predict.gp: Prediction Method for the '"gp"' S3 Class
In kergp: Gaussian Process Laboratory

Description Usage Arguments Details Value Author(s) See Also

Prediction method for the "gp" S3 class.

## S3 method for class 'gp'
predict(object, newdata,
        type = ifelse(object$trendKnown, "SK", "UK"), 
        seCompute = TRUE, covCompute = FALSE,
        lightReturn = FALSE, biasCorrect = FALSE,
        forceInterp,        
        ...)

`object`	An object with S3 class `"gp"`.
`newdata`	A data frame containing all the variables required for prediction: inputs and trend variables, if applicable.
`type`	A character string corresponding to the GP "kriging" family, to be chosen between simple kriging (`"SK"`), or universal kriging (`"UK"`).
`seCompute`	Optional logical. If `FALSE`, only the kriging mean is computed. If `TRUE`, the kriging variance (actually, the corresponding standard deviation) and prediction intervals are computed too.
`covCompute`	Logical. If `TRUE` the covariance matrix is computed.
`lightReturn`	Optional logical. If `TRUE`, `c` and `cStar` are not returned. This should be reserved to expert users who want to save memory and know that they will not miss these values.
`biasCorrect`	Optional logical to correct bias in the UK variance and covariances. Default is `FALSE`. See Details below.
`forceInterp`	Logical used to force a nugget-type prediction. If `TRUE`, the noise will be interpreted as a nugget effect. This argument is likely to be removed in the future.
`...`	Not used yet.

The estimated (UK) variance and covariances are NOT multiplied by n/(n-p) by default (n and p denoting the number of rows and columns of the trend matrix F). Recall that this correction would contribute to limit bias: it would totally remove it if the correlation parameters were known (which is not the case here). However, this correction is often ignored in the context of computer experiments, especially in adaptive strategies. It can be activated by turning biasCorrect to TRUE, when type = "UK"

A list with the following elements.

`mean`	GP mean ("kriging") predictor (including the trend) computed at `newdata`.
`sd`	GP prediction ("kriging") standard deviation computed at `newdata`. Not computed if `seCompute` is `FALSE`.
`sdSK`	Part of the above standard deviation corresponding to simple kriging (coincides with `sd` when `type = "SK"`). Not computed if `seCompute` is `FALSE`.
`trend`	The computed trend function, evaluated at `newdata`.
`cov`	GP prediction ("kriging") conditional covariance matrix. Not computed if `covCompute` is `FALSE` (default).
`lower95,`
`upper95`	Bounds of the 95 % GP prediction interval computed at `newdata` (to be interpreted with special care when parameters are estimated, see description above). Not computed if `seCompute` is `FALSE`.
`c`	An auxiliary matrix c, containing all the covariances between the points in `newdata` and those in the initial design. Not returned if `lightReturn` is `TRUE`.
`cStar`	An auxiliary vector, equal to L^(-1) %% c* where L is the Cholesky root of the covariance matrix C used in the estimation. Not returned if `lightReturn` is `TRUE`.