as.BDataPDFs: Converts to representations as Probability Density functions

In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

Converts to representations as Probability Density functions

Description

A probability density function or Lebesgue density of a measure can only be computed for Lebesgue continuous distributions. As a dataset of BayesSpace elements (BData) they can be encoded as BDataPDFs objects. This function converts other representations to this representation.

Usage

as.BDataPDFs(x, ..., basis = getBayesSpaceBasis(x))

## S3 method for class 'BDataDensities'
as.BDataPDFs(x, ..., basis = getBayesSpaceBasis(x))

## S3 method for class 'BDataLogDensities'
as.BDataPDFs(x, ..., basis = getBayesSpaceBasis(x))

## S3 method for class 'BDataLogPDFs'
as.BDataPDFs(x, ..., basis = getBayesSpaceBasis(x))

## S3 method for class 'BDataPDFs'
as.BDataPDFs(x, ..., basis)

## S3 method for class 'BDataDensityHistograms'
as.BDataPDFs(x, ..., background = 0, basis = getBayesSpaceBasis(x))

## S3 method for class 'BDataFrequencyHistograms'
as.BDataPDFs(x, ..., background = 0, basis = getBayesSpaceBasis(x))

## S3 method for class 'BDataSamples'
as.BDataPDFs(
  x,
  ...,
  method = c("Fisher", "kde"),
  basis = getBayesSpaceBasis(x)
)

## S3 method for class 'BDataSplines'
as.BDataPDFs(x, ..., basis = getBayesSpaceBasis(x))

Arguments

x	a BData object that shall be converted to probability density function (Lebesgue density representation)
...	further parameters for generic use
basis	the BayesSpaceBasis to be used with the new object
background	the background density to be added in order to avoid 0 densities e.g. in case of histograms for empty classes or regions outside of the histogram breaks.
method	a character string defining the method of computation of the density form the data. "Fisher" stands for Fisher scoring and tries to fit an exponential family model given by the subspace of the BayesSpace. It might fail for certain datasets. See [FisherScoringEstimation] for further method parameters. "kde" stands for kernel density estimation. See [kdeDensity] for further possible parameters.

Value

a BDataPDFs object

Methods (by class)

• as.BDataPDFs(BDataDensities): method for BDataDensities multiplies with reference measure density

• as.BDataPDFs(BDataLogDensities): method for BDataLogDensities exponentiates and multiplies with reference measure density

• as.BDataPDFs(BDataLogPDFs): method for BDataLogPDFs exponentiates and multiplies with reference measure density

• as.BDataPDFs(BDataPDFs): method for BDataPDFs possibly changes the basis

• as.BDataPDFs(BDataDensityHistograms): method for BDataDensityHistograms interprets histogram as density.

• as.BDataPDFs(BDataFrequencyHistograms): method for BDataFrequencyHistograms

• as.BDataPDFs(BDataSamples): method for BDataSamples

• as.BDataPDFs(BDataSplines): method for BDataSplines

See Also

[BDataPDFs]

Other BayesSpaceConverters: as.BDataDensities(), as.BDataDensityHistograms(), as.BDataFrequencyHistograms(), as.BDataHistograms(), as.BDataLogDensities(), as.BDataLogPDFs(), as.BDataSamples(), as.BDataSplines(), idt.BData(), toHistogram()

Examples

X <- BDataDensities(list(\(x)(1),\(x)(x)),basis=B2N01Basis5)
Xpdfs = as.BDataPDFs(X)
Xpdfs
X1 = as.BDataPDFs(as.BDataLogDensities(X))
X2 = as.BDataPDFs(as.BDataLogPDFs(X))
X3 = as.BDataPDFs(Xpdfs)
set.seed(47)
Xs = BDataSamples(replicate(7,runif(200),simplify=FALSE),B2Unif01Basis5)
# X4 = as.BDataPDFs(Xs) # TODO
X5 = as.BDataPDFs(as.BDataFrequencyHistograms(Xs,breaks=seq(0,1,by=0.1)))
X6 = as.BDataPDFs(as.BDataDensityHistograms(Xs,breaks=seq(0,1,by=0.1)^2))
# plot(Xpdfs)

DistributionalDDA documentation built on Oct. 20, 2026, 5:06 p.m.