GPModel: Create a 'GPModel' object

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

Create a GPModel object

Description

Create a GPModel which contains a Gaussian process and / or mixed effects model with grouped random effects

Usage

GPModel(likelihood = "gaussian", group_data = NULL,
  group_rand_coef_data = NULL, ind_effect_group_rand_coef = NULL,
  drop_intercept_group_rand_effect = NULL, gp_coords = NULL,
  gp_rand_coef_data = NULL, cov_function = "exponential",
  cov_fct_shape = 0.5, gp_approx = "none", cov_fct_taper_range = 1,
  cov_fct_taper_shape = 0, num_neighbors = 20L,
  vecchia_ordering = "random", num_ind_points = 500L,
  matrix_inversion_method = "cholesky", seed = 0L, cluster_ids = NULL,
  free_raw_data = FALSE, vecchia_approx = NULL, vecchia_pred_type = NULL,
  num_neighbors_pred = NULL)

Arguments

likelihood	A string specifying the likelihood function (distribution) of the response variable. Available options: "gaussian" "bernoulli_probit": binary data with Bernoulli likelihood and a probit link function "bernoulli_logit": binary data with Bernoulli likelihood and a logit link function "gamma" "poisson"
group_data	A vector or matrix whose columns are categorical grouping variables. The elements being group levels defining grouped random effects. The elements of 'group_data' can be integer, double, or character. The number of columns corresponds to the number of grouped (intercept) random effects
group_rand_coef_data	A vector or matrix with numeric covariate data for grouped random coefficients
ind_effect_group_rand_coef	A vector with integer indices that indicate the corresponding categorical grouping variable (=columns) in 'group_data' for every covariate in 'group_rand_coef_data'. Counting starts at 1. The length of this index vector must equal the number of covariates in 'group_rand_coef_data'. For instance, c(1,1,2) means that the first two covariates (=first two columns) in 'group_rand_coef_data' have random coefficients corresponding to the first categorical grouping variable (=first column) in 'group_data', and the third covariate (=third column) in 'group_rand_coef_data' has a random coefficient corresponding to the second grouping variable (=second column) in 'group_data'
drop_intercept_group_rand_effect	A vector of type logical (boolean). Indicates whether intercept random effects are dropped (only for random coefficients). If drop_intercept_group_rand_effect[k] is TRUE, the intercept random effect number k is dropped / not included. Only random effects with random slopes can be dropped.
gp_coords	A matrix with numeric coordinates (= inputs / features) for defining Gaussian processes
gp_rand_coef_data	A vector or matrix with numeric covariate data for Gaussian process random coefficients
cov_function	A string specifying the covariance function for the Gaussian process. Available options: "exponential", "gaussian", "matern", "powered_exponential", "wendland" For "exponential", "gaussian", and "powered_exponential", we use parametrization of Diggle and Ribeiro (2007) For "matern", we use the parametrization of Rasmussen and Williams (2006) For "wendland", we use the parametrization of Bevilacqua et al. (2019, AOS)
cov_fct_shape	A numeric specifying the shape parameter of the covariance function (=smoothness parameter for Matern covariance) This parameter is irrelevant for some covariance functions such as the exponential or Gaussian
gp_approx	A string specifying the large data approximation for Gaussian processes. Available options: "none": No approximation "vecchia": A Vecchia approximation; see Sigrist (2022, JMLR for more details) "tapering": The covariance function is multiplied by a compactly supported Wendland correlation function
cov_fct_taper_range	A numeric specifying the range parameter of the Wendland covariance function and Wendland correlation taper function. We follow the notation of Bevilacqua et al. (2019, AOS)
cov_fct_taper_shape	A numeric specifying the shape (=smoothness) parameter of the Wendland covariance function and Wendland correlation taper function. We follow the notation of Bevilacqua et al. (2019, AOS)
num_neighbors	An integer specifying the number of neighbors for the Vecchia approximation. Note: for prediction, the number of neighbors can be set through the 'num_neighbors_pred' parameter in the 'set_prediction_data' function. By default, num_neighbors_pred = 2 * num_neighbors. Further, the type of Vecchia approximation used for making predictions is set through the 'vecchia_pred_type' parameter in the 'set_prediction_data' function
vecchia_ordering	A string specifying the ordering used in the Vecchia approximation. Available options: "none": the default ordering in the data is used "random": a random ordering
num_ind_points	An integer specifying the number of inducing points / knots for, e.g., a predictive process approximation
matrix_inversion_method	A string specifying the method used for inverting covariance matrices. Available options: "cholesky": Cholesky factorization "iterative": iterative methods. Only supported for non-Gaussian likelihoods with a Vecchia-Laplace approximation. This a combination of conjugate gradient, Lanczos algorithm, and other methods
seed	An integer specifying the seed used for model creation (e.g., random ordering in Vecchia approximation)
cluster_ids	A vector with elements indicating independent realizations of random effects / Gaussian processes (same values = same process realization). The elements of 'cluster_ids' can be integer, double, or character.
free_raw_data	A boolean. If TRUE, the data (groups, coordinates, covariate data for random coefficients) is freed in R after initialization
vecchia_approx	Discontinued. Use the argument gp_approx instead
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

Value

A GPModel containing ontains a Gaussian process and / or mixed effects model with grouped random effects

Author(s)

Fabio Sigrist

Examples

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

data(GPBoost_data, package = "gpboost")

#--------------------Grouped random effects model: single-level random effect----------------
gp_model <- GPModel(group_data = group_data[,1], likelihood="gaussian")

#--------------------Gaussian process model----------------
gp_model <- GPModel(gp_coords = coords, cov_function = "exponential",
                    likelihood="gaussian")

#--------------------Combine Gaussian process with grouped random effects----------------
gp_model <- GPModel(group_data = group_data,
                    gp_coords = coords, cov_function = "exponential",
                    likelihood="gaussian")

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