66 lines
2.6 KiB
R
66 lines
2.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/distrib.extrapol.R
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\name{extrapol.freq}
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\alias{extrapol.freq}
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\title{Read frequencies krigging}
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\usage{
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extrapol.freq(x, min.coord, max.coord, grid.grain = 100, coords, otus.table,
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cutoff = 0.001, return.metabarcoding.data = FALSE)
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}
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\arguments{
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\item{x}{a vector or matrix from a row-normalized read table
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\code{\link{metabarcoding.data}} object}
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\item{min.coord}{a vector of length = 2 indicating the minimum values of x and y
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coordinates to be used for the predicted grid}
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\item{max.coord}{a vector of length = 2 indicating the maximum values of x and y
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coordinates to be used for the predicted grid}
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\item{grid.grain}{an integer indicating the resolution (i.e. nb of subpoints) in x and y
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coordinates required for the predicted grid}
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\item{coords}{a dataframe containing the x and y coordinates of the abundances
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from x to be extrapolated.}
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\item{otus.table}{a motus data.frame containing motus informations of x}
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\item{cutoff}{a cutoff below which abundances are set to 0.
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This threshold also determines the value to be added to 0 values for log10
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transformation}
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\item{return.metabarcoding.data}{if \code{TRUE}, returns a \code{\link{metabarcoding.data}} object. Default is \code{FALSE}}
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}
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\value{
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either a dataframe or a S3 object with a structure similar to \code{\link{metabarcoding.data}} object.
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The number of samples corresponds to the predicted points.
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The two last columns (if \code{return.metabarcoding.data==F}) or sample data.frame contains x y coordinates of the predicted grid
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The all but last two columns (if \code{return.metabarcoding.data==F}) or read matrix contains the predicted log10 transformed relative abundances
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instead of reads counts
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If \code{return.metabarcoding.data==F} the motus data.frame contains the motus informations from x
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}
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\description{
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Extrapolates read frequencies from a \code{\link{metabarcoding.data}} object in space for a finer resolution
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}
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\examples{
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data(termes)
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#Create dummy spatial coordinates
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attr(termes, "samples")[c("x", "y")] = expand.grid(1:7,1:3)
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#compute frequencies
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attr(termes, "layers")[["reads.freq"]] = normalize(termes, MARGIN=1)$reads
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# Getting extrapolations
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termes.pred = extrapol.freq(attr(termes, "layers")[["reads.freq"]], min.coord=c(1,1), max.coord=c(7,3),
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grid.grain=100,termes$samples[,c("x", "y")], termes$motus, cutoff=1e-3)
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head(termes.pred$reads)
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}
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\seealso{
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\code{\link{map.extrapol.freq}} as well as \code{sp} and \code{gstat} packages
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}
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\author{
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Lucie Zinger
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}
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