supplied. If it is supplied, it returns a function that relies on integrate of dfun for returning pfun. Since this method is VERY slow, it is not returned within pfun. Instead, pfun will pre-compute pfun_integrate_dfun on all values of x.
Each component is a function to perform the usual tasks of distributions.
Examples
set.seed(2016-08-18)
x <- rnorm(100)
x_funs <- edfun(x)
x_funs$qfun(0) # -2.6
# for extreme cases, we can add the support vector
x_funs <- edfun(x, support = c(-Inf, Inf))
x_funs$qfun(0) # -Inf
f <- x_funs$dfun
curve(f, -2,2)
f <- x_funs$pfun
curve(f, -2,2)
f <- x_funs$qfun
curve(f, 0,1)
f <- x_funs$rfun
hist(f(1000))