Title: Functions and Data Sets for "That's Weird: Anomaly Detection Using R" by Rob J Hyndman
Version: 1.0.2
Description: All functions and data sets required for the examples in the book Hyndman (2024) "That's Weird: Anomaly Detection Using R" https://OTexts.com/weird/. All packages needed to run the examples are also loaded.
Imports: aplpack, broom, cli (≥ 1.0.0), crayon (≥ 1.3.4), dbscan, dplyr (≥ 0.7.4), evd, ggplot2 (≥ 3.1.1), grDevices, interpolation, ks, purrr (≥ 0.2.4), rlang, robustbase, rstudioapi (≥ 0.7), stray, tibble (≥ 1.4.2)
License: GPL-3
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.0
Depends: R (≥ 4.1.0)
Suggests: mgcv, outliers, testthat (≥ 3.0.0), tidyr
Config/testthat/edition: 3
URL: https://pkg.robjhyndman.com/weird-package/, https://github.com/robjhyndman/weird-package
BugReports: https://github.com/robjhyndman/weird-package/issues
NeedsCompilation: no
Packaged: 2024-01-24 06:27:01 UTC; hyndman
Author: Rob Hyndman ORCID iD [aut, cre, cph], RStudio [cph]
Maintainer: Rob Hyndman <Rob.Hyndman@monash.edu>
Repository: CRAN
Date/Publication: 2024-01-24 14:50:02 UTC

weird: Functions and Data Sets for "That's Weird: Anomaly Detection Using R" by Rob J Hyndman

Description

All functions and data sets required for the examples in the book Hyndman (2024) "That's Weird: Anomaly Detection Using R" https://OTexts.com/weird/. All packages needed to run the examples are also loaded.

Author(s)

Maintainer: Rob Hyndman Rob.Hyndman@monash.edu (ORCID) [copyright holder]

Other contributors:

See Also

Useful links:

Convert data frame or matrix object to kde class

Description

A density specified as a data frame or matrix can be converted to a kde object. This is useful for plotting the density using autoplot.kde. As kde objects are defined on a grid, the density values are interpolated based on the points in the data frame or matrix.

Usage

as_kde(object, density_column, ngrid, ...)

Arguments

object

Data frame or matrix with numerical columns, where one column (specified by density_column) contains the density values, and the remaining columns define the points at which the density is evaluated.

density_column

Name of the column containing the density values, specified as a bare expression. If missing, the last column is used.

ngrid

Number of points to use for the grid in each dimension. Default is 10001 for univariate densities and 101 for multivariate densities.

...

Additional arguments are ignored.

Value

An object of class "kde"

Author(s)

Rob J Hyndman

Examples

tibble(y = seq(-4, 4, by = 0.01), density = dnorm(y)) |>
  as_kde()

Produce ggplot of densities in 1 or 2 dimensions

Description

Produce ggplot of densities in 1 or 2 dimensions

Usage

## S3 method for class 'kde'
autoplot(
  object,
  prob = seq(9)/10,
  fill = FALSE,
  show_hdr = FALSE,
  show_points = FALSE,
  show_mode = FALSE,
  show_lookout = FALSE,
  color = "#00659e",
  palette = hdr_palette,
  alpha = ifelse(fill, 1, min(1, 1000/NROW(object$x))),
  ...
)

Arguments

object

Probability density function as estimated by ks::kde().

prob

Probability of the HDR contours to be drawn (for a bivariate plot only).

fill

If TRUE, and the density is bivariate, the bivariate contours are shown as filled regions rather than lines.

show_hdr

If TRUE, and the density is univariate, then the HDR regions specified by prob are shown as a ribbon below the density.

show_points

If TRUE, then individual points are plotted.

show_mode

If TRUE, then the mode of the distribution is shown.

show_lookout

If TRUE, then the observations with lookout probabilities less than 0.05 are shown in red.

color

Color used for mode and HDR contours. If palette = hdr_palette, it is also used as the basis for HDR regions.

palette

Color palette function to use for HDR filled regions (if fill is TRUE or show_hdr is TRUE).

alpha

Transparency of points. When fill is FALSE, defaults to min(1, 1000/n), where n is the number of observations. Otherwise, set to 1.

