predict.ann {validann} | R Documentation |
Predict new examples using a trained neural network.
Description
Predict new examples using a trained neural network.
Usage
## S3 method for class 'ann'
predict(object, newdata = NULL, derivs = FALSE, ...)
Arguments
object |
an object of class ‘ann’ as returned by function |
newdata |
matrix, data frame or vector of input data.
A vector is considered to comprise examples of a single input or
predictor variable. If |
derivs |
logical; should derivatives of hidden and output nodes be
returned? Default is |
... |
additional arguments affecting the predictions produced (not currently used). |
Details
This function is a method for the generic function predict()
for class ‘ann’. It can be invoked by calling predict(x)
for an
object x
of class ‘ann’.
predict.ann
produces predicted values, obtained by evaluating the
‘ann’ model given newdata
, which contains the inputs to be used
for prediction. If newdata
is omitted, the
predictions are based on the data used for the fit.
Derivatives may be returned for sensitivity analyses, for example.
Value
if derivs = FALSE
, a vector of predictions is returned.
Otherwise, a list with the following components is returned:
values |
matrix of values returned by the trained ANN. |
derivs |
matrix of derivatives of hidden (columns |
See Also
Examples
## fit 1-hidden node `ann' model to ar9 data
data("ar9")
samp <- sample(1:1000, 200)
y <- ar9[samp, ncol(ar9)]
x <- ar9[samp, -ncol(ar9)]
x <- x[, c(1,4,9)]
fit <- ann(x, y, size = 1, act_hid = "tanh", act_out = "linear", rang = 0.1)
## get model predictions based on a new sample of ar9 data.
samp <- sample(1:1000, 200)
y <- ar9[samp, ncol(ar9)]
x <- ar9[samp, -ncol(ar9)]
x <- x[, c(1,4,9)]
sim <- predict(fit, newdata = x)
## if derivatives are required...
tmp <- predict(fit, newdata = x, derivs = TRUE)
sim <- tmp$values
derivs <- tmp$derivs