grid.predict {RSAGA} | R Documentation |
This function can be used to apply the predict method
of hopefully any fitted predictive model pixel by pixel to a stack of grids
representing the explanatory variables. It is intended to be called
primarily by multi.focal.function
.
grid.predict(fit, predfun, trafo, control.predict, predict.column, trace = 0, location, ...)
fit |
a model object for which prediction is desired |
predfun |
optional prediction function; if missing, the
fit 's predict method is called. In some cases it
may be convenient to define a wrapper function for the
predict method that may be passed as predfun argument. |
trafo |
an optional function(x) that takes a data.frame
x and returns a data.frame with the same number of rows;
this is intended to perform transformations on the input variables,
e.g. derive a log-transformed variable from the raw input read from
the grids, or more complex variables such as the NDVI etc.;
the data.frame resulting from a call to trafo
(if provided) is passed to predfun |
control.predict |
an optional list of arguments to be passed on
to predfun ; this may be e.g. type="response" to
obtain probability prediction maps from a logistic regression model |
predict.column |
optional character string: Some predict methods (e.g.
predict.lda ) return a data.frame with several columns,
e.g. one column per class in a classification problem.
predict.column is used to pick the one that is of interest |
trace |
integer >=0: positive values give more (=2) or less (=1) information on predictor variables and predictions |
location |
optional location data received from multi.focal.function ;
is added to the newdata object that is passed on to predfun . |
... |
these arguments are provided by the calling function, usually
multi.focal.function . They contain the explanatory
(predictor) variables required by the fit model. |
grid.predict
is a simple wrapper function. First it binds
the arguments in ...
together in a data.frame
with the
raw predictor variables that have been read from their grids by
the caller, multi.focal.function
.
Then it calls the optional trafo
function to transform or combine
predictor variables (e.g. perform log transformations, ratioing, arithmetic
operations such as calculating the NDVI).
Finally the predfun
(or, typically, the default predict
method of fit
) is called, handing over the fit
, the predictor
data.frame
, and the optional control.predict
arguments.
grid.predict
returns the
result of the call to predfun
or the default predict
method.
Though grid.predict
can in principle deal with predict
methods returning factor variables, its usual caller
multi.focal.function
cannot; classification models should be
dealt with by setting a type="prob"
(for rpart
) or
type="response"
(for logistic regression and logistic additive model)
argument, for example (see second Example below).
Alexander Brenning
Brenning, A. (2008): Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models. In: J. Boehner, T. Blaschke, L. Montanarella (eds.), SAGA - Seconds Out (= Hamburger Beitraege zur Physischen Geographie und Landschaftsoekologie, 19), 23-32. http://www.environment.uwaterloo.ca/u/brenning/Brenning-2008-RSAGA.pdf
## Not run: # Assume that d is a data.frame with point observations # of a numerical response variable y and predictor variables # a, b, and c. # Fit a generalized additive model to y,a,b,c. # We want to model b and c as nonlinear terms: require(gam) fit <- gam(y ~ a + s(b) + s(c), data = d) multi.focal.function(in.grids = c("a", "b", "c"), out.varnames = "pred", fun = grid.predict, fit = fit ) # Note that the 'grid.predict' uses by default the # predict method of 'fit'. # Model predictions are written to a file named pred.asc ## End(Not run) ## Not run: # A fake example of a logistic additive model: require(gam) fit <- gam(cl ~ a + s(b) + s(c), data = d, family = binomial) multi.focal.function(in.grids = c("a", "b", "c"), out.varnames = "pred", fun = grid.predict, fit = fit, control.predict = list(type = "response") ) # 'control.predict' is passed on to 'grid.predict', which # dumps its contents into the arguments for 'fit''s # 'predict' method. # Model predictions are written to a file named pred.asc ## End(Not run)