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Package ‘akmeans’
February 20, 2019
Type Package
Title akmeans: 'Anchored' kmeans for Longitudinal Data
Version 0.1.0
Date 2019-02-06
Author Monsuru Adepeju [cre, aut], Samuel Langton [aut], Jon Bannister [aut]
Maintainer Monsuru Adepeju 
Description Advances an akmeans clustering technque and a stabilitybased quality criterion for longitudinal data. Also, contains
functions for useful for the analysis of longitudinal data.
License GPL-2
Encoding UTF-8
LazyData TRUE
Imports kml, devtools, Hmisc, ggplot2, rgdal, base, utils, reshape2, later
Suggests knitr,
rmarkdown
RoxygenNote 6.1.1
VignetteBuilder knitr

R topics documented:
akmeans_clust .
alphaLabel . . .
assault_data . .
gm_crime_data
lpm_centroids .
missingV_filler
outlierDetect .
plot_clust . . .
props . . . . .
qpm_centroids .
whiteSpaces . .

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Index

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2
2
3
3
3
4
5
6
6
7
7
8

1

2

alphaLabel

akmeans_clust

akmeans_clust

Description
This function group trajectories based on a given list of initial centroids
Usage
akmeans_clust(traj, id_field = FALSE, init_method = "lpm", n_clusters = 3)
Arguments
traj

A matrix or data.frame with each row representing the trajectory of observations
of a unique location. The columns show the observations at consecutive time
steps.

id_field

Whether the first column is a unique (id) field. Default: FALSE

init_method

initialisation method. Specifying a method to determine the initial centroids for
clustering. Default: "lpm" - linear partitioning medoids @seealso lpm_centroids]

n_clusters

number of clusters to generate. Default: 3: (minimum value)

Details
Given a list of trajectories represented in a matrix or data.frame, and a method for choosing initial
cluster centroids (e.g. lpm_centroids), a list of clusters is generated after a limited number of
iterations. traj <- assault_data print(traj) result <- akmeans_clust(traj, id_field = TRUE, init_method
= "lpm", n_clusters = 3) plot_clust(result)
Value
The original (traj) data with cluster label appended

alphaLabel

Numerics ids to alphabetical ids

Description
Function to transform a list of numeric ids to alphabetic ids
Usage
alphaLabel(x)
Arguments
x

A vector of numeric ids

assault_data

assault_data

3

Sample crime (assault) dataset

Description
Simulated crime dataset with missing values.
Usage
assault_data
Format
A matrix

gm_crime_data

Sample crime dataset

Description
Crime dataset of greater Manchester crime data aggregated at the LSOA geographical level data
(Source: data.police.uk)
Usage
gm_crime_data
Format
A matrix

lpm_centroids

Linear Partition Medoids (LPM) Centroids

Description
This function to create the initial centroids based on linear partitioning medoids (lpm) initialisation
(Adepeju et al. 2019, submitted)
Usage
lpm_centroids(dat, id_field2 = FALSE, n_centroids = 3)
Arguments
dat

id_field2
n_centroids

A matrix or data.frame with each row representing the trajectory of observations
of a unique location. The columns show the observation at consecutive time
steps.
Whether the first column is a unique (id) field. default: FALSE
Number of initial (linear) centroids to generate based on lpm technique

4

missingV_filler

Value
l_centroids
References
Adepeju M, Langton S, Bannister J. (2019). akmeans: Anchored k-means: A longitudinal clustering
technique for measuring long-term inequality in the exposure to crime at the micro-area levels
(submitted).

missingV_filler

Data imputing for longitudinal data

Description
This function fills up any missing entries (NA, Inf, 0) in a matrix or dataframe using a value
derived using a chosen method.
Usage
missingV_filler(traj, id_field = FALSE, method = 2, replace_with = 1, fill_zeros = FALSE)
Arguments
traj

A matrix or data.frame with each row representing the trajectory of a unique
location. The columns show the observations at consecutive time steps.

id_field

Whether the first column is a unique (id) field. default: FALSE

method

Method for calculating the missing values. Available options: 1: arithmetic, 2:
regression. default: 1

replace_with

How to calculate the missing value. For arithmetic method: replace_with
options are: 1: Mean value of column, 2: Minimum value of column, 3: Maximum value of column, 4: Mean value of row, 5: Minimum value of row, or
6: Maximum value of row. For regression method: the only available option
for replace_with is: 1: linear. That is, use a linear regression to interpolate or
extrapolate the missing data values. Note: only the missing data points derive
their new values from the regression line while the rest of the data points retain
their original values. Trajectories with only one observation will be removed.

fill_zeros

Whether to consider zeros (0) as missing values. Default: FALSE. Only available
for 2: regression method.

