sample_rvtable: Sample A Random Variable Table
In leonawicz/rvtable: Random Variable Data Tables

Description Usage Arguments Details Value Examples

View source: R/sampling.R

Bootstrap resampling with replacement from a distribution of a random variable stored in an rvtable.

1 2	sample_rvtable(x, resample = FALSE, n = 10000, interp = TRUE, n.interp = 1e+05, decimals = NULL, density.args)

`x`	an `rvtable` object.
`resample`	if `x` is a sample-based rvtable, return `x` unchanged if `resample=FALSE`. Otherwise resample from the samples in the Val column. Ignored if rvtable is distribution-based.
`n`	bootstrap sample size.
`interp`	linearly interpolate between observed values of Val before sampling if Val is continuous. Ignored for discrete random variables.
`n.interp`	length of sequence of interpolated sampling points if. Ignored for discrete random variables or `interp=FALSE`.
`decimals`	number of decimal places for rounding samples. Ignored for discrete random variables.
`density.args`	optional arguments passed to `density`. If supplied, overrides the `density.args` attribute of `x`.

Resample an rvtable yielding a new rvtable. This is used to convert from a distribution-based rvtable, which has Val and Prob columns representing a distribution to one with only a Val column representing samples from a distribution. This is often used preceding plotting data in an rvtable so that samples may be passed to plot code rather than a representation of a distribution using values and associated probabilities. This function can also take a sample-based rvtable, in which case it will resample it if resample=TRUE. If is sample-based rvtable is passed and the random variable is continuous, the generation of a new continuous density prior to resampling can be controlled via density.args. All rvtable objects are either distribution-based or sample-based.

an rvtable object where the Val column represents samples and the Prob column is dropped.

x <- rvtable(rnorm(1000))
sample_rvtable(x, n=10)
x <- rvtable(sample(1:100, 50), discrete=TRUE)
y <- sample_rvtable(x, n=10)
sample_rvtable(y, n=8, resample=TRUE)