hyperposterior {MagmaClustR}

Compute the hyper-posterior distribution in Magma

Description

Compute the parameters of the hyper-posterior Gaussian distribution of the mean process in Magma (similarly to the expectation step of the EM algorithm used for learning). This hyper-posterior distribution, evaluated on a grid of inputs provided through the grid_inputs argument, is a key component for making prediction in Magma, and is required in the function pred_magma.

Usage

hyperposterior(
  trained_model = NULL,
  data = NULL,
  hp_0 = NULL,
  hp_i = NULL,
  kern_0 = NULL,
  kern_i = NULL,
  prior_mean = NULL,
  grid_inputs = NULL,
  pen_diag = 1e-10
)

Arguments

trained_model

A list, containing the information coming from a Magma model, previously trained using the train_magma function. If trained_model is not provided, the arguments data, hp_0, hp_i, kern_0, and kern_i are all required.

data

A tibble or data frame. Required columns: 'Input', 'Output'. Additional columns for covariates can be specified. The 'Input' column should define the variable that is used as reference for the observations (e.g. time for longitudinal data). The 'Output' column specifies the observed values (the response variable). The data frame can also provide as many covariates as desired, with no constraints on the column names. These covariates are additional inputs (explanatory variables) of the models that are also observed at each reference 'Input'. Recovered from trained_model if not provided.

hp_0

A named vector, tibble or data frame of hyper-parameters associated with kern_0. Recovered from trained_model if not provided.

hp_i

A tibble or data frame of hyper-parameters associated with kern_i. Recovered from trained_model if not provided.

kern_0

A kernel function, associated with the mean GP. Several popular kernels (see The Kernel Cookbook) are already implemented and can be selected within the following list:

• "SE": (default value) the Squared Exponential Kernel (also called Radial Basis Function or Gaussian kernel),

• "LIN": the Linear kernel,

• "PERIO": the Periodic kernel,

• "RQ": the Rational Quadratic kernel. Compound kernels can be created as sums or products of the above kernels. For combining kernels, simply provide a formula as a character string where elements are separated by whitespaces (e.g. "SE + PERIO"). As the elements are treated sequentially from the left to the right, the product operator '*' shall always be used before the '+' operators (e.g. 'SE * LIN + RQ' is valid whereas 'RQ + SE * LIN' is not). Recovered from trained_model if not provided.

kern_i

A kernel function, associated with the individual GPs. ("SE", "PERIO" and "RQ" are aso available here). Recovered from trained_model if not provided.

prior_mean

Hyper-prior mean parameter of the mean GP. This argument, can be specified under various formats, such as:

• NULL (default). The hyper-prior mean would be set to 0 everywhere.

• A number. The hyper-prior mean would be a constant function.

• A vector of the same length as all the distinct Input values in the data argument. This vector would be considered as the evaluation of the hyper-prior mean function at the training Inputs.

• A function. This function is defined as the hyper-prior mean.

• A tibble or data frame. Required columns: Input, Output. The Input values should include at least the same values as in the data argument.

grid_inputs

A vector or a data frame, indicating the grid of additional reference inputs on which the mean process' hyper-posterior should be evaluated.

pen_diag

A number. A jitter term, added on the diagonal to prevent numerical issues when inverting nearly singular matrices.

Value

A list gathering the parameters of the mean processes' hyper-posterior distributions, namely:

• mean: A tibble, the hyper-posterior mean parameter evaluated at each training Input.

• cov: A matrix, the covariance parameter for the hyper-posterior distribution of the mean process.

• pred: A tibble, the predicted mean and variance at Input for the mean process' hyper-posterior distribution under a format that allows the direct visualisation as a GP prediction.

Examples

TRUE