`do.made`

first aims at finding local dimesion estimates using nearest neighbor techniques based on
the first-order approximation of the probability mass function and then combines them to get a single global estimate. Due to the rate of convergence of such
estimate to be independent of assumed dimensionality, authors claim this method to be
*manifold-adaptive*.

```
est.made(
X,
k = round(sqrt(ncol(X))),
maxdim = min(ncol(X), 15),
combine = c("mean", "median", "vote")
)
```

X | an \((n\times p)\) matrix or data frame whose rows are observations. |
---|---|

k | size of neighborhood for analysis. |

maxdim | maximum possible dimension allowed for the algorithm to investigate. |

combine | method to aggregate local estimates for a single global estimate. |

a named list containing containing

- estdim
estimated global intrinsic dimension.

- estloc
a length-\(n\) vector estimated dimension at each point.

Farahmand AM, Szepesvári C, Audibert J (2007).
“Manifold-Adaptive Dimension Estimation.”
In *ICML*, volume 227 of *ACM International Conference Proceeding Series*, 265--272.

Kisung You

```
# \donttest{
## create a data set of intrinsic dimension 2.
X = aux.gensamples(dname="swiss")
## compare effect of 3 combining scheme
out1 = est.made(X, combine="mean")
out2 = est.made(X, combine="median")
out3 = est.made(X, combine="vote")
## print the results
line1 = paste0("* est.made : 'mean' estiamte is ",round(out1$estdim,2))
line2 = paste0("* est.made : 'median' estiamte is ",round(out2$estdim,2))
line3 = paste0("* est.made : 'vote' estiamte is ",round(out3$estdim,2))
cat(paste0(line1,"\n",line2,"\n",line3))
#> * est.made : 'mean' estiamte is 2
#> * est.made : 'median' estiamte is 2
#> * est.made : 'vote' estiamte is 1
# }
```