ctdDecimate {oce} | R Documentation

## Decimate a CTD profile

### Description

Interpolate a CTD profile to specified pressure values.

### Usage

    ctdDecimate(x, p = 1, method = "boxcar", rule = 1, e = 1.5,
      debug = getOption("oceDebug"))

### Arguments

`x` | A `ctd` object, i.e. one inheriting from `ctd-class`.
`p` | pressure increment, or vector of pressures. In the first case, pressures from 0dbar to the rounded maximum pressure are used, incrementing by `p` dbars. If a vector of pressures is given, interpolation is done to these pressures.

`method`

the method to be used for calculating decimated values. This may be a function or a string naming a built-in method. The built-in methods are `"boxcar"` (based on a local average), `"approx"` (based on linear interpolation between neighboring points, using `approx` with the `rule` argument specified here), `"lm"` (based on local regression, with `e` setting the size of the local region), `"rr"` (for the Reineger and Ross method, carried out with `oce.approx`) and `"unesco"` (for the UNESCO method, carried out with. `oce.approx`. If `method` is a function, then it must take three arguments, the first being pressure, the second being an arbitrary variable in another column of the data, and the third being a vector of target pressures at which the calculation is carried out, and the return value must be a vector. See “Examples”.

`rule` | an integer that is passed to `approx`, in the case where `method` is `"approx"`. Note that the default value for `rule` is 1, which will inhibit extrapolation beyond the observed pressure range. This is a change from the behaviour previous to May 8, 2017, when a `rule` of 2 was used (without stating so as an argument).

`e`

is an expansion coefficient used to calculate the local neighbourhoods for the `"boxcar"` and `"lm"` methods. If `e=1`, then the neighbourhood for the i-th pressure extends from the (`i-1`)-th pressure to the (`i+1`)-th pressure. At the endpoints it is assumed that the outside bin is of the same pressure range as the first inside bin. For other values of `e`, the neighbourhood is expanded linearly in each direction. If the `"lm"` method produces warnings about "prediction from a rank-deficient fit", a larger value of `"e"` should be used.

`debug`

an integer specifying whether debugging information is to be printed during the processing. This is a general parameter that is used by many `oce` functions. Generally, setting `debug=0` turns off the printing, while higher values suggest that more information be printed. If one function calls another, it usually reduces the value of `debug` first, so that a user can often obtain deeper debugging by specifying higher `debug` values.

### Details

The `"approx"` method is best for bottle data, in which the usual task is to interpolate from a coarse sampling grid to a finer one. For CTD data, the `"boxcar"` method is the more common choice, because the task is normally to sub-sample, and some degree of smoothing is usually desired. (The `"lm"` method is quite slow, and the results are similar to those of the boxcar method.)

Note that a sort of numerical cabeling effect can result from this procedure, but it can be avoided as follows

    xd <- ctdDecimate(x)
    xd[["sigmaTheta"]] <- swSigmaTheta(xd[["salinity"]],xd[["temperature"]],xd[["pressure"]])

### Value

An object of `ctd-class`, with pressures that are as set by the `"p"` parameter and all other properties modified appropriately.

### A note about flags

Data-quality flags contained within the original object are ignored by this function, and the returned value contains no such flags. This is because such flags represent an assessment of the original data, not of quantities derived from those data. This function produces a warning to this effect. The recommended practice is to use `handleFlags` or some other means to deal with flags before calling the present function.

### Author(s)

Dan Kelley

### References

R.F. Reiniger and C.K. Ross, 1968. A method of interpolation with application to oceanographic data. Deep Sea Research, 15, 185-193.

### See Also

The documentation for `ctd-class` explains the structure of CTD objects, and also outlines the other functions dealing with them.

Other things related to `ctd` data: `[[,ctd-method`, `[[<-,ctd-method`, `as.ctd`, `cnvName2oceName`, `ctd-class`, `ctdFindProfiles`, `ctdRaw`, `ctdTrim`, `ctd`, `handleFlags,ctd-method`, `oceNames2whpNames`, `oceUnits2whpUnits`, `plot,ctd-method`, `plotProfile`, `plotScan`, `plotTS`, `read.ctd.itp`, `read.ctd.odf`, `read.ctd.sbe`, `read.ctd.woce.other`, `read.ctd.woce`, `read.ctd`, `subset,ctd-method`, `summary,ctd-method`, `woceNames2oceNames`, `woceUnit2oceUnit`, `write.ctd`

### Examples

    library(oce)
    data(ctd)
    plotProfile(ctd, "salinity", ylim=c(10, 0))
    p <- seq(0, 45, 1)
    ctd2 <- ctdDecimate(ctd, p=p)
    lines(ctd2[["salinity"]], ctd2[["pressure"]], col="blue")
    p <- seq(0, 45, 1)
    ctd3 <- ctdDecimate(ctd, p=p, method=function(x, y, xout)
                        predict(smooth.spline(x, y, df=30), xout)$y)
    lines(ctd3[["salinity"]], ctd3[["pressure"]], col="red")

[Package oce version 0.9-22 Index]