denoise: Noise filtering through principal components
In remote: Empirical Orthogonal Teleconnections in R

Description Usage Arguments Value See Also Examples

Filter noise from a RasterStack by decomposing into principal components and subsequent reconstruction using only a subset of components

1 2	denoise(x, k = NULL, expl.var = NULL, weighted = TRUE, use.cpp = TRUE, verbose = TRUE, ...)

`x`	RasterStack to be filtered
`k`	number of components to be kept for reconstruction (ignored if `expl.var` is supplied)
`expl.var`	minimum amount of variance to be kept after reconstruction (should be set to NULL or omitted if `k` is supplied)
`weighted`	logical. If `TRUE` the covariance matrix will be geographically weighted using the cosine of latitude during decomposition (only important for lat/lon data)
`use.cpp`	logical. Determines whether to use Rcpp functionality, defaults to `TRUE`.
`verbose`	logical. If `TRUE` some details about the calculation process will be output to the console
`...`	additional arguments passed to `princomp`

a denoised RasterStack

anomalize, deseason

data("vdendool")
vdd_dns <- denoise(vdendool, expl.var = 0.8)

opar <- par(mfrow = c(1,2))
plot(vdendool[[1]], main = "original")
plot(vdd_dns[[1]], main = "denoised")
par(opar)