pointdensity  R Documentation 
Calculate the local density at each data point as either the number of
points in the epsneighborhood (as used in dbscan()
) or perform kernel density
estimation (KDE) using a uniform kernel. The function uses a kdtree for fast
fixedradius nearest neighbor search.
pointdensity( x, eps, type = "frequency", search = "kdtree", bucketSize = 10, splitRule = "suggest", approx = 0 )
x 
a data matrix. 
eps 
radius of the epsneighborhood, i.e., bandwidth of the uniform kernel). 
type 

search, bucketSize, splitRule, approx 
algorithmic parameters for

dbscan()
estimates the density around a point as the number of points in the
epsneighborhood of the point (including the query point itself).
Kernel density estimation (KDE) using a uniform kernel, which is just this point
count in the epsneighborhood divided by (2 eps n), where
n is the number of points in x
.
Points with low local density often indicate noise (see e.g., Wishart (1969) and Hartigan (1975)).
A vector of the same length as data points (rows) in x
with
the count or density values for each data point.
Michael Hahsler
Wishart, D. (1969), Mode Analysis: A Generalization of Nearest Neighbor which Reduces Chaining Effects, in Numerical Taxonomy, Ed., A.J. Cole, Academic Press, 282311.
John A. Hartigan (1975), Clustering Algorithms, John Wiley & Sons, Inc., New York, NY, USA.
frNN()
, stats::density()
.
Other Outlier Detection Functions:
glosh()
,
kNNdist()
,
lof()
set.seed(665544) n < 100 x < cbind( x=runif(10, 0, 5) + rnorm(n, sd = 0.4), y=runif(10, 0, 5) + rnorm(n, sd = 0.4) ) plot(x) ### calculate density d < pointdensity(x, eps = .5, type = "density") ### density distribution summary(d) hist(d, breaks = 10) ### plot with point size is proportional to Density plot(x, pch = 19, main = "Density (eps = .5)", cex = d*5) ### Wishart (1969) single link clustering after removing lowdensity noise # 1. remove noise with low density f < pointdensity(x, eps = .5, type = "frequency") x_nonoise < x[f >= 5,] # 2. use singlelinkage on the nonnoise points hc < hclust(dist(x_nonoise), method = "single") plot(x, pch = 19, cex = .5) points(x_nonoise, pch = 19, col= cutree(hc, k = 4) + 1L)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.