ColonyTrack_metrics_description.RmdThe colonytrack_metrics object contains features/metrics
calculated from the tracking data. The data are segmented by day
(technically by ‘window’; see the following section) and processed in
parallel to extract a range of metrics describing the activity of the
subjects and their interactions with each other. Details of the
resulting colonytrack_metrics object are given below.
The metrics are calculated for blocks of time, referred to as
‘windows’. In the current version of the software, these windows default
to a 12-hour period during the dark phase (ZT12–ZT24). Support for
calculation of light phase metrics (and limited support for customised
window intervals) will be added to an upcoming version. An additional
parameter trim allows for hours to be trimmed off the start
and end of the metrics calculation period. This makes it possible to
remove the first and last hours of data in the situation that the light
cycle is not strictly maintained for example. This feature could also
enable the user to focus on a particular period of interest—although it
is recommended to perform such trimming during later analysis of the
metrics results. The trim parameter will only accept
integer values (rounded hours) and care should be taken in interpreting
the results if this feature is used. The windows are described in the
windows section of the info component and
consist of an ID (by default the date in YYYY-MM-DD format) and a start
and end time as UNIX timestamps.
The cage layout is described in the cage.layout section
of the info component as a list of igraph
objects (see the data
object description for additional background). This list, with one
cage layout per day, allows for the flexibility to include modifications
of the cage layout during an experiment. The igraph objects
are passed through from the colonytrack_data object and
contain the same information.
The calculate_metrics function generates a list of 109
variables, many of which are derivatives (e.g. mean, median, variance)
of the same information (see the detailed description below for a list
of all metrics that are calculated). All of these values describe the
underlying data in subtly different ways and are thus useful for certain
applications such as machine learning. Reflecting this intended purpose,
these variables are referred to in the metrics object as ‘features’. A
list of the feature names can also be obtained computationally by
querying a colonytrack_metrics object and inspecting the
metrics$info$feature.names component.
For general use, however, the large collection of partially redundant
variables is not helpful, so a select subset is returned in the
individual component of the metrics object. The names of
the individual metrics can be recalled from the
metrics$info$var.names component of a
colonytrack_metrics object. These individual metrics are
accessible for plotting by the plot_metric function.
The cage.use component holds a list for each day of
tracking which is further subdivided into each cage; where the
proportion of time spent in each cage by each subject is recorded. Thus
the values for the first subject over all cages in one day should sum to
1. This enables the time spent in a cage of interest (e.g. a cage with
water access) to be easily calculated and can provide the basis for a
customised analysis of your cage set-up.
The ethogram is calculated hourly and is used as the basis for the
built-in plot_ethogram function. The ethogram uses 3
summary metrics to encapsulate the ‘activity’, ‘exploration’ and
‘sociality’ dimensions of the subjects behaviour. Although this does not
make use of the full multi-parametric power of ColonyTrack, it provides
a rapid and intuitive overview (for users with full colour vision) of
the behavioural patterns for very many subjects over long periods of
time. The ethogram component makes the numeric data
underlying the ethogram plot accessible to users—for example to enable
custom plots to be generated. Split into a list by day, the three
dimensions (‘activity’, ‘exploration’ and ‘sociality’) are available as
a numeric matrix with 12 columns (one for each hour of the day) and a
row for each subject. Users can, for example, build a custom one-colour
heatmap for each dimension separately using an appropriate palette to
provide colourblind-friendly output.
The group-housing nature of the ColonyRack enables interactions
between subjects to be recorded. Different to the individual metrics,
these social interactions are reported as a subject x subject matrix.
The group metrics that are calculated are interaction.time
(the amount of time that each pair of subjects spend in the same cage),
cage.share (an obsolete measure of the proportion of time
intervals in which two subjects were in the same cage; this will be
removed from future ColonyTrack versions), social.distance
(the average number of cages separating each pair of subjects;
technically, the mean shortest path in the cage network between the two
subjects over the course of the day) and chasing(the number
of times one subject [columns] chases another [rows]; see the section on
‘chasing’ below). With the exception of chasing, these
interaction matrices are symmetrical (in graph theory parlance, they are
undirected networks). The chasing network, on the other
hand, is directed—the subject doing the chasing (the ‘aggressor’) is in
the columns with the subject being chased (the ‘victim’) in the
rows.
A chase event is defined as two subjects through the same tunnel in
the same direction in rapid (less than 1 second delay) succession. The
second subject passing through the tunnel is taken to be pursuing the
first (this interpretation has been initially confirmed using video
observation of the cage system). The directed nature of this interaction
means that a hierarchy can be calculated by summing the ‘wins’ (the
number of times a subject was the chaser) for each pair of subjects. A
subject with more ‘wins’ over another is taken to be dominant in the
social hierarchy. The results are presented using this terminology for
practical convenience—the actual dominance relationships identified by
this method admittedly require some further investigation. The
dominance score for each subject is pre-calculated for
convenience from the chase network for each day. It is provided as a raw
sum of ‘wins’ and ‘losses’ and can be converted to ranks by the user if
desired.
