# RtoDPQ.LC

##### Default procedure to fill slots d,p,q given r for Lebesgue decomposed distributions

function to do get empirical density, cumulative distribution and quantile function from random numbers

- Keywords
- distribution, arith, math

##### Usage

```
RtoDPQ.LC(r, e = getdistrOption("RtoDPQ.e"),
n = getdistrOption("DefaultNrGridPoints"), y = NULL)
```

##### Arguments

- r
the random number generator

- e
\(10^e\) numbers are generated, a higher number leads to a better result.

- n
The number of grid points used to create the approximated functions, a higher number leads to a better result.

- y
a (numeric) vector or

`NULL`

##### Details

RtoDPQ.LC generates \(10^e\) random numbers, by default $$e = RtoDPQ.e$$.
Replicates are assumed to be part of the discrete part, unique values to be
part of the a.c. part of the distribution. For the replicated ones,
we generate a discrete distribution by a call to `DiscreteDistribution`

.

For the a.c. part, similarly to `RtoDPQ`

we have an optional parameter `y`

for using N. Horbenko's quantile trick: i.e.; on an equally spaced grid `x.grid`

on [0,1], apply
`f(q(x)(x.grid))`

, write the result to `y`

and use these
values instead of simulated ones.

The a.c. density is formed on the basis of \(n\)
points using approxfun and density (applied to the unique values), by default $$n = DefaultNrGridPoints$$.
The cumulative distribution function is based on all random variables,
and, as well as the quantile function, is also created on the basis of \(n\) points using
`approxfun`

and `ecdf`

. Of course, the results are usually not exact as they rely on random numbers.

##### Value

`RtoDPQ.LC`

returns an object of class `UnivarLebDecDistribution`

.

##### Note

Use `RtoDPQ`

for absolutely continuous and `RtoDPQ.d`

for discrete distributions.

##### See Also

##### Examples

```
# NOT RUN {
rn2 <- function(n)ifelse(rbinom(n,1,0.3),rnorm(n)^2,rbinom(n,4,.3))
x <- RtoDPQ.LC(r = rn2, e = 4, n = 512)
plot(x)
# returns density, cumulative distribution and quantile function of
# squared standard normal distribution
d.discrete(x)(4)
x2 <- RtoDPQ.LC(r = rn2, e = 5, n = 1024) # for a better result
plot(x2)
# }
```

*Documentation reproduced from package distr, version 2.7.0, License: LGPL-3*