| predict.gp {mlegp} | R Documentation |
Gaussian Process Predictions
Description
Gaussian Process Predictions
Usage
## S3 method for class 'gp'
predict(object, newData = object$X, se.fit = FALSE, ...)
Arguments
object |
an object of class |
newData |
an optional data frame or matrix with rows corresponding to inputs for which to predict.
If omitted, the design matrix |
se.fit |
a switch indicating if standard errors are desired |
... |
for compatibility with generic method |
Details
The Gaussian process is used to predict output at the design points newData; if the logical se.fit is set to TRUE, standard errors (standard deviations of the predicted values) are also calculated. Note that if the Gaussian process contains a nugget term, these standard deviations correspond to standard deviations of predicted expected values, and NOT standard deviations of predicted observations. However, the latter can be obtained by noting that the variance of a predicted observation equals the variance of the predicted expected value plus a nugget term.
If newData is equal to the design matrix of object (the default), and there is no nugget term, the Gaussian process interpolates the observations and the
predictions will be identical to component Z of object. For cross-validation, the function CV should be used.
Value
predict.gp produces a vector of predictions. If se.fit is TRUE, a list with the following components is returned:
fit |
vector as above |
se.fit |
standard error of the predictions |
Note
for predictions with gp.list objects, call predict.gp separately for each gp in the list
Author(s)
Garrett M. Dancik dancikg@easternct.edu
References
https://github.com/gdancik/mlegp/
See Also
For cross-validated predictions, see CV
Examples
x = -5:5; y = sin(x) + rnorm(length(x), sd = 0.001)
fit = mlegp(x,y)
predict(fit, matrix(c(2.4, 3.2)))
## predictions at design points match the observations
## (because there is no nugget)
round(predict(fit) - fit$Z, 6)
# this is not necessarily true if there is a nugget
fit = mlegp(x,y, min.nugget = 0.01)
round(predict(fit) - fit$Z,6)