run.analsis: Run t-SNE clustering algorithm

In jiongyi-cao/tSneClstGRF: t-SNE Clustering analysis for causal forest object

Run t-SNE clustering algorithm

Description

This function read previous proceeded causal forest object and get 2D projection of covariates space based on t-SNE algorithm

Usage

run.analsis(object, n, distance = NULL, perp = NULL)

Arguments

object	Causal forest object that has been created.
n	Size of subsample from whole dataset. Suggest no larger than few thousands.
distance	Distance metrix of covariate space either provided by user or default: gower distance
perplexity	Perplexity parameter (should no larger than 3 * perplexity < nrow(X) - 1, see details for interpretation).

Value

A t-SNE clustering object with the follwing elements:

index
distance
result

References

Maaten, L. Van Der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

Examples

tsne_obj <- run.analsis(my_cf)

jiongyi-cao/tSneClstGRF documentation built on June 2, 2022, 11:40 p.m.