wa {analogue} | R Documentation |
Implements the weighted averaging transfer function methodology. Tolerance down-weighting and inverse and classicial deshrinking are supported.
wa(x, ...) ## Default S3 method: wa(x, env, deshrink = c("inverse", "classical", "expanded", "none"), tol.dw = FALSE, useN2 = TRUE, na.tol = c("min","mean","max"), small.tol = c("min","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"), tol.dw = FALSE, ..., model = FALSE) ## S3 method for class 'wa': fitted(object, ...) ## S3 method for class 'wa': residuals(object, ...) ## S3 method for class 'wa': coef(object, ...)
x |
The species training set data |
env |
The response vector |
deshrink |
Which deshrinking method to use? One of
"inverse" or "classical" , "expanded" or
"none" |
tol.dw |
logical; should species with wider tolerances be given lower weight? |
useN2 |
logical; should Hill's N2 values be used to produce un-biased tolerances? |
na.tol |
character; method to use to replace missing (NA )
tolerances in WA computations. Missing values are replaced with the
minimum, average or maximum tolerance observed that is not
missing. |
small.tol |
character; method to replace small tolerances. See Details. |
min.tol |
numeric; threshold below which tolerances are treated as being ‘small’. |
f |
numeric, 0 < f < 1; fraction of environmental gradient
env to replace small tolerances with if small.tol =
"fraction" is specified. |
formula |
a model formula |
data |
an optional data frame, list or environment (or object
coercible by as.data.frame to a data frame) containing
the variables specified on the RHS of the model formula. If not found in
data , the variables are taken from
environment(formula) , typically the environment from which
wa is called. |
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 NA s. The default is set by the
na.action setting of options , and is na.fail if
that is unset. The 'factory-fresh' default is na.omit .
Another possible value is NULL , no action. Value
na.exclude can be useful. |
model |
logical. If TRUE the model frame is returned. |
object |
an Object of class "wa" , the result of a call to
wa . |
... |
arguments to other methods. |
A typical model has the form response ~ terms
whereresponse
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 down-weighting is used. In wa
, small
tolerances are defined as a tolerance that is <
min.tol
. These small tolerances can be adjusted in several
ways:
min
min.tol
. With this method, the replaced values will be no
smaller than any other observed tolerance. This is the default in
analogue.fraction
f
, of the observed environmental gradient in the
training set, env
.absolute
min.tol
.
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 tol , but will no
contain any NA s and any small tolerances will have been
replaced with the appropriate value. |
fitted.values |
The fitted values of the response for each of the training set samples. |
residuals |
Model residuals. |
coefficients |
Deshrinking coefficients. |
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 down-weighting applied? |
call |
The matched function call. |
orig.x |
The training set species data. |
orig.env |
The response data for the training set. |
terms, model |
Model terms and model.frame
components. Only returned by the formula method of
wa . |
Gavin L. Simpson and Jari Oksanen
mat
for an alternative transfer function method.
data(swapdiat) data(swappH) swapdiat <- swapdiat / 100 ## fit the WA model mod <- wa(swappH ~., data = swapdiat) 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)) ## tolerance DW mod2 <- wa(swappH ~., data = swapdiat, tol.dw = TRUE) ## tolerances with(mod2, tolerances) ## Imbrie and Kipp data(ImbrieKipp) data(SumSST) ik.wa <- wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE, min.tol = 2, small.tol = "min") ik.wa ## compare actual tolerances to working values with(ik.wa, rbind(tolerances, model.tol))