Package 'lookout'

Plots outliers identified by lookout algorithm.

Description

Scatterplot of two columns from the data set with outliers highlighted.

Usage

## S3 method for class 'lookoutliers'
autoplot(object, columns = 1:2, ...)

Arguments

object	The output of the function 'lookout'.
columns	Which columns of the original data to plot (specified as either numbers or strings)
...	Other arguments currently ignored.

Value

A ggplot object.

Examples

X <- rbind(
  data.frame(x = rnorm(500),
             y = rnorm(500)),
  data.frame(x = rnorm(5, mean = 10, sd = 0.2),
             y = rnorm(5, mean = 10, sd = 0.2))
)
lo <- lookout(X)
autoplot(lo)

---

Plots outlier persistence for a range of significance levels.

Description

This function plots outlier persistence for a range of significance levels using the algorithm lookout, an outlier detection method that uses leave-one-out kernel density estimates and generalized Pareto distributions to find outliers.

Usage

## S3 method for class 'persistingoutliers'
autoplot(object, alpha = object$alpha, ...)

Arguments

object	The output of the function 'persisting_outliers'.
alpha	The significance levels to plot.
...	Other arguments currently ignored.

Value

A ggplot object.

Examples

X <- rbind(
  data.frame(
    x = rnorm(500),
    y = rnorm(500)
  ),
  data.frame(
    x = rnorm(5, mean = 10, sd = 0.2),
    y = rnorm(5, mean = 10, sd = 0.2)
  )
)
plot(X, pch = 19)
outliers <- persisting_outliers(X, unitize = FALSE)
autoplot(outliers)

---

Identifies bandwidth for outlier detection.

Description

This function identifies the bandwidth that is used in the kernel density estimate computation. The function uses topological data analysis (TDA) to find the badnwidth.

Usage

find_tda_bw(X, fast)

Arguments

X	The input data in a dataframe, matrix or tibble format.
fast	If set to TRUE, makes the computation faster by sub-setting the data for the bandwidth calculation.

Value

The bandwidth

Examples

X <- rbind(
  data.frame(x = rnorm(500),
             y = rnorm(500)),
  data.frame(x = rnorm(5, mean = 10, sd = 0.2),
             y = rnorm(5, mean = 10, sd = 0.2))
)
find_tda_bw(X, fast = TRUE)

---

Identifies outliers using the algorithm lookout.

Description

This function identifies outliers using the algorithm lookout, an outlier detection method that uses leave-one-out kernel density estimates and generalized Pareto distributions to find outliers.

Usage

lookout(X, alpha = 0.05, unitize = TRUE, bw = NULL, gpd = NULL, fast = TRUE)

Arguments

X	The input data in a dataframe, matrix or tibble format.
alpha	The level of significance. Default is 0.05.
unitize	An option to normalize the data. Default is TRUE, which normalizes each column to [0,1].
bw	Bandwidth parameter. Default is NULL as the bandwidth is found using Persistent Homology.
gpd	Generalized Pareto distribution parameters. If 'NULL' (the default), these are estimated from the data.
fast	If set to TRUE, makes the computation faster by sub-setting the data for the bandwidth calculation.

Value

A list with the following components:

outliers	The set of outliers.
outlier_probability	The GPD probability of the data.
outlier_scores	The outlier scores of the data.
bandwidth	The bandwdith selected using persistent homology.
kde	The kernel density estimate values.
lookde	The leave-one-out kde values.
gpd	The fitted GPD parameters.

Examples

X <- rbind(
  data.frame(x = rnorm(500),
             y = rnorm(500)),
  data.frame(x = rnorm(5, mean = 10, sd = 0.2),
             y = rnorm(5, mean = 10, sd = 0.2))
)
lo <- lookout(X)
lo
autoplot(lo)

---

Identifies outliers in univariate time series using the algorithm lookout.

Description

This is the time series implementation of lookout.

Usage

lookout_ts(x, alpha = 0.05)

Arguments

x	The input univariate time series.
alpha	The level of significance. Default is 0.05.

Value

A lookout object.

See Also

lookout

Examples

set.seed(1)
x <- arima.sim(list(order = c(1,1,0), ar = 0.8), n = 200)
x[50] <- x[50] + 10
plot(x)
lo <- lookout_ts(x)
lo

---

Computes outlier persistence for a range of significance values.

Description

This function computes outlier persistence for a range of significance values, using the algorithm lookout, an outlier detection method that uses leave-one-out kernel density estimates and generalized Pareto distributions to find outliers.

Usage

persisting_outliers(
  X,
  alpha = seq(0.01, 0.1, by = 0.01),
  st_qq = 0.9,
  unitize = TRUE,
  num_steps = 20
)

Arguments

X	The input data in a matrix, data.frame, or tibble format. All columns should be numeric.
alpha	Grid of significance levels.
st_qq	The starting quantile for death radii sequence. This will be used to compute the starting bandwidth value.
unitize	An option to normalize the data. Default is TRUE, which normalizes each column to [0,1].
num_steps	The length of the bandwidth sequence.

Value

A list with the following components:

out	A 3D array of N x num_steps x num_alpha where N denotes the number of observations, num_steps denote the length of the bandwidth sequence and num_alpha denotes the number of significance levels. This is a binary array and the entries are set to 1 if that observation is an outlier for that particular bandwidth and significance level.
bw	The set of bandwidth values.
gpdparas	The GPD parameters used.
lookoutbw	The bandwidth chosen by the algorithm lookout using persistent homology.

Examples

X <- rbind(
  data.frame(x = rnorm(500),
             y = rnorm(500)),
  data.frame(x = rnorm(5, mean = 10, sd = 0.2),
             y = rnorm(5, mean = 10, sd = 0.2))
)
plot(X, pch = 19)
outliers <- persisting_outliers(X, unitize = FALSE)
outliers
autoplot(outliers)

---