wa {analogue}  R Documentation 
Implements the weighted averaging transfer function methodology. Tolerance downweighting and inverse and classicial deshrinking are supported.
wa(x, ...)
## Default S3 method:
wa(x, env,
deshrink = c("inverse", "classical", "expanded", "none", "monotonic"),
tol.dw = FALSE, useN2 = TRUE,
na.tol = c("min","mean","max"),
small.tol = c("min","mean","fraction","absolute"),
min.tol = NULL, f = 0.1, ...)
## S3 method for class 'formula'
wa(formula, data, subset, na.action,
deshrink = c("inverse", "classical", "expanded", "none", "monotonic"),
tol.dw = FALSE, useN2 = TRUE, na.tol = c("min","mean","max"),
small.tol = c("min","mean","fraction","absolute"), min.tol = NULL,
f = 0.1,..., model = FALSE)
## S3 method for class 'wa'
fitted(object, ...)
## S3 method for class 'wa'
residuals(object, ...)
## S3 method for class 'wa'
coef(object, ...)
waFit(x, y, tol.dw, useN2, deshrink, na.tol, small.tol,
min.tol, f)
x 
The species training set data 
env , y 
The response vector 
deshrink 
Which deshrinking method to use? One of

tol.dw 
logical; should species with wider tolerances be given lower weight? 
useN2 
logical; should Hill's N2 values be used to produce unbiased tolerances? 
na.tol 
character; method to use to replace missing ( 
small.tol 
character; method to replace small tolerances. See Details. 
min.tol 
numeric; threshold below which tolerances are treated as being ‘small’. Default is not to replace small tolerances. 
f 
numeric, 
formula 
a model formula 
data 
an optional data frame, list or environment (or object
coercible by 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
na.action 
a function which indicates what should happen when
the data contain 
model 
logical. If 
object 
an Object of class 
... 
arguments to other methods. 
A typical model has the form response ~ terms
where response
is the (numeric) response vector (the variable to
be predicted) and terms
is a series of terms which specifies a
linear predictor for response
. A terms specification of the
form first + second
indicates all the terms in first
together with all the terms in second
with duplicates
removed. A specification of .
is shorthand for all terms in
data
not already included in the model.
Species that have very small tolerances can dominate reconstructed
values if tolerance downweighting is used. In wa
, small
tolerances are defined as a tolerance that is <
min.tol
. The default is to not replace small tolerances, and
the user needs to specify suitable values of min.tol
. Function
tolerance
may be of use in computing tolerances before
fitting the WA model.
Small tolerances can be adjusted in several ways:
min
small tolerances are replaced by the smallest
observed tolerance that is greater than, or equal to,
min.tol
. With this method, the replaced values will be no
smaller than any other observed tolerance. This is the default in
analogue.
mean
small tolerances are replaced by the average
observed tolerance from the set that are greater than, or equal
to, min.tol
.
fraction
small tolerances are replaced by the
fraction, f
, of the observed environmental gradient in the
training set, env
.
absolute
small tolerances are replaced by
min.tol
.
Function waFit
is the workhorse implementing the actual WA
computations. It performs no checks on the input data and returns a
simple list containing the optima, tolernances, model tolerances,
fitted values, coefficients and the numbers of samples and
species. See Value below for details of each component.
An object of class "wa"
, a list with the following components:
wa.optima 
The WA optima for each species in the model. 
tolerances 
The actual tolerances calculated (these are weighted standard deviations). 
model.tol 
The tolerances used in the WA model
computations. These will be similar to 
fitted.values 
The fitted values of the response for each of the training set samples. 
residuals 
Model residuals. 
coefficients 
Deshrinking coefficients. Note that in the case of

rmse 
The RMSE of the model. 
r.squared 
The coefficient of determination of the observed and fitted values of the response. 
avg.bias , max.bias 
The average and maximum bias statistics. 
n.samp , n.spp 
The number of samples and species in the training set. 
deshrink 
The deshrinking regression method used. 
tol.dw 
logical; was tolerance downweighting applied? 
call 
The matched function call. 
orig.x 
The training set species data. 
orig.env 
The response data for the training set. 
options.tol 
A list, containing the values of the arguments

terms , model 
Model 
Gavin L. Simpson and Jari Oksanen
mat
for an alternative transfer function method.
data(ImbrieKipp)
data(SumSST)
## fit the WA model
mod < wa(SumSST ~., data = ImbrieKipp)
mod
## extract the fitted values
fitted(mod)
## residuals for the training set
residuals(mod)
## deshrinking coefficients
coef(mod)
## diagnostics plots
par(mfrow = c(1,2))
plot(mod)
par(mfrow = c(1,1))
## caterpillar plot of optima and tolerances
caterpillarPlot(mod) ## observed tolerances
caterpillarPlot(mod, type = "model") ## with tolerances used in WA model
## plot diagnostics for the WA model
par(mfrow = c(1,2))
plot(mod)
par(mfrow = c(1,1))
## tolerance DW
mod2 < wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE,
min.tol = 2, small.tol = "min")
mod2
## compare actual tolerances to working values
with(mod2, rbind(tolerances, model.tol))
## tolerance DW
mod3 < wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE,
min.tol = 2, small.tol = "mean")
mod3
## fit a WA model with monotonic deshrinking
mod4 < wa(SumSST ~., data = ImbrieKipp, deshrink = "monotonic")
mod4
## extract the fitted values
fitted(mod4)
## residuals for the training set
residuals(mod4)