Below is an overview of the hierarchy of the
colonytrack_metrics object together with the names and
classes of each component. Where the class is not part of the R base
package, it is given in square brackets after the class name.
metrics : colonytrack_metrics [ColonyTrack]
info : list
windows : colonytrack_windows [ColonyTrack]
id : character
start : numeric
end : numeric
subjects : character
subject.info : data.frame
SubjectID : character
Tag : character
(optional additional user-defined columns)
feature.names : character
var.names : character
cage.layout : list
(window) : igraph [igraph]
processed : POSIXct
version : character
features : list
(subject) : data.frame
path.length : numeric
max.time.in.cage : numeric
mean.time.in.cage : numeric
min.time.in.cage : numeric
sd.time.in.cage : numeric
upper.time.in.cage : numeric
median.time.in.cage : numeric
lower.time.in.cage : numeric
time.high.activity : numeric
time.sustained.activity : numeric
time.active : numeric
time.inactive : numeric
total.activity.burst.time : numeric
max.activity.burst.length : numeric
max.activity.burst.time : numeric
max.cage.use : numeric
mean.cage.use : numeric
min.cage.use : numeric
sd.cage.use : numeric
upper.cage.use : numeric
median.cage.use : numeric
lower.cage.use : numeric
cage.time.entropy : numeric
max.adj.cage.use : numeric
mean.adj.cage.use : numeric
min.adj.cage.use : numeric
sd.adj.cage.use : numeric
upper.adj.cage.use : numeric
median.adj.cage.use : numeric
lower.adj.cage.use : numeric
adjusted.cage.time.entropy : numeric
max.cage.transitions : numeric
mean.cage.transitions : numeric
min.cage.transitions : numeric
sd.cage.transitions : numeric
upper.cage.transitions : numeric
median.cage.transitions : numeric
lower.cage.transitions : numeric
cage.location.entropy : numeric
max.adj.cage.transitions : numeric
mean.adj.cage.transitions : numeric
min.adj.cage.transitions : numeric
sd.adj.cage.transitions : numeric
upper.adj.cage.transitions : numeric
median.adj.cage.transitions : numeric
lower.adj.cage.transitions : numeric
adjusted.cage.location.entropy : numeric
max.revisit.time : numeric
mean.revisit.time : numeric
min.revisit.time : numeric
sd.revisit.time : numeric
upper.revisit.time : numeric
median.revisit.time : numeric
lower.revisit.time : numeric
max.revisit.length : numeric
mean.revisit.length : numeric
min.revisit.length : numeric
sd.revisit.length : numeric
upper.revisit.length : numeric
median.revisit.length : numeric
lower.revisit.length : numeric
peak.inactive.timepoint : numeric
peak.active.timepoint : numeric
number.activity.blocks : numeric
max.cage.sharing : numeric
mean.cage.sharing : numeric
upper.cage.sharing : numeric
median.cage.sharing : numeric
lower.cage.sharing : numeric
time.alone : numeric
max.social.interaction.time : numeric
mean.social.interaction.time : numeric
min.social.interaction.time : numeric
sd.social.interaction.time : numeric
upper.social.interaction.time : numeric
median.social.interaction.time: numeric
lower.social.interaction.time : numeric
social.exposure : numeric
max.distance.from.all : numeric
mean.distance.from.all : numeric
min.distance.from.all : numeric
sd.distance.from.all : numeric
upper.distance.from.all : numeric
median.distance.from.all : numeric
lower.distance.from.all : numeric
max.distance.from.each : numeric
min.distance.from.each : numeric
sd.distance.from.each : numeric
upper.distance.from.each : numeric
median.distance.from.each : numeric
lower.distance.from.each : numeric
max.sharing.change : numeric
mean.sharing.change : numeric
min.sharing.change : numeric
sd.sharing.change : numeric
upper.sharing.change : numeric
median.sharing.change : numeric
lower.sharing.change : numeric
max.influence : numeric
mean.influence : numeric
min.influence : numeric
sd.influence : numeric
upper.influence : numeric
median.influence : numeric
lower.influence : numeric
number.chase.events : numeric
sd.chase.events : numeric
mean.chase.wins : numeric
sd.chase.wins : numeric
chase.sds : numeric
individual : list
(subject) : data.frame
distance.moved : numeric
time.per.cage : numeric
high.activity : numeric
sustained.activity : numeric
cage.variability : numeric
cage.time.entropy : numeric
adjusted.cage.time.entropy : numeric
cage.location.entropy : numeric
revisit.time : numeric
revisit.length : numeric
peak.inactive : numeric
peak.active : numeric
activity.blocks : numeric
cage.sharing : numeric
time.alone : numeric
social.interaction : numeric
social.distance : numeric
social.gradient : numeric
social.influence : numeric
chase.events : numeric
chase.dominance : numeric
cage.use : list
(window) : data.frame
(cage) : numeric
ethogram : list
(window) : list
activity : numeric
exploration : numeric
sociality : numeric
clustering : list
interaction.time : list
(window) : numeric matrix
cage.share : list
(window) : numeric matrix
social.distance : list
(window) : numeric matrix
chasing : list
(window) : numeric matrix
dominance : list
(window) : numeric
chase.events : list
(window) : list
(subject) : list
(subject) : data.frame
Timestamp : numeric
Transition : character
development : list
elapsed.time : numeric
threads : list
start.time : numeric
end.time : numeric
elapsed.time : numeric