...

Additional arguments are currently ignored.

Details

This function produces a ggplot of the density estimate produced by ks::kde(). For univariate densities, it produces a line plot of the density function, with an optional ribbon showing some highest density regions (HDRs) and/or the observations. For bivariate densities, it produces a contour plot of the density function, with the observations optionally shown as points. The mode can also be drawn as a point with the HDRs. For bivariate densities, the combination of fill = TRUE, show_points = TRUE, show_mode = TRUE, and prob = c(0.5, 0.99) is equivalent to an HDR boxplot. For univariate densities, the combination of show_hdr = TRUE, show_points = TRUE, show_mode = TRUE, and prob = c(0.5, 0.99) is equivalent to an HDR boxplot.

Value

A ggplot object.

Author(s)

Rob J Hyndman

Examples

# Univariate density
c(rnorm(500), rnorm(500, 4, 1.5)) |>
  kde() |>
  autoplot(show_hdr = TRUE, prob= c(0.5, 0.95), color = "#c14b14")
ymat <- tibble(y1 = rnorm(5000), y2 = y1 + rnorm(5000))
ymat |>
  kde(H = kde_bandwidth(ymat)) |>
  autoplot(show_points = TRUE, alpha = 0.1, fill = TRUE)

Cricket batting data for international test players

Description

A dataset containing career batting statistics for all international test players (men and women) up to 6 October 2021.

Usage

cricket_batting

Format

A data frame with 3754 rows and 15 variables:

Player

Player name in form of "initials surname"

Country

Country played for

Start

First year of test playing career

End

Last year of test playing career

Matches

Number of matches played

Innings

Number of innings batted

NotOuts

Number of times not out

Runs

Total runs scored

HighScore

Highest score in an innings

HighScoreNotOut

Was highest score not out?

Average

Batting average at end of career

Hundreds

Total number of 100s scored

Fifties

Total number of 50s scored

Ducks

Total number of 0s scored

Gender

"Men" or "Women"

Value

Data frame

Source

https://www.espncricinfo.com

Examples

cricket_batting |>
  filter(Innings > 20) |>
  select(Player, Country, Matches, Runs, Average, Hundreds, Fifties, Ducks) |>
  arrange(desc(Average))

Density scores

Description

Compute density scores or leave-one-out density scores from a model or a kernel density estimate of a data set. The density scores are defined as minus the log of the conditional density, or kernel density estimate, at each observation. The leave-one-out density scores (or LOO density scores) are obtained by estimating the conditional density or kernel density estimate using all other observations.

Usage

density_scores(object, loo = FALSE, ...)

## Default S3 method:
density_scores(
  object,
  loo = FALSE,
  h = kde_bandwidth(object, method = "double"),
  H = kde_bandwidth(object, method = "double"),
  ...
)

## S3 method for class 'kde'
density_scores(object, loo = FALSE, ...)

## S3 method for class 'lm'
density_scores(object, loo = FALSE, ...)

## S3 method for class 'gam'
density_scores(object, loo = FALSE, ...)

Arguments

object

A model object or a numerical data set.

loo

Should leave-one-out density scores be computed?

...

Other arguments are ignored.

h

Bandwidth for univariate kernel density estimate. Default is kde_bandwidth.

H

Bandwidth for multivariate kernel density estimate. Default is kde_bandwidth.

Details

If the first argument is a numerical vector or matrix, then a kernel density estimate is computed, using a Gaussian kernel, with default bandwidth given by a robust normal reference rule. Otherwise the model is used to compute the conditional density function at each observation, from which the density scores (or possibly the LOO density scores) are obtained.

Value

A numerical vector containing either the density scores, or the LOO density scores.

