Description Usage Arguments Details Value ZOC Detection of dive phases Note Author(s) References See Also Examples

Detect periods of major activities in a TDR
record, calibrate depth readings, and generate a
`TDRcalibrate`

object essential for subsequent summaries
of diving behaviour.

1 2 3 4 | ```
calibrateDepth(x, dry.thr=70, wet.cond, wet.thr=3610, dive.thr=4,
zoc.method=c("visual", "offset", "filter"), ...,
interp.wet=FALSE, smooth.par=0.1, knot.factor=3,
descent.crit.q=0, ascent.crit.q=0)
``` |

`x` |
An object of class |

`dry.thr` |
numeric: dry error threshold in seconds. Dry phases shorter than this threshold will be considered as wet. |

`wet.cond` |
logical: indicates which observations should be considered wet. If it is not provided, records with non-missing depth are assumed to correspond to wet conditions (see ‘Details’ and ‘Note’ below). |

`wet.thr` |
numeric: wet threshold in seconds. At-sea phases shorter than this threshold will be considered as trivial wet. |

`dive.thr` |
numeric: threshold depth below which an underwater phase should be considered a dive. |

`zoc.method` |
character string to indicate the method to use for zero offset correction. One of “visual”, “offset”, or “filter” (see ‘Details’). |

`...` |
Arguments required for ZOC methods |

`interp.wet` |
logical: if TRUE (default is FALSE), then an
interpolating spline function is used to impute NA depths in wet
periods ( |

`smooth.par` |
numeric scalar representing amount of smoothing
(argument |

`knot.factor` |
numeric scalar that multiplies the number of samples in the dive. This is used to construct the time predictor for the derivative. |

`descent.crit.q` |
numeric: critical quantile of rates of descent below which descent is deemed to have ended. |

`ascent.crit.q` |
numeric: critical quantile of rates of ascent above which ascent is deemed to have started. |

This function is really a wrapper around `.detPhase`

,
`.detDive`

, and `.zoc`

which perform the work on simplified
objects. It performs wet/dry phase detection, zero-offset correction
of depth, and detection of dives, as well as proper labelling of the
latter.

The procedure starts by zero-offset correcting depth (see ‘ZOC’
below), and then a factor is created with value “L” (dry) for
rows with NAs for `depth`

and value “W” (wet) otherwise.
This assumes that TDRs were programmed to turn off recording
of depth when instrument is dry (typically by means of a salt-water
switch). If this assumption cannot be made for any reason, then a
logical vector as long as the time series should be supplied as
argument `wet.cond`

to indicate which observations should be
considered wet. This argument is directly analogous to the
`subset`

argument in `subset.data.frame`

, so it can
refer to any variable in the `TDR`

object (see
‘Note’ section below). The duration of each of these phases of
activity is subsequently calculated. If the duration of a dry phase
(“L”) is less than `dry.thr`

, then the values in the
factor for that phase are changed to “W” (wet). The duration
of phases is then recalculated, and if the duration of a phase of wet
activity is less than `wet.thr`

, then the corresponding value for
the factor is changed to “Z” (trivial wet). The durations of
all phases are recalculated a third time to provide final phase
durations.

Some instruments produce a peculiar pattern of missing data near the
surface, at the beginning and/or end of dives. The argument
`interp.wet`

may help to rectify this problem by using an
interpolating spline function to impute the missing data, constraining
the result to a minimum depth of zero. Please note that this optional
step is performed after ZOC and before identifying dives, so that
interpolation is performed through dry phases coded as wet because
their duration was briefer than `dry.thr`

. Therefore,
`dry.thr`

must be chosen carefully to avoid interpolation through
legitimate dry periods.

The next step is to detect dives whenever the zero-offset corrected depth in an underwater phase is below the specified dive threshold. A new factor with finer levels of activity is thus generated, including “U” (underwater), and “D” (diving) in addition to the ones described above.

Once dives have been detected and assigned to a period of wet activity, phases within dives are identified using the descent, ascent and wiggle criteria (see ‘Detection of dive phases’ below). This procedure generates a factor with levels “D”, “DB”, “B”, “BA”, “DA”, “A”, and “X”, breaking the input into descent, descent/bottom, bottom, bottom/ascent, ascent, descent/ascent (ocurring when no bottom phase can be detected) and non-dive (surface), respectively.

An object of class `TDRcalibrate`

.

This procedure is required to correct drifts in the pressure transducer of TDR records and noise in depth measurements. Three methods are available to perform this correction.

Method “visual” calls `plotTDR`

, which plots depth
and, optionally, speed vs. time with the ability of zooming in and out
on time, changing maximum depths displayed, and panning through time.
The button to zero-offset correct sections of the record allows for
the collection of ‘x’ and ‘y’ coordinates for two
points, obtained by clicking on the plot region. The first point
clicked represents the offset and beginning time of section to
correct, and the second one represents the ending time of the section
to correct. Multiple sections of the record can be corrected in this
