estimate_mle2: Maximum Likelihood Esimation with Poisson Process and Bias...

In Rdimtools: Dimension Reduction and Estimation Methods

Maximum Likelihood Esimation with Poisson Process and Bias Correction

Description

Authors argue that the approach proposed in est.mle1 is empirically bias-prone in that the averaging of sample statistics over all data points is taken to be a harmonic manner.

Usage

est.mle2(X, k1 = 10, k2 = 20)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations.
k1	minimum neighborhood size, larger than 1.
k2	maximum neighborhood size, smaller than n.

Value

a named list containing containing

estdim

estimated intrinsic dimension.

Author(s)

Kisung You

References

\insertRef

mackay_comments_2005Rdimtools

Examples

## create example data sets with intrinsic dimension 2
X1 = aux.gensamples(dname="swiss")
X2 = aux.gensamples(dname="ribbon")
X3 = aux.gensamples(dname="saddle")

## acquire an estimate for intrinsic dimension
out1 = est.mle2(X1)
out2 = est.mle2(X2)
out3 = est.mle2(X3)

line1 = paste0("* est.mle2 : dimension of 'swiss'  data is ",round(out1$estdim,2))
line2 = paste0("* est.mle2 : dimension of 'ribbon' data is ",round(out2$estdim,2))
line3 = paste0("* est.mle2 : dimension of 'saddle' data is ",round(out3$estdim,2))
cat(paste0(line1,"\n",line2,"\n",line3))

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.