dirichlet_sample: Function which performs Dirichlet sampling

In archetypal: Finds the Archetypal Analysis of a Data Frame

Function which performs Dirichlet sampling

Description

It uses Dirichlet weights for creating sub-samples of initial data set.

Usage

dirichlet_sample(in_data = NULL, sample_size = NULL,
replacement = NULL, rseed = NULL)

Arguments

in_data	The initial data frame that must be re-sampled. It must contain: an ID variable the variables of interest a grouping variable
sample_size	An integer for the size of the new sample
replacement	A logical input: TRUE/FALSE if replacement should be used or not, respectively
rseed	The random seed that will be used for setting initial A matrix. Useful for reproducible results

Value

It returns a data frame with exactly the same variables as the initial one, except that group variable has now only the given value from input data frame.

Author(s)

David Midgley

See Also

grouped_resample

Examples

## Load absolute temperature data set:
data("AbsoluteTemperature")
df=AbsoluteTemperature
## Find portions for climate zones
pcs=table(df$z)/dim(df)[1]
## Choose the approximate size of the new sample and compute resample sizes
N=1000
resamplesizes=as.integer(round(N*pcs))
sum(resamplesizes)
## Create the grouping matrix
groupmat=data.frame("Group_ID"=1:4,"Resample_Size"=resamplesizes)
groupmat
## Dirichlet resampling:
resample_dirichlet <- grouped_resample(in_data = df,grp_vector = "z",
                      grp_matrix = groupmat,replace = FALSE,
                      option = "Dirichlet", rseed = 20191220)
cat(dim(resample_dirichlet),"\n")

archetypal documentation built on May 29, 2024, 8:46 a.m.