manner, by panning through the time and repeating the procedure. In
case there's overlap between zero offset corrected windows, the last
one prevails.

Method “offset” can be used when the offset is known in advance, and this value is used to correct the entire time series. Therefore, offset=0 specifies no correction.

Method “filter” implements a smoothing/filtering mechanism
where running quantiles can be applied to depth measurements in a
recursive manner (Luque and Fried 2011), using
`.depth.filter`

. It relies on function
`runquantile`

from the `caTools`

package.
The method calculates the first running quantile defined by
`probs[1]`

on a moving window of size `k[1]`

. The next
running quantile, defined by `probs[2]`

and `k[2]`

, is
applied to the smoothed/filtered depth measurements from the previous
step, and so on. The corrected depth measurements (d) are calculated
as:

*d=d[0] - d[n]*

where *d[0]* is original depth and *d[n]* is the
last smoothed/filtered depth. This method is under development, but
reasonable results can be achieved by applying two filters (see
‘Examples’). The default `na.rm=TRUE`

works well when
there are no level shifts between non-NA phases in the data, but
`na.rm=FALSE`

is better in the presence of such shifts. In other
words, there is no reason to pollute the moving window with NAs when
non-NA phases can be regarded as a continuum, so splicing non-NA
phases makes sense. Conversely, if there are level shifts between
non-NA phases, then it is better to retain NA phases to help the
algorithm recognize the shifts while sliding the window(s). The
search for the surface can be limited to specified bounds during
smoothing/filtering, so that observations outside these bounds are
interpolated using the bounded smoothed/filtered series.

Once the whole record has been zero-offset corrected, remaining depths below zero, are set to zero, as these are assumed to indicate values at the surface.

The process for each dive begins by taking all observations below the
dive detection threshold, and setting the beginning and end depths to
zero, at time steps prior to the first and after the last,
respectively. The latter ensures that descent and ascent derivatives
are non-negative and non-positive, respectively, so that the end and
beginning of these phases are not truncated. A smoothing spline is
used to model the dive and its derivative to investigate its changes
in vertical rate. This method requires at least 4 observations (see
`smooth.spline`

), so the time series is linearly
interpolated at equally spaced time steps if this limit is not
achieved in the current dive. Wiggles at the beginning and end of the
dive are assumed to be zero offset correction errors, so depth
observations at these extremes are interpolated between zero and the
next observations when this occurs.

A set of knots is established to fit the smoothing spline by using an
regular time sequence with beginning and end equal to the extremes of
the input sequence, and with length equal to *N * \code{knot.factor}*. The first derivate of the
spline is evaluated at the same set of knots to calculate the vertical
rate throughout the dive and determine the end of descent and
beginning of ascent. Equivalent procedures are used for detecting
descent and ascent phases.

The quantile corresponding to (`descent.crit.q`

) of all the
positive derivatives (rate of descent) at the beginning of the dive is
used as threshold for determining the end of descent. Descent is
deemed to have ended at the *first* minimum derivative, and the
nearest input time observation is considered to indicate the end of
descent. The sign of the comparisons is reversed for detecting the
ascent. If observed depth to the left and right of the derivative
defining the ascent are the same, the right takes precedence.

The particular dive phase categories are subsequently defined using simple set operations.

Note that the condition implied with argument `wet.cond`

is
evaluated after the ZOC procedure, so it can refer to corrected depth.
In many cases, not all variables in the `TDR`

object are
sampled with the same frequency, so they may need to be interpolated
before using them for this purpose. Note also that any of these
variables may contain similar problems as those dealth with during
ZOC, so programming instruments to record depth only when wet is
likely the best way to ensure proper detection of wet/dry conditions.

Sebastian P. Luque [email protected]

Luque, S.P. and Fried, R. (2011) Recursive filtering for zero offset correction of diving depth time series. PLoS ONE 6:e15850

`TDRcalibrate`

, `.zoc`

,
`.depthFilter`

, `.detPhase`

,
`.detDive`

, `plotTDR`

, and
`plotZOC`

to visually assess ZOC procedure.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data(divesTDR)
divesTDR
## Consider a 3 m offset, a dive threshold of 3 m, the 1% quantile for
## critical vertical rates, and a set of knots 20 times as long as the
## observed time steps.
(dcalib <- calibrateDepth(divesTDR, dive.thr=3, zoc.method="offset",
offset=3, descent.crit.q=0.01, ascent.crit.q=0,
knot.factor=20))
## Or ZOC algorithmically with method="filter":
## Not run: ## This can take a while due to large window needed for 2nd
## filter in this dataset
(dcalib <- calibrateDepth(divesTDR, dive.thr=3, zoc.method="filter",
k=c(3, 5760), probs=c(0.5, 0.02), na.rm=TRUE,
descent.crit.q=0.01, ascent.crit.q=0,
knot.factor=20))
## End(Not run)
``` |

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