Transform pdqr-function

Source: R/form_trans.R

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())

Arguments

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 f_list or a single argument for form_trans_self(). Should return numeric or logical values.
...

Extra arguments to trans.
method

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

Number of elements to sample.
args_new

List of extra arguments for new_*() to control density().
f

A pdqr-function.

Value

A pdqr-function for transformed random variable.

Details

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 n_sample from every element of f_list (if element is single number, it is repeated n_sample times).

  • Calls trans with all generated samples (in order aligned with 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.

See also

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()

Examples

# 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)

lines(as_d(dnorm, sd = sqrt(2)), col = "red")


## Input list can have single numbers
form_trans(list(d_norm, 100), trans = `+`)

#> 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)