Author(s)

Rob J Hyndman

See Also

kde_bandwidth kde

Examples

# Density scores computed from bivariate data set
of <- oldfaithful |>
  filter(duration < 7000, waiting < 7000) |>
  mutate(
    fscores = density_scores(cbind(duration, waiting)),
    loo_fscores = density_scores(cbind(duration, waiting), loo = TRUE),
    lookout_prob = lookout(density_scores = fscores, loo_scores = loo_fscores)
  )
of |>
  ggplot(aes(x = duration, y = waiting, color = lookout_prob < 0.01)) +
  geom_point()
# Density scores computed from bivariate KDE
f_kde <- kde(of[, 2:3], H = kde_bandwidth(of[, 2:3]))
of |>
  mutate(
    fscores = density_scores(f_kde),
    loo_fscores = density_scores(f_kde, loo = TRUE)
  )
# Density scores computed from linear model
of <- oldfaithful |>
  filter(duration < 7200, waiting < 7200)
lm_of <- lm(waiting ~ duration, data = of)
of |>
  mutate(
    fscore = density_scores(lm_of),
    loo_fscore = density_scores(lm_of, loo = TRUE),
    lookout_prob = lookout(density_scores = fscore, loo_scores = loo_fscore)
  ) |>
  ggplot(aes(x = duration, y = waiting, color = lookout_prob < 0.02)) +
  geom_point()
# Density scores computed from GAM
of <- oldfaithful |>
  filter(duration > 1, duration < 7200, waiting < 7200)
gam_of <- mgcv::gam(waiting ~ s(duration), data = of)
of |>
  mutate(
    fscore = density_scores(gam_of),
    lookout_prob = lookout(density_scores = fscore)
  ) |>
  filter(lookout_prob < 0.02)

Wine prices and points

Description

A data set containing data on wines from 44 countries, taken from Wine Enthusiast Magazine during the week of 15 June 2017. The data are downloaded and returned.

Usage

fetch_wine_reviews()

Format

A data frame with 110,203 rows and 8 columns:

country

Country of origin

state

State or province of origin

region

Region of origin

winery

Name of vineyard that made the wine

variety

Variety of grape

points

Points allocated by WineEnthusiast reviewer on a scale of 0-100

price

Price of a bottle of wine in $US

year

Year of wine extracted from title

Value

Data frame

Source

https://kaggle.com

Examples

## Not run: 
wine_reviews <- fetch_wine_reviews()
wine_reviews |>
 ggplot(aes(x = points, y = price)) +
 geom_jitter(height = 0, width = 0.2, alpha = 0.1) +
 scale_y_log10()

## End(Not run)

Bagplot

Description

Produces a bivariate bagplot. A bagplot is analagous to a univariate boxplot, except it is in two dimensions. Like a boxplot, it shows the median, a region containing 50% of the observations, a region showing the remaining observations other than outliers, and any outliers.

Usage

gg_bagplot(
  data,
  var1,
  var2,
  col = c(hdr_palette(color = "#00659e", prob = c(0.5, 0.99)), "#000000"),
  scatterplot = FALSE,
  ...
)

Arguments

data

A data frame or matrix containing the data.

var1

The name of the first variable to plot (a bare expression).

var2

The name of the second variable to plot (a bare expression).

col

The colors to use in the order: median, bag, loop and outliers.

scatterplot

A logical argument indicating if a regular bagplot is required (FALSE), or if a scatterplot in the same colors is required (TRUE).

...

Other arguments are passed to the compute.bagplot function.

Value

A ggplot object showing a bagplot or scatterplot of the data.

Author(s)

Rob J Hyndman

References

Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999). The bagplot: A bivariate boxplot. The American Statistician, 52(4), 382–387.

See Also

bagplot

Examples

gg_bagplot(n01, v1, v2)
gg_bagplot(n01, v1, v2, scatterplot = TRUE)

HDR plot

Description

Produces a 1d or 2d box plot of HDR regions. The darker regions contain observations with higher probability, while the lighter regions contain points with lower probability. Points outside the largest HDR are shown as individual points. Points with lookout probabilities less than 0.05 are optionally shown in red.

