grid.predict: Helper function for applying predict methods to stacks of...
In debangs/RSAGA: SAGA Geoprocessing and Terrain Analysis in R

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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.local.function or multi.focal.function.

1 2	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.local.function` or `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.local.function (or 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.local.function / 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.

focal.function, multi.local.function, multi.focal.function

## 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.local.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.local.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)