TWAPLS.w | R Documentation |
TWA-PLS training function, which can perform fx
correction.
1/fx^2
correction will be applied at step 7.
TWAPLS.w( modern_taxa, modern_climate, nPLS = 5, usefx = FALSE, fx_method = "bin", bin = NA )
modern_taxa |
The modern taxa abundance data, each row represents a sampling site, each column represents a taxon. |
modern_climate |
The modern climate value at each sampling site. |
nPLS |
The number of components to be extracted. |
usefx |
Boolean flag on whether or not use |
fx_method |
Binned or p-spline smoothed |
bin |
Binwidth to get fx, needed for both binned and p-splined method.
if |
A list of the training results, which will be used by the predict function. Each element in the list is described below:
fit
the fitted values using each number of components.
x
the observed modern climate values.
taxon_name
the name of each taxon.
optimum
the updated taxon optimum
comp
each component extracted (will be used in step 7 regression).
u
taxon optimum for each component (step 2).
t
taxon tolerance for each component (step 2).
z
a parameter used in standardization for each component (step 5).
s
a parameter used in standardization for each component (step 5).
orth
a list that stores orthogonalization parameters (step 4).
alpha
a list that stores regression coefficients (step 7).
meanx
mean value of the observed modern climate values.
nPLS
the total number of components extracted.
fx
, TWAPLS.predict.w
, and
WAPLS.w
## Not run: # Load modern pollen data modern_pollen <- read.csv("/path/to/modern_pollen.csv") # Extract taxa taxaColMin <- which(colnames(modern_pollen) == "taxa0") taxaColMax <- which(colnames(modern_pollen) == "taxaN") taxa <- modern_pollen[, taxaColMin:taxaColMax] # Training fit_t_Tmin <- fxTWAPLS::TWAPLS.w(taxa, modern_pollen$Tmin, nPLS = 5) fit_tf_Tmin <- fxTWAPLS::TWAPLS.w( taxa, modern_pollen$Tmin, nPLS = 5, usefx = TRUE, fx_method = "bin", bin = 0.02 ) ## End(Not run)
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