Usage

gg_hdrboxplot(
  data,
  var1,
  var2 = NULL,
  prob = c(0.5, 0.99),
  color = "#00659e",
  scatterplot = FALSE,
  show_lookout = TRUE,
  ...
)

Arguments

data

A data frame or matrix containing the data.

var1

The name of the first variable to plot (a bare expression).

var2

Optionally, the name of the second variable to plot (a bare expression).

prob

A numeric vector specifying the coverage probabilities for the HDRs.

color

The base color to use for the mode. Colors for the HDRs are generated by whitening this color.

scatterplot

A logical argument indicating if a regular HDR plot is required (FALSE), or if a scatterplot in the same colors is required (TRUE).

show_lookout

A logical argument indicating if the plot should highlight observations with "lookout" probabilities less than 0.05.

...

Other arguments passed to kde.

Details

The original HDR boxplot proposed by Hyndman (1996), R can be produced with all arguments set to their defaults other than lookout.

Value

A ggplot object showing an HDR plot or scatterplot of the data.

Author(s)

Rob J Hyndman

References

Hyndman, R J (1996) Computing and Graphing Highest Density Regions, The American Statistician, 50(2), 120–126. https://robjhyndman.com/publications/hdr/ Kandanaarachchi, S & Hyndman, R J (2022) "Leave-one-out kernel density estimates for outlier detection", J Computational & Graphical Statistics, 31(2), 586-599. https://robjhyndman.com/publications/lookout/

Examples

df <- data.frame(x = c(rnorm(1000), rnorm(1000, 5, 1)))
df$y <- df$x + rnorm(200, sd=2)
gg_hdrboxplot(df, x)
gg_hdrboxplot(df, x, y, scatterplot = TRUE)
oldfaithful |>
  filter(duration < 7000, waiting < 7000) |>
  gg_hdrboxplot(duration, waiting, scatterplot = TRUE)
cricket_batting |>
  filter(Innings > 20) |>
  gg_hdrboxplot(Average)

GLOSH scores

Description

Compute Global-Local Outlier Score from Hierarchies. This is based on hierarchical clustering where the minimum cluster size is k. The resulting outlier score is a measure of how anomalous each observation is. The function uses dbscan::hdbscan to do the calculation.

Usage

glosh_scores(y, k = 10, ...)

Arguments

y

Numerical matrix or vector of data

k

Minimum cluster size. Default: 5.

...

Additional arguments passed to dbscan::hdbscan

Value

Numerical vector containing GLOSH values

Author(s)

Rob J Hyndman

See Also

dbscan::glosh

Examples

y <- c(rnorm(49), 5)
glosh_scores(y)

Statistical tests for anomalies using Grubbs' test and Dixon's test

Description

Grubbs' test (proposed in 1950) identifies possible anomalies in univariate data using z-scores assuming the data come from a normal distribution. Dixon's test (also from 1950) compares the difference in the largest two values to the range of the data. Critical values for Dixon's test have been computed using simulation with interpolation using a quadratic model on logit(alpha) and log(log(n)).

Usage

grubbs_anomalies(y, alpha = 0.05)

dixon_anomalies(y, alpha = 0.05, two_sided = TRUE)

Arguments

y

numerical vector of observations

alpha

size of the test.

two_sided

If TRUE, both minimum and maximums will be considered. Otherwise only the maximum will be used. (Take negative values to consider only the minimum with two_sided=FALSE.)

Details

Grubbs' test is based on z-scores, and a point is identified as an anomaly when the associated absolute z-score is greater than a threshold value. A vector of logical values is returned, where TRUE indicates an anomaly. This version of Grubbs' test looks for outliers anywhere in the sample. Grubbs' original test came in several variations which looked for one outlier, or two outliers in one tail, or two outliers on opposite tails. These variations are implemented in the grubbs.test function. Dixon's test only considers the maximum (and possibly the minimum) as potential outliers.

