hmat {anomalyDetection} | R Documentation |
Display a histogram matrix for visual inspection of anomalous observation detection. The color of the blocks represents how anomalous each block is, where a lighter blue represents a more anomalous block. The size of the points indicate which values are driving the anomaly, with larger blocks representing more anomalous values.
hmat(data, input = "data", top = 20, order = "numeric", block_length = NULL, level_limit = 50, level_keep = 10, partial_block = TRUE, na.rm = FALSE, min_var = 0.1, max_cor = 0.9, action = "exclude", output = "both", normalize = FALSE)
data |
the data set (data frame or matrix) |
input |
the type of input data being passed to the function. |
top |
how many of the most anomalous blocks you would like to display (default 20) |
order |
whether to show the anomalous blocks in numeric order or in order of most anomalous to least anomalous (default is "numeric", other choice is "anomaly") |
block_length |
argument fed into |
level_limit |
argument fed into |
level_keep |
argument fed into |
partial_block |
argument fed into |
na.rm |
whether to keep track of missing values as part of the analysis or
ignore them (default |
min_var |
argument fed into |
max_cor |
argument fed into |
action |
argument fed into |
output |
argument fed into |
normalize |
argument fed into |
## Not run: # Data set input hmat(security_logs,block_length = 8) # Data Set input with top 10 blocks displayed hmat(security_logs, top = 10, block_length = 5) # State Vector Input tabulate_state_vector(security_logs, block_length = 6, level_limit = 20) %>% hmat(input = "SV") ## End(Not run)