View source: R/gp.functions6.R
gprPredict | R Documentation |
Prediction of GPR model
gprPredict( train = NULL, inputNew = NULL, noiseFreePred = F, hyper = NULL, input = NULL, Y = NULL, mSR = NULL, Cov = NULL, gamma = NULL, nu = NULL, meanModel = 0, mu = 0 )
train |
A 'gpr' object obtained from 'gpr' function. Default to NULL. If NULL, learning is done based on the other given arguments; otherwise, prediction is made based on the trained model of class gpr'. |
inputNew |
Test input covariates. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate. |
noiseFreePred |
Logical. If TRUE, predictions will be noise-free. |
hyper |
The hyperparameters. Default to NULL. If not NULL, then it must be a list with appropriate names. |
input |
Input covariates. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate. |
Y |
Training response. It should be a matrix, where each column is a realisation. It can be a vector if there is only one realisation. |
mSR |
Subset size m if Subset of Regressors method is used for prediction. It must be smaller than the total sample size. |
Cov |
Covariance function(s) to use. Options are: 'linear', 'pow.ex', 'rat.qu', and 'matern'. Default to 'power.ex'. |
gamma |
Power parameter used in powered exponential kernel function. It must be 0<gamma<=2. |
nu |
Smoothness parameter of the Matern class. It must be a positive value. |
meanModel |
Type of mean function. It can be
Default to 0. If argument 'mu' is specified, then 'meanModel' will be set to 'userDefined'. |
mu |
Mean function specified by the user. It must be a vector. Its length must be the same as the sample size, that is, nrow(response). |
A list containing
Mean of predictions
Standard deviation of predictions
Test input data
Logical. If TRUE, predictions are noise-free.
Objects of 'gpr' class.
## See examples in vignettes: # vignette("gpr_ex1", package = "GPFDA") # vignette("gpr_ex2", package = "GPFDA") # vignette("co2", package = "GPFDA")
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