Description Usage Arguments Details Value References See Also Examples

View source: R/find_HDoutliers.R

Detect anomalies in high dimensional data. This is a modification of
`HDoutliers`

.

1 2 | ```
find_HDoutliers(data, maxrows = 1000, alpha = 0.01,
method = c("HDadv", "hdr", "ahull"))
``` |

`data` |
A vector, matrix, or data frame consisting of numeric and/or categorical variables. |

`maxrows` |
If the number of observations is greater than |

`alpha` |
Threshold for determining the cutoff for outliers. Observations are considered
outliers if they fall in the |

`method` |
Outlier detection method used for detecting outlier in the high dimensional space. |

If the number of observations exceeds `maxrows`

, the data is first partitioned into lists
associated with *exemplars* and their *members* within `radius`

of each *exemplar*, to
reduce the number of k-nearest neighbor computations required for outlier detection.

The indexes of the observations determined to be outliers.

Wilkinson, L. (2018), 'Visualizing big data outliers through distributed aggregation', IEEE transactions on visualization and computer graphics 24(1), 256-266.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
require(ggplot2)
set.seed(1234)
data <- c(rnorm(1000, mean = -6), 0, rnorm(1000, mean = 6))
outliers <- find_HDoutliers(data)
display_HDoutliers(data,outliers )
set.seed(1234)
n <- 1000 # number of observations
nout <- 10 # number of outliers
typical_data <- tibble::as.tibble(matrix(rnorm(2*n), ncol = 2, byrow = TRUE))
out <- tibble::as.tibble(matrix(5*runif(2*nout,min=-5,max=5), ncol = 2, byrow = TRUE))
data <- rbind(out, typical_data )
outliers <- find_HDoutliers(data)
display_HDoutliers(data, outliers)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.