replaceBiasedSigma: Replace Biased Backscattering Cross Sections
In JVAdams/EchoNet2Fish: Estimate Fish Abundance from Acoustic Echoes and Net Catch

Description Usage Arguments Details Value Examples

Replace biased backscattering cross sections (sigma_bs in m^2) with averages from unbiased sigma_bs using cells in the same layer and (if possible) transect.

replaceBiasedSigma(
  df,
  varNv,
  varsigmabs,
  varTranLay,
  varLay = varTranLay[-1],
  Nvcut = 0.1
)

`df`	A data frame containing the variables specified by name below.
`varNv`	A character scalar, the name of the variable in `df` containing a numeric vector of the number of fish per acoustic sampling volume.
`varsigmabs`	A character scalar, the name of the variable in `df` containing a numeric vector of the backscattering cross section, sigma_bs in m^2.
`varTranLay`	A character vector, the names of the variable in `df` used to identify unique transect-layers. Include names of other needed by variables if `df` contains information from multiple waterbodies or time periods.
`varLay`	A character vector, the names of the variable in `df` used to identify unique layers. The default, `varTranLay`[-1], assumes that the first variable names in `varTranLay` identifies the transect. This must be a subset of `varTranLay`. Include names of other needed by variables if `df` contains information from multiple waterbodies or time periods.
`Nvcut`	A numeric scalar, the cutoff for Nv (specified by `VarNv`), above which all sigma_bs are considered biased, default 0.1. Any sigma_bs with missing values are left unchanged.

TS = 10*log10(sigma_bs).

A numeric vector of target strengths, TS, in dB, same length as sigmabs.

mydf <- data.frame(nv=c(0.01, 0.1, 0.2, 0.2, 0.3, 0.05, 0.01),
  sig=1:7, tran=c(1, 1, 1, 2, 2, 1, 1), layer=c(1, 1, 1, 2, 2, 2, 2))
sig <- replaceBiasedSigma(df=mydf, varNv="nv", varsigmabs="sig",
  varTranLay=c("tran", "layer"))
cbind(mydf, sig)