spatialShrunkenCentroids-methods {Cardinal} | R Documentation |
Performs spatially-aware nearest shrunken centroid clustering or classification on an imaging dataset. These methods use statistical regularization to shrink the t-statistics of the features toward 0 so that unimportant features are removed from the analysis. A Gaussian spatial kernel or an adaptive kernel based on bilateral filtering are used for spatial smoothing.
## S4 method for signature 'SImageSet,missing' spatialShrunkenCentroids(x, y, r = 1, k = 2, s = 0, method = c("gaussian", "adaptive"), iter.max=10, ...) ## S4 method for signature 'SImageSet,factor' spatialShrunkenCentroids(x, y, r = 1, s = 0, method = c("gaussian", "adaptive"), priors = table(y), ...) ## S4 method for signature 'SImageSet,character' spatialShrunkenCentroids(x, y, ...) ## S4 method for signature 'SpatialShrunkenCentroids' predict(object, newx, newy, ...)
x |
The imaging dataset to cluster. |
y |
A |
r |
The spatial neighborhood radius of nearby pixels to consider. This can be a vector of multiple radii values. |
k |
The number of clusters. This can be a vector to try different numbers of clusters. |
s |
The sparsity thresholding parameter by which to shrink the t-statistics. |
method |
The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) weights, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) weights. |
iter.max |
The maximum number of clustering iterations. |
priors |
Prior probabilities on the classes for classification. Improper priors will be normalized automatically. |
... |
Ignored. |
object |
The result of a previous call to |
newx |
An imaging dataset for which to calculate the predicted response from shrunken centroids. |
newy |
Optionally, a new response from which residuals should be calculated. |
An object of class SpatialShrunkenCentroids
, which is a ResultSet
, where each component of the resultData
slot contains at least the following components:
classes
:A factor indicating the predicted class for each pixel in the dataset.
centers
:A matrix of shrunken class centers.
time
:The amount of time the algorithm took to run.
r
:The neighborhood spatial smoothing radius.
k
:The number of clusters.
s
:The sparsity parameter.
method
:The type of spatial kernel used.
scores
:A matrix of discriminant scores.
probabilities
:A matrix of class probabilities.
tstatistics
:A matrix of shrunken t-statistics of the features.
sd
:The pooled within-class standard deviations for each feature.
iter
:The number of iterations performed.
Kylie A. Bemis
Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. (2003). Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays. Statistical Science, 18, 104-117.
Alexandrov, T., & Kobarg, J. H. (2011). Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics, 27(13), i230-i238. doi:10.1093/bioinformatics/btr246
set.seed(1) data <- matrix(c(NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, 0, 1, 1, NA, NA, NA, NA, NA, 1, 0, 0, 1, 1, NA, NA, NA, NA, NA, 0, 1, 1, 1, 1, NA, NA, NA, NA, 0, 1, 1, 1, 1, 1, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA), nrow=9, ncol=9) sset <- generateImage(data, range=c(200, 300), step=1) clust1 <- spatialShrunkenCentroids(sset, r=c(1,2), k=c(2,3), s=c(0,1), method="gaussian") clust2 <- spatialShrunkenCentroids(sset, r=c(1,2), k=c(2,3), s=c(0,1), method="adaptive") y <- factor(data[!is.na(data)], labels=c("black", "red")) class1 <- spatialShrunkenCentroids(sset, y, r=c(1,2), s=c(0,1), method="gaussian") class1 <- spatialShrunkenCentroids(sset, y, r=c(1,2), s=c(0,1), method="adaptive")