denoise: Noise filtering through principal components.

In environmentalinformatics-marburg/Reot: Empirical Orthogonal Teleconnections in R

Description

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

Usage

denoise(data, k = NULL, expl.var = 0.95, weighted = TRUE, ...)

Arguments

data	RasterStack to be filtered
k	number of components to be kept for reconstruction (set this to NULL if you supply expl.var)
expl.var	minimum amount of variance to be kept after reconstruction (not used 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)
...	additional arguments passed to princomp

Value

a denoised RasterStack

See Also

anomalize, deseason

Examples

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)

environmentalinformatics-marburg/Reot documentation built on May 16, 2019, 7:50 a.m.