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

est.mle2R Documentation

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 Sept. 23, 2022, 1:06 a.m.