repairGaps: Process Tracks Containing Gaps
In MotilityLab: Quantitative Analysis of Motion

Description Usage Arguments Examples

Many common motility analyses, such as mean square displacement plots, assume that object positions are recorded at constant time intervals. For some application domains, such as intravital imaging, this may not always be the case. This function can be used to pre-process data imaged at nonconstant intervals, provided the deviations are not too extreme.

1	repairGaps(x, how = "split", tol = 0.05, split.min.length = 2)

`x`	the input tracks object.
`how`	string specifying what do with tracks that contain gaps. Possible values are: "drop": the simplest option – discard all tracks that contain gaps. "split": split tracks around the gaps, e.g. a track for which the step between the 3rd and 4th positions is too long or too short is split into one track corresponding to positions 1 to 3 and another track corresponding to position 3 onwards. "interpolate": approximate the track positions using linear interpolation (see `interpolateTrack`). The result is a tracks object with constant step durations.
`tol`	nonnegative number specifying by which fraction each step may deviate from the average step duration without being considered a gap. For instance, if the average step duration (see `timeStep`) is 100 seconds and `tol` is 0.05 (the default), then step durations between 95 and 105 seconds (both inclusive) are not considered gaps. This option is ignored for `how="interpolate"`.
`split.min.length`	nonnegative integer. For `how="split"`, this discards all resulting tracks shorter than this many positions.

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## The Neutrophil data are imaged at rather nonconstant intervals
print( length( Neutrophils ) )
print( length( repairGaps( Neutrophils, tol=0.01 ) ) )