iccTraj: Estimates the intraclass correlation coefficient (ICC) for...

In iccTraj: Estimates the Intraclass Correlation Coefficient for Trajectory Data

Estimates the intraclass correlation coefficient (ICC) for trajectory data

Description

Estimates the intraclass correlation coefficient (ICC) for trajectory data

Usage

iccTraj(
  data,
  ID,
  trip,
  LON,
  LAT,
  time,
  projection = CRS("+proj=longlat"),
  origin = "1970-01-01 UTC",
  parallel = TRUE,
  individual = TRUE,
  distance = c("H", "F"),
  bootCI = TRUE,
  nBoot = 100,
  q = 0.5,
  future_seed = 123
)

Arguments

data	A data frame with the locations and times of trajectories. It is assumed the time between locations is uniform. It must contain at least five columns: subject identifier, trip identifier, latitude, longitude, and time of the reading.
ID	Character string indicating the name of the subjects column in the dataset.
trip	Character string indicating the trip column in the dataset.
LON	Numeric. Longitude readings.
LAT	Numeric. Latitude readings.
time	Numeric. Time of the readings.
projection	Projection string of class CRS-class.
origin	Optional. Origin of the date-time. Only needed in the internal process to create an object of type POSIXct.
parallel	TRUE/FALSE value. Use parallel computation? Default value is TRUE.
individual	TRUE/FALSE value. Compute individual within-subjects variances? Default value is TRUE.
distance	Metric used to compute the distances between trajectories. Options are "H" for median Hausforff distance, and "F" for discrete Fréchet distance.
bootCI	TRUE/FALSE value. If TRUE it will generate boostrap resamples. Default value is TRUE.
nBoot	Numeric. Number of bootstrap resamples. Ignored if bootCI is FALSE. Default value is 100.
q	Quantile for the extended Hausdorff distance. Default value q=0.5 leads to median Hausdorff distance.
future_seed	Logical/Integer. The seed to be used for parallellization. Further details in furrr_options.

Details

The intraclass correlation coefficient is estimated using the distance matrix among trajectories.

Bootstrap resamples are obtained using balanced randomized cluster bootstrap approach (Davison and Hinkley, 1997; Field and Welsh, 2007)

Value

An object of class iccTraj.The output is a list with the following components:

• est. Data frame with the following estimates: the ICC (r), the subjects' mean sum-of-squares (MSA), the between-subjects variance (sb), the total variance (st), and the within-subjects variance (se).

• boot. If bootCI argument is set to TRUE, data frame with the bootstrap estimates.

• D. Data frame with the pairwise distances among trajectories.

• indW. Data frame with the following columns: the subject's identifier (ID), the individual within-subjects variances (w), the individual ICC (r), and the number of trips (n).

References

Davison A.C., Hinkley D.V. (1997). Bootstrap Methods and Their Application. Cambridge: Cambridge University Press.

Field, C.A., Welsh, A.H. (2007). Bootstrapping Clustered Data. Journal of the Royal Statistical Society. Series B (Statistical Methodology). 69(3), 369-390.

Examples

# Using median Hausdorff distance.
 Hd<-iccTraj(gull_data,"ID","trip","LONG","LAT","triptime")
 Hd$est
# Using discrete Fréchet distance.
Fd<-iccTraj(gull_data,"ID","trip","LONG","LAT","triptime", distance="F")
Fd$est

iccTraj documentation built on June 8, 2025, 10:06 a.m.