WAPLS.w2 {fxTWAPLS} | R Documentation |
WA-PLS training function v2
Description
WA-PLS training function, which can perform fx
correction.
1/fx
correction will be applied at step 2 and step 7.
Usage
WAPLS.w2(
modern_taxa,
modern_climate,
nPLS = 5,
usefx = FALSE,
fx_method = "bin",
bin = NA
)
Arguments
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 |
Value
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 (u* in the WA-PLS paper).
comp
each component extracted (will be used in step 7 regression).
u
taxon optimum 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.
See Also
fx
, TWAPLS.w
, and
WAPLS.predict.w
Examples
## 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_Tmin2 <- fxTWAPLS::WAPLS.w2(taxa, modern_pollen$Tmin, nPLS = 5)
fit_f_Tmin2 <- fxTWAPLS::WAPLS.w2(
taxa,
modern_pollen$Tmin,
nPLS = 5,
usefx = TRUE,
fx_method = "bin",
bin = 0.02
)
## End(Not run)