R: Estimate density from histogram - for different 'alpha'

smoothSplinesVal {robCompositions} | R Documentation

## Estimate density from histogram - for different `alpha`

### Description

As `smoothSplines`, `smoothSplinesVal` computes the density function that 'best' fits discretized distributional data, using B-spline basis functions, for different `alpha`.
Comparing and choosing an appropriate `alpha` is the ultimate goal.

### Usage

    smoothSplinesVal(
      k,
      l,
      alpha,
      data,
      xcp,
      knots,
      weights = matrix(1, dim(data)[1], dim(data)[2]),
      prior = "default",
      cores = 1
    )

### Arguments

`k` | smoothing splines degree
`l` | order of derivative in the penalization term
`alpha` | vector of weights for penalization
`data` | an object of class "matrix" containing data to be smoothed, row by row
`xcp` | vector of control points
`knots` | either vector of knots for the splines or a integer for the number of equispaced knots
`weights` | matrix of weights. If not gives, all data points will be weighted the same.
`prior` | prior used for zero-replacements. This must be one of "perks", "jeffreys", "bayes_laplace", "sq" or "default"
`cores` | number of cores for parallel execution

### Details

See `smoothSplines` for the description of the algorithm.

### Value

A list of three objects:

``alpha`` | the values of `alpha`
``J`` | the values of the functional evaluated in the minimizing
``CV-error`` | the values of the leave-one-out CV-error

### Author(s)

Alessia Di Blasi, Federico Pavone, Gianluca Zeni, Matthias Templ

### References

J. Machalova, K. Hron & G.S. Monti (2016): Preprocessing of centred logratio transformed density functions using smoothing splines. Journal of Applied Statistics, 43:8, 1419-1435.

### Examples

    SepalLengthCm <- iris$Sepal.Length
    Species <- iris$Species

    iris1 <- SepalLengthCm[iris$Species==levels(iris$Species)[1]]
    h1 <- hist(iris1, nclass = 12, plot = FALSE)

    ## Not run:
    midx1 <- h1$mids
    midy1 <- matrix(h1$density, nrow=1, ncol = length(h1$density), byrow=TRUE)
    knots <- 7
    sol1 <- smoothSplinesVal(k=3,l=2,alpha=10^seq(-4,4,by=1),midy1,midx1,knots,cores=1)

    ## End(Not run)

[Package robCompositions version 2.2.1 Index]