est.mle2 {Rdimtools} | R 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 |
k1 |
minimum neighborhood size, larger than 1. |
k2 |
maximum neighborhood size, smaller than |
Value
a named list containing containing
- estdim
estimated intrinsic dimension.
Author(s)
Kisung You
References
MacKay DJC, Ghahramani Z (2005). “Comments on 'Maximum Likelihood Estimation of Intrinsic Dimension' by E. Levina and P. Bickel (2004).” https://www.inference.org.uk/mackay/dimension/.
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))
[Package Rdimtools version 1.1.2 Index]