iccTraj {iccTraj}

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
)

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.

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