# Prediction from a model fit The following functions can be used to compute point predictions and/or various measures of uncertainty associated to such predictions: `*``predict` can be used for prediction of the response variable by its expected value obtained as (the inverse link transformation of) the linear predictor ($\eta$) and more generally for terms of the form X _n$\beta$+Z _nL v , for new design matrices X _n and Z _n. `*`Various components of prediction variances and predictions intervals can also be computed using `predict`. The `get_`... functions are convenient extractors for such components; `*``get_predCov_var_fix` extracts a block of a prediction covariance matrix. It was conceived for the specific purpose of computing the spatial prediction covariances between two new sets of geographic locations, without computing the full covariance matrix for both the new locations and the original (fitted) locations. When one of the two sets of new locations is fixed while the other varies, some expensive computations can be performed once for all sets of new locations, and be provided as the `fix_X_ZAC.object` argument. The `preprocess_fix_corr` extractor is designed to compute this argument. ```r ## S3 method for class 'HLfit' predict(object, newdata = newX, newX = NULL, re.form = NULL, variances=list(), binding = FALSE, intervals = NULL, level = 0.95, blockSize = 2000L, type = "response", verbose=c(showpbar=eval(spaMM.getOption("barstyle"))), control=list(), na.action=na.omit, cluster_args=list(), ...) get_predCov_var_fix(object, newdata = NULL, fix_X_ZAC.object, fixdata, re.form = NULL, variances=list(disp=TRUE,residVar=FALSE,cov=FALSE), control=list(), ...) preprocess_fix_corr(object, fixdata, re.form = NULL, variances=list(residVar=FALSE, cov=FALSE), control=list()) get_fixefVar(...) get_predVar(..., variances=list(), which="predVar") get_residVar(...) get_respVar(...) get_intervals(..., intervals="predVar") ``` ## Arguments - `object`: The return object of fitting functions `HLfit,corrHLfit,HLCor`... returning an object inheriting from `HLfit` class. - `newdata`: Either NULL, a matrix or data frame, or a numeric vector. If `NULL`, the original data are reused. Otherwise, all variables required to evaluate model formulas must be included. Which variables are required may depend on other arguments: see prediction with given phi's example, also illustrating the syntax when formulas include an offset. If `newdata` is a numeric vector, its names (if any) are ignored. This makes it easier to use `predict` as an objective function for an optimization procedure such as `optim`, which calls the objective function on unnamed vectors. However, one must make sure that the order of elements in the vector is the order of first occurrence of the variables in the model formula. This order can be checked in the error message returned when calling `predict` on a `newX` vector of clearly wrong size, e.g. `predict(

predict function