Value

A logical vector

Author(s)

Rob J Hyndman

References

Grubbs, F. E. (1950). Sample criteria for testing outlying observations. Annals of Mathematical Statistics, 21(1), 27–58. Dixon, W. J. (1950). Analysis of extreme values. Annals of Mathematical Statistics, 21(4), 488–506.

See Also

grubbs.test, dixon.test

Examples

x <- c(rnorm(1000), 5:10)
tibble(x = x) |> filter(grubbs_anomalies(x))
tibble(x = x) |> filter(dixon_anomalies(x))
y <- c(rnorm(1000), 5)
tibble(y = y) |> filter(grubbs_anomalies(y))
tibble(y = y) |> filter(dixon_anomalies(y))

Color palette designed for plotting Highest Density Regions

Description

A sequential color palette is returned, with the first color being color, and the rest of the colors being a mix of color with increasing amounts of white. If prob is provided, then the mixing proportions are determined by prob (and n is ignored). Otherwise the mixing proportions are equally spaced between 0 and 1.

Usage

hdr_palette(n, color = "#00659e", prob = NULL)

Arguments

n

Number of colors in palette.

color

First color of vector.

prob

Vector of probabilities between 0 and 1.

Value

A function that returns a vector of colors of length length(prob) + 1.

Examples

hdr_palette(prob = c(0.5, 0.99))

Table of Highest Density Regions

Description

Compute the highest density regions (HDR) for a kernel density estimate. The HDRs are returned as a tibble with one row per interval and columns: prob (giving the probability coverage), density (the value of the density at the boundary of the HDR), For one dimensional density functions, the tibble also has columns lower (the lower ends of the intervals), upper (the upper ends of the interval), mode (the point at which the density is maximized within each interval).

Usage

hdr_table(
  y = NULL,
  density = NULL,
  prob = c(0.5, 0.99),
  h = kde_bandwidth(y, method = "double"),
  H = kde_bandwidth(y, method = "double"),
  ...
)

Arguments

y

Numerical vector or matrix of data

density

Probability density function, either estimated by ks::kde() or a data frame or matrix with numerical columns that can be passed to as_kde().

prob

Probability of the HDR

h

Bandwidth for univariate kernel density estimate. Default is kde_bandwidth.

H

Bandwidth for multivariate kernel density estimate. Default is kde_bandwidth.

...

If y is supplied, other arguments are passed to kde. Otherwise, additional arguments are passed to as_kde.

Value

A tibble

Author(s)

Rob J Hyndman

References

Hyndman, R J. (1996) Computing and Graphing Highest Density Regions, The American Statistician, 50(2), 120–126.

Examples

# Univariate HDRs
y <- c(rnorm(100), rnorm(100, 3, 1))
hdr_table(y = y)
hdr_table(density = ks::kde(y))
x <- seq(-4, 4, by = 0.01)
hdr_table(density = data.frame(y = x, density = dnorm(x)), prob = 0.95)
# Bivariate HDRs
y <- cbind(rnorm(100), rnorm(100))
hdr_table(y = y)
grid <- seq(-4, 4, by=0.1)
density <- expand.grid(grid, grid) |>
  mutate(density = dnorm(Var1) * dnorm(Var2))
hdr_table(density = density)

Robust bandwidth estimation for kernel density estimation

Description

Robust bandwidth estimation for kernel density estimation

Usage

kde_bandwidth(
  data,
  method = c("robust_normal", "double", "lookout"),
  max.iter = 2
)

Arguments

data

A numeric matrix or data frame.

method

Method to use for selecting the bandwidth. robust_normal uses a robust version of the normal reference rule. lookout uses the topological data analysis approach that is part of the lookout algorithm.

max.iter

How many times should the lookout method be iterated. That is, outliers (probability < 0.05) are removed and the bandwidth is re-computed from the remaining observations.

Value

A matrix of bandwidths (or scalar in the case of univariate data).

Author(s)

Rob J Hyndman

Examples

# Univariate bandwidth calculation
kde_bandwidth(oldfaithful$duration)
# Bivariate bandwidth calculation
kde_bandwidth(oldfaithful[,2:3])

Local outlier factors

Description

Compute local outlier factors using k nearest neighbours. A local outlier factor is a measure of how anomalous each observation is based on the density of neighbouring points. The function uses dbscan::lof to do the calculation.

