predict.GPModel: Make predictions for a 'GPModel'

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

Make predictions for a GPModel

Description

Make predictions for a GPModel

Usage

## S3 method for class 'GPModel'
predict(object, y = NULL, group_data_pred = NULL,
  group_rand_coef_data_pred = NULL, gp_coords_pred = NULL,
  gp_rand_coef_data_pred = NULL, cluster_ids_pred = NULL,
  predict_cov_mat = FALSE, predict_var = FALSE, cov_pars = NULL,
  X_pred = NULL, use_saved_data = FALSE, predict_response = TRUE,
  fixed_effects = NULL, fixed_effects_pred = NULL,
  vecchia_pred_type = NULL, num_neighbors_pred = NULL, ...)

Arguments

object	a GPModel
y	Observed data (can be NULL, e.g. when the model has been estimated already and the same data is used for making predictions)
group_data_pred	A vector or matrix with elements being group levels for which predictions are made (if there are grouped random effects in the GPModel)
group_rand_coef_data_pred	A vector or matrix with covariate data for grouped random coefficients (if there are some in the GPModel)
gp_coords_pred	A matrix with prediction coordinates (=features) for Gaussian process (if there is a GP in the GPModel)
gp_rand_coef_data_pred	A vector or matrix with covariate data for Gaussian process random coefficients (if there are some in the GPModel)
cluster_ids_pred	A vector with elements indicating the realizations of random effects / Gaussian processes for which predictions are made (set to NULL if you have not specified this when creating the GPModel)
predict_cov_mat	A boolean. If TRUE, the (posterior) predictive covariance is calculated in addition to the (posterior) predictive mean
predict_var	A boolean. If TRUE, the (posterior) predictive variances are calculated
cov_pars	A vector containing covariance parameters which are used if the GPModel has not been trained or if predictions should be made for other parameters than the trained ones
X_pred	A matrix with prediction covariate data for the fixed effects linear regression term (if there is one in the GPModel)
use_saved_data	A boolean. If TRUE, predictions are done using a priory set data via the function '$set_prediction_data' (this option is not used by users directly)
predict_response	A boolean. If TRUE, the response variable (label) is predicted, otherwise the latent random effects
fixed_effects	(usually not used) A numeric vector with additional external training data fixed effects. The length of this vector needs to equal the number of training data points. Used only for non-Gaussian data. For Gaussian data, this is ignored
fixed_effects_pred	(usually not used) A numeric vector with additional external prediction fixed effects. The length of this vector needs to equal the number of prediction points. Used only for non-Gaussian data. For Gaussian data, this is ignored
vecchia_pred_type	A string specifying the type of Vecchia approximation used for making predictions. This is discontinued here. Use the function 'set_prediction_data' to specify this
num_neighbors_pred	an integer specifying the number of neighbors for making predictions. This is discontinued here. Use the function 'set_prediction_data' to specify this
...	(not used, ignore this, simply here that there is no CRAN warning)

Value

Predictions from a GPModel. A list with three entries is returned:

• "mu" (first entry): predictive (=posterior) mean. For (generalized) linear mixed effects models, i.e., models with a linear regression term, this consists of the sum of fixed effects and random effects predictions

• "cov" (second entry): predictive (=posterior) covariance matrix. This is NULL if 'predict_cov_mat=FALSE'

• "var" (third entry) : predictive (=posterior) variances. This is NULL if 'predict_var=FALSE'

Author(s)

Fabio Sigrist

Examples

# See https://github.com/fabsig/GPBoost/tree/master/R-package for more examples


data(GPBoost_data, package = "gpboost")
# Add intercept column
X1 <- cbind(rep(1,dim(X)[1]),X)
X_test1 <- cbind(rep(1,dim(X_test)[1]),X_test)

#--------------------Grouped random effects model: single-level random effect----------------
gp_model <- fitGPModel(group_data = group_data[,1], y = y, X = X1,
                       likelihood="gaussian", params = list(std_dev = TRUE))
summary(gp_model)
# Make predictions
pred <- predict(gp_model, group_data_pred = group_data_test[,1], 
                X_pred = X_test1, predict_var = TRUE)
pred$mu # Predicted mean
pred$var # Predicted variances
# Also predict covariance matrix
pred <- predict(gp_model, group_data_pred = group_data_test[,1], 
                X_pred = X_test1, predict_cov_mat = TRUE)
pred$mu # Predicted mean
pred$cov # Predicted covariance


#--------------------Gaussian process model----------------
gp_model <- fitGPModel(gp_coords = coords, cov_function = "exponential",
                       likelihood="gaussian", y = y, X = X1, params = list(std_dev = TRUE))
summary(gp_model)
# Make predictions
pred <- predict(gp_model, gp_coords_pred = coords_test, 
                X_pred = X_test1, predict_cov_mat = TRUE)
pred$mu # Predicted (posterior) mean of GP
pred$cov # Predicted (posterior) covariance matrix of GP

gpboost documentation built on Oct. 24, 2023, 9:09 a.m.