Perform a transformation of pdqr-function(s) (which assumed to be independent).

```
form_trans(f_list, trans, ..., method = "random", n_sample = 10000,
args_new = list())
form_trans_self(f, trans, ..., method = "random", args_new = list())
```

f_list | A list consisting from pdqr-function(s) and/or single number(s). Should have at least one pdqr-function (see Details). |
---|---|

trans | Transformation function. Should take as many (vectorized)
arguments as there are elements in |

... | Extra arguments to |

method | Transformation method. One of "random" or "bruteforce". |

n_sample | Number of elements to sample. |

args_new | |

f | A pdqr-function. |

A pdqr-function for transformed random variable.

`form_trans_self()`

is a thin wrapper for `form_trans()`

that
accepts a single pdqr-function instead of a list of them.

Class of output is chosen as class of first pdqr-function in
`f_list`

. Type of output is chosen to be "discrete" in case
all input pdqr-functions have "discrete" type, and "continuous" otherwise.

Method "random" performs transformation using random generation of samples:

**Generates a sample of size**(if element is single number, it is repeated`n_sample`

from every element of`f_list`

`n_sample`

times).**Calls**with all generated samples (in order aligned with`trans`

`f_list`

).**Note**that output should be either numeric or logical and have`n_sample`

elements (one for each combination of input values in "vectorized" fashion). So, for example, using`sum`

directly is not possible as it returns only single number.**Creates output pdqr-function**. If output is logical, probability of being true is estimated as share of`TRUE`

in output, and boolean pdqr-function is created (type "discrete" with "x" values equal to 0 and 1, and probabilities of being false and true respectively). If output is numeric, one of`new_*()`

(suitable for output class) is called with arguments from`args_new`

list.

Method "bruteforce":

**Retypes input**pdqr-function to "discrete" type (using "piecelin" method).**Computes output for every combination of "x" values**(probability of which will be a product of corresponding probabilities).**Creates pdqr-function of type "discrete"**with suitable`new_*()`

function.**Possibly retypes to "continuous" type**if output should have it (also with "piecelin" method).

**Notes** about "bruteforce" method:

Its main advantage is that it is not random.

It may start to be very memory consuming very quickly.

It is usually useful when type of output function is "discrete". In case of "continuous" type, retyping from "discrete" to "continuous" might introduce big errors.

Used "discrete" probabilities shouldn't be very small because they will be directly multiplied, which might cause numerical accuracy issues.

Pdqr methods for S3 group generic functions
for more accurate implementations of most commonly used functions. Some of
them are direct (without randomness) and some of them use `form_trans()`

with "random" method.

`form_regrid()`

to increase/decrease granularity of pdqr-functions for method
"bruteforce".

Other form functions:
`form_estimate()`

,
`form_mix()`

,
`form_regrid()`

,
`form_resupport()`

,
`form_retype()`

,
`form_smooth()`

,
`form_tails()`

```
# Default "random" transformation
d_norm <- as_d(dnorm)
## More accurate result would give use of `+` directly with: d_norm + d_norm
d_norm_2 <- form_trans(list(d_norm, d_norm), trans = `+`)
plot(d_norm_2)
#> Density function of continuous type
#> Support: ~[95.80216, 104.85072] (511 intervals)
## Output of `trans` can be logical. Next example is random version of
## `d_norm >= 0`.
form_trans(list(d_norm, 0), trans = `>=`)
#> Probability mass function of discrete type
#> Support: [0, 1] (2 elements, probability of 1: 0.4952)
# Transformation with "bruteforce" method
power <- function(x, n = 1) {
x^n
}
p_dis <- new_p(
data.frame(x = 1:3, prob = c(0.1, 0.2, 0.7)),
type = "discrete"
)
p_dis_sq <- form_trans_self(
p_dis, trans = power, n = 2, method = "bruteforce"
)
meta_x_tbl(p_dis_sq)
#> x prob cumprob
#> 1 1 0.1 0.1
#> 2 4 0.2 0.3
#> 3 9 0.7 1.0## Compare with "random" method
p_dis_sq_rand <- form_trans_self(p_dis, trans = power, n = 2)
meta_x_tbl(p_dis_sq_rand)
#> x prob cumprob
#> 1 1 0.1010 0.1010
#> 2 4 0.1998 0.3008
#> 3 9 0.6992 1.0000
# `form_trans_self()` is a wrapper for `form_trans()`
form_trans_self(d_norm, trans = function(x) {
2 * x
})
#> Density function of continuous type
#> Support: ~[-7.70272, 7.89026] (511 intervals)
```