View source: R/PCA.geoChronR.R
pcaEns | R Documentation |
Ensemble PCA, or Monte Carlo Empirical Orthogonal Functions
pcaEns(
bin.list,
method = "ppca",
weights = NA,
pca.type = "corr",
gaussianize = TRUE,
n.pcs = 8,
n.ens = 1000,
simulateTrendInNull = FALSE
)
bin.list |
A list of binned data, the output of binTs() |
method |
What method to use for PCA? pcaMethods::listPcaMethods() for options. "ppca" is default. Other options may not work in GeoChronR. |
weights |
Vector of weights to apply to timeseries in the bin.list |
pca.type |
Correlation ("corr" - default) or Covariance ("cov"), matrix |
gaussianize |
Map input data to a standard Gaussian distribution? This is only relevant for correlation matrices, covariance matrices will not be gaussianized. (default = TRUE) |
n.pcs |
number of PCs/EOFs to calculate |
n.ens |
how many ensemble members to calculate |
simulateTrendInNull |
Should the null include the trend? |
View a full-fledged example of how to use this function.
Other pca:
ar1Surrogates()
,
createSyntheticTimeseries()
,
plotPcaEns()
,
plotScreeEns()
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