Usage

lof_scores(y, k = 10, ...)

Arguments

y

Numerical matrix or vector of data

k

Number of neighbours to include. Default: 5.

...

Additional arguments passed to dbscan::lof

Value

Numerical vector containing LOF values

Author(s)

Rob J Hyndman

See Also

dbscan::lof

Examples

y <- c(rnorm(49), 5)
lof_scores(y)

Lookout probabilities

Description

Compute leave-one-out log score probabilities using a Generalized Pareto distribution. These give the probability of each observation being an anomaly.

Usage

lookout(
  object = NULL,
  density_scores = NULL,
  loo_scores = density_scores,
  threshold_probability = 0.95
)

Arguments

object

A model object or a numerical data set.

density_scores

Numerical vector of log scores

loo_scores

Optional numerical vector of leave-one-out log scores

threshold_probability

Probability threshold when computing the POT model for the log scores.

Details

This function can work with several object types. If object is not NULL, then the object is passed to density_scores to compute density scores (and possibly LOO density scores). Otherwise, the density scores are taken from the density_scores argument, and the LOO density scores are taken from the loo_scores argument. Then the Generalized Pareto distribution is fitted to the scores, to obtain the probability of each observation.

Value

A numerical vector containing the lookout probabilities

Author(s)

Rob J Hyndman

References

Sevvandi Kandanaarachchi & Rob J Hyndman (2022) "Leave-one-out kernel density estimates for outlier detection", J Computational & Graphical Statistics, 31(2), 586-599. https://robjhyndman.com/publications/lookout/

Examples

# Univariate data
tibble(
  y = c(5, rnorm(49)),
  lookout = lookout(y)
)
# Bivariate data with score calculation done outside the function
tibble(
  x = rnorm(50),
  y = c(5, rnorm(49)),
  fscores = density_scores(y),
  loo_fscores = density_scores(y, loo = TRUE),
  lookout = lookout(density_scores = fscores, loo_scores = loo_fscores)
)
# Using a regression model
of <- oldfaithful |> filter(duration < 7200, waiting < 7200)
fit_of <- lm(waiting ~ duration, data = of)
of |>
  mutate(lookout_prob = lookout(fit_of)) |>
  arrange(lookout_prob)

Compute robust multivariate scaled data

Description

A multivariate version of base::scale(), that takes account of the covariance matrix of the data, and uses robust estimates of center, scale and covariance by default. The centers are removed using medians, the scale function is the IQR, and the covariance matrix is estimated using a robust OGK estimate. The data are scaled using the Cholesky decomposition of the inverse covariance. Then the scaled data are returned. This is useful for computing pairwise Mahalanobis distances.

Usage

mvscale(
  object,
  center = stats::median,
  scale = robustbase::s_IQR,
  cov = robustbase::covOGK,
  warning = TRUE
)

Arguments

object

A vector, matrix, or data frame containing some numerical data.

center

A function to compute the center of each numerical variable. Set to NULL if no centering is required.

scale

A function to scale each numerical variable. When cov = robustbase::covOGK, it is passed as the sigmamu argument.

cov

A function to compute the covariance matrix. Set to NULL if no rotation required.

warning

Should a warning be issued if non-numeric columns are ignored?

Details

Optionally, the centering and scaling can be done for each variable separately, so there is no rotation of the data, by setting cov = NULL. Also optionally, non-robust methods can be used by specifying center = mean, scale = stats::sd, and cov = stats::cov. Any non-numeric columns are retained with a warning.

Value

A vector, matrix or data frame of the same size and class as object, but with numerical variables replaced by scaled versions.