Details
Given a matrix or data.frame with some missing values represented by (NA, Inf, 0), the function missingV_filler determines the missing values using either the arithmetic or regression
method.
Value
A data.frame with missing values (NA, Inf, 0) filled up

outlierDetect

5

Examples
traj <- assault_data
print(traj)
missingV_filler(traj, id_field = TRUE, method = 2, replace_with = 1, fill_zeros = FALSE)

outlierDetect

Outlier detection in longitudinal or repeated observations

Description
Detect outlier in a longitudinal or repeated data. This function identify the outlier observations according to a specified method. A matrix, ’outlier_mat’, is created with entries ’TRUE’ or ’FALSE’
indicating whether or not an observation is an outlier. The final list of outlier trajectories is determined by the ’hortz_tolerance’ parameter i.e. how many observation in a trajectory exceed the
’threshold’ value.
Usage
outlierDetect(dat, id_field = FALSE, method = "quantile",
threshold = 0.95, hortz_tolerance = 1, replace_with = "Mean_row")
Arguments
dat

A matrix or data.frame with each row representing the trajectory of observations
of a unique location. The columns show the observation at consecutive time
steps.

id_field

Whether the first column is a unique (id) field. [default: FALSE]

method

Specify the method for identifying the outlier. Available methods: (1) "quantile"
(2) "manual" - a user-defined value

threshold

Value in which an observation must exceed in order to be flagged as outlier.
Depending on the method specified: (1) for "quantile" method, enter a numeric
vector of probabilities with values in [0,1], (2) for "Manual" method: a userspecified value.

hortz_tolerance
Specifying the number of observations of a trajectory that have to exceed the cutoff ’threshold’ value in order for the trajectory to be flagged as outlier. [default:
1]
replace_with

Value
dat_

Value to replace the outlier observation with. Values to replace with [Values:
"Mean_col" or "Mean_row"]. The default is "Mean_row", meaning to imput the
average values of the field in which the observation is located.

6

props

plot_clust

To plot the clusters

Description
To plot the clusters
Usage
plot_clust(data_clusters_list, id_field = TRUE)
Arguments
data_clusters_list
A data.frame of clusters from akmeans_clust, in which the last column represents alphabetical cluster ids (labels)
id_field

Whether the first column is a unique (id) field. [default: TRUE]

Value
data_clusters_list

props

Function to convert counts or rates to proportion

Description
Function to convert counts or rates to proportion
Usage
props(rates, id_field = FALSE)
Arguments
rates

A matrix or data.frame with each row representing the trajectory of observations
of a unique location. The columns show the observation at consecutive time
steps.

id_field

Whether the first column is a unique (id) field. [default: FALSE]

Value
props

qpm_centroids

qpm_centroids

7

Quadratic Partition Medoids (QPM) Centroids

Description
Quadratic Partition Medoids (QPM) Centroids
Usage
qpm_centroids(dat, n_centroids = 3, id_field = FALSE)
Arguments
dat

A matrix or data.frame with each row representing the trajectory of observations
of a unique location. The columns show the observation at consecutive time
steps.

n_centroids

Number of initial (quadratic) centroids to generate based on the qpm method
(See attached Vignette)

id_field

Whether the first column is a unique (id) field. [default: FALSE]

Value
q_centroids

whiteSpaces

Function to remove whitespaces in data entries

Description
Function to remove whitespaces in data entries
Usage
whiteSpaces(dat, head = TRUE)
Arguments
dat

A matrix or data.frame

head

If column names exist

Value
dat_Cleaned

Index
∗Topic datasets
assault_data, 3
gm_crime_data, 3
akmeans_clust, 2, 6
alphaLabel, 2
assault_data, 3
gm_crime_data, 3
lpm_centroids, 2, 3
missingV_filler, 4
outlierDetect, 5
plot_clust, 6
props, 6
qpm_centroids, 7
whiteSpaces, 7

8
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