allProb: Outlier probabilities for all observations

In probout: Unsupervised Multivariate Outlier Probabilities for Large Datasets

Description

Outlier probabilities for all of the data, obtained by assigning to each observation the probabilty of the its associated leader partition.

Usage

allProb( leaderInstance, partprob)

Arguments

leaderInstance	A single component from a call to leader, giving leader algorithm results for one value of the partitioning radius.
partprob	A vector of probabilities for each partition in leaderInstance.

Value

A vector of probabilities for each observation in the data underlying leaderInstance. Each observation inherits the probability of its associated partition.

See Also

leader, partProb

Examples

 set.seed(0)

 lead <- leader(faithful)
 nlead <- length(lead[[1]]$partitions)

# repeat multiple times to account for randomness
 ntimes <- 100
 probs <- matrix( NA, nlead, ntimes)
 for (i in 1:ntimes) {
    probs[,i] <- partProb( simData(lead[[1]]), method = "distance")
 }

# median probability for each partition
 partprobs <- apply( probs, 1, median)

 quantile(partprobs)

# plot leaders with outlier probability > .95
 plot( faithful[,1], faithful[,2], pch = 16, cex = .5,
       main = "red : instances with outlier probability > .95")
 allprobs <- allProb( lead[[1]], partprobs)
 out <- allprobs > .95
 points( faithful[out,1], faithful[out,2], pch = 8, cex = 1, col = "red")

probout documentation built on Feb. 11, 2022, 5:10 p.m.