Author(s)

Rob J Hyndman

Examples

# Univariate z-scores (no rotation)
mvscale(oldfaithful, center = mean, scale = sd, cov = NULL, warning = FALSE)
# Non-robust scaling with rotation
mvscale(oldfaithful, center = mean, cov = stats::cov, warning = FALSE)
mvscale(oldfaithful, warning = FALSE)
# Robust Mahalanobis distances
oldfaithful |>
  select(-time) |>
  mvscale() |>
  head(5) |>
  dist()

Multivariate standard normal data

Description

A synthetic data set containing 1000 observations on 10 variables generated from independent standard normal distributions.

Usage

n01

Format

A data frame with 1000 rows and 10 columns.

Value

Data frame

Examples

n01

Old faithful eruption data

Description

A data set containing data on recorded eruptions of the Old Faithful Geyser in Yellowstone National Park, Wyoming, USA, from 1 January 2015 to 1 October 2021. Recordings are incomplete, especially during the winter months when observers may not be present.

Usage

oldfaithful

Format

A data frame with 2261 rows and 3 columns:

time

Time eruption started

duration

Duration of eruption in seconds

waiting

Time to the following eruption

Value

Data frame

Source

https://geysertimes.org

Examples

oldfaithful |>
 filter(duration < 7000, waiting < 7000) |>
 ggplot(aes(x = duration, y = waiting)) +
 geom_point()

Anomalies according to Peirce's and Chauvenet's criteria

Description

Peirce's criterion and Chauvenet's criterion were both proposed in the 1800s as a way of determining what observations should be rejected in a univariate sample.

Usage

peirce_anomalies(y)

chauvenet_anomalies(y)

Arguments

y

numerical vector of observations

Details

These functions take a univariate sample y and return a logical vector indicating which observations should be considered anomalies according to either Peirce's criterion or Chauvenet's criterion.

Value

A logical vector

Author(s)

Rob J Hyndman

References

Peirce, B. (1852). Criterion for the rejection of doubtful observations. The Astronomical Journal, 2(21), 161–163.

Chauvenet, W. (1863). 'Method of least squares'. Appendix to Manual of Spherical and Practical Astronomy, Vol.2, Lippincott, Philadelphia, pp.469-566.

Examples

y <- rnorm(1000)
tibble(y = y) |> filter(peirce_anomalies(y))
tibble(y = y) |> filter(chauvenet_anomalies(y))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autoplot

Stray anomalies

Description

Test if observations are anomalies according to the stray algorithm.

Usage

stray_anomalies(y, ...)

Arguments

y

A vector, matrix, or data frame consisting of numerical variables.

...

Other arguments are passed to find_HDoutliers.

Value

Numerical vector containing logical values indicating if the observation is identified as an anomaly using the stray algorithm.

Author(s)

Rob J Hyndman

Examples

# Univariate data
y <- c(6, rnorm(49))
stray_anomalies(y)
# Bivariate data
y <- cbind(rnorm(50), c(5, rnorm(49)))
stray_anomalies(y)

Stray scores

Description

Compute stray scores indicating how anomalous each observation is.

Usage

stray_scores(y, ...)

Arguments

y

A vector, matrix, or data frame consisting of numerical variables.

...

Other arguments are passed to find_HDoutliers.

Value

Numerical vector containing stray scores.

Author(s)

Rob J Hyndman

Examples

# Univariate data
y <- c(6, rnorm(49))
scores <- stray_scores(y)
threshold <- stray::find_threshold(scores, alpha = 0.01, outtail = "max", p = 0.5, tn = 50)
which(scores > threshold)

Conflicts between weird packages and other packages

Description

This function lists all the conflicts between packages in the weird collection and other packages that you have loaded.

Usage

weird_conflicts()

Details

Some conflicts are deliberately ignored: intersect, union, setequal, and setdiff from dplyr; and intersect, union, setdiff, and as.difftime from lubridate. These functions make the base equivalents generic, so shouldn't negatively affect any existing code.

Value

A list object of class weird_conflicts.

Examples

weird_conflicts()

List all packages loaded by weird

Description

List all packages loaded by weird

Usage

weird_packages(include_self = FALSE)

Arguments

include_self

Include weird in the list?

Value

A character vector of package names.

Examples

weird_packages()