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

View source: R/learnParameters.R

`learnParameters`

performs maximum likelihood parameter estimation in the PSGP framework.

1 | ```
learnParameters(object)
``` |

`object` |
a list object of intamap type. Most arguments necessary for
interpolation are passed through this object.
See |

The Projected Spatial Gaussian Process (PSGP) framework provides an approximation to the full Gaussian process in which the observations are projected sequentially onto an optimal subset of 'active' observations. Spatial interpolation is done using a mixture of covariance kernels (exponential and Matern 5/2).

The function `learnParameters`

is an internal function for estimating the
parameters of the covariance function given the data, using a maximum likelihood
approach. A valid intamap `object`

must be passed in.

PSGP is able to also take the measurement characteristics (i.e. errors) into
account using possibly many error models. For each error model, assumed Gaussian, the
error variance can be specified. The vector
`object$observations$oevar`

contains all variances for the error models (one
value per error model).
Which error model is used for a given observation is determined by the
`object$observations$oeid`

vector of indices, which specifies the index of the
model to be used for each observation.

It is advised to use the intamap wrapper `estimateParameters`

rather than calling this method directly.

Ben Ingram, Remi Barillec

Csato and Opper, 2002; Ingram et al., 2008

`makePrediction`

,
`learnParameters`

,
`estimateParameters`

,
`createIntamapObject`

1 | ```
# see example in estimateParameters
``` |

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