dirichlet_sample {archetypal} | R Documentation |
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:
|
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
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")
[Package archetypal version 1.3.1 Index]