Generate datapoints

Generates datapoints of chosen joint variates, according to posterior probabilities and posterior conditional probabilities.

Usage

rPr(n, Ynames, X = NULL, K, tails = NULL, mcsamples = NULL, parallel = NULL)

Arguments

n

Positive integer: number of samples to draw.

Ynames

Character vector: names of variates to draw jointly

X

List or data.table or NULL: set of values of variates on which we want to condition the joint probability for Y. If NULL (default), no conditioning is made. Any rows beyond the first are discarded

K

A "Knowledge" object produced by learn(). It can also be a path to a 'K.rds' file containing such object, or to a directory containing one.

tails

Named vector or list, or NULL (default). The names must match some or all of the variates in arguments X. For variates in this list, the probability conditional is understood in a semi-open interval sense: \(X \le x\) or \(X \ge x\), an so on. See analogous argument in Pr().

mcsamples

Vector of integers, or 'all', or NULL (default): which Monte Carlo samples calculated by the learn() function should be used to draw the variate values. The default is to choose a random subset if n is smaller than their number, otherwise to recycle them as necessary.

parallel

Not used: this function does not use parallelization.

Value

A data frame of joint draws of the variates Ynames from the posterior distribution, conditional on X. The row names of the data frame report the Monte Carlo sample (from learn()) used for that draw, and the total number of draws from that sample so far.

Details

This function generates datapoints according to the posterior probability \(\mathrm{Pr}(Y = y \vert X = x, K)\) or \(\mathrm{Pr}(Y = y \vert X \le x, K)\) or combinations thereof, for the variates specified in the argument Y, and conditional on the variate values specified in the argument X. It is somewhat analogous to the rxxx-variants of R distribution functions. If X is omitted or NULL, then the posterior probability \(\mathrm{Pr}(Y | K)\) is used. Each variate in the argument X can be specified either as a point-value \(X = x\) or as a left-open interval \(X \le x\) or as a right-open interval \(X \ge x\), through the argument tails.

See also

learn(), which generates the K objects required by qPr().

Pr() to calculate joint and conditional probabilities.

qPr() to calculate quantiles.

Examples

## Load the example `K`nowledge object calculated from the "penguins" dataset;
## variates: 'species' and 'bill_len'
K <- Kexample

## ## Example 1:
## Generate 10 values of the 'species' variate,
## according to the frequency distribution estimated from the data

datapoints <- rPr(
  n = 10,
  Ynames = 'species',
  K = K
)

c(datapoints)
#> $species
#>  [1] "Adelie"    "Gentoo"    "Adelie"    "Chinstrap" "Adelie"    "Gentoo"   
#>  [7] "Gentoo"    "Adelie"    "Chinstrap" "Gentoo"   
#> 


## ## Example 2:
## Generate 5 joint values of the 'species' and 'bill_len' variates.

datapoints <- rPr(
  n = 5,
  Ynames = c('species', 'bill_len'),
  K = K
)

print(datapoints, row.names = FALSE) ## row names give MCMC information
#>    species bill_len
#>     Adelie     39.0
#>     Gentoo     44.6
#>     Gentoo     48.9
#>  Chinstrap     52.0
#>     Gentoo     48.0


## ## Example 3:
## Generate 5 values of the 'species' variate,
## for the subpopulation of penguins having bill length shorter than 40 mm

datapoints <- rPr(
  n = 5,
  Ynames = 'species',
  X = data.frame(bill_len = 40),
  tails = list(bill_len = 'lower'),
  K = K
)

c(datapoints)
#> $species
#> [1] "Adelie" "Adelie" "Adelie" "Adelie" "Adelie"
#>