Model class functions
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A full list of functions to be added here.
| Method | Description | |
|---|---|---|
covariance | D | The matrix D, the covariance matrix of the random effects |
Z | The matrix Z, the design matrix of the random effects | |
chol_D() | Generates the Cholesky decomposition of D | |
log_likelihood | For a given vector $u$ calculates the multivariate Gaussian log-likelihood with zero mean and covariance D | |
simulate_re() | Simulates a vector $u$ | |
sparse() | Choose whether to use sparse matrix methods | |
parameters | The parameters $\theta$ | |
formula | The random effects formula used to create D | |
update_parameters() | Updates $\theta$ and related matrices | |
nngp() | Sets or returns options for the nearest neighbour Gaussian process approximation | |
hsgp() | Sets or returns options for the Hilbert space Gaussian process approximation | |
mean | X | The matrix X, the design matrix of fixed effects |
parameters | The parameters $\beta$ | |
offset | The optional model offset | |
formula | The fixed effects formula used to create X | |
linear_predictor() | Generates the linear predictor | |
update_parameters() | Updates $\beta$ and related matrices | |
family | An R family object | |
var_par | The scale parameter | |
fitted() | Full linear predictor including random effects (either simulated or provided) | |
predict() | Predictions from the model at new data values | |
sim_data() | Simulates data from the model | |
Sigma() | Generates $\Sigma$ (or an approximation) | |
information_matrix() | Generates the information matrix | |
sandwich() | Generates the robust sandwich matrix | |
kenward_roger() | Small sample bias-corrected variance matrix of $\hat{\beta}$ | |
partial_sigma() | Generates matrices $\partial \Sigma / \partial \theta$ and $\partial^2 \Sigma / \partial \theta_i \partial \theta_j$ | |
use_attenuation() | Option for improving the approximation of $\Sigma$ | |
power() | Estimates the power | |
MCML() | MCMC Maximum likelihood model fitting | |
LA() | Laplace approximation model fitting | |
mcmc_sample() | Sample $u$ using MCMC | |
w_matrix() | Returns $diag(W)$ | |
dh_deta() | Returns $\partial h^{-1}(\eta) / \partial \eta$ | |
log_gradient() | Returns the gradient of the log likelihood with respect to either the random effects or $\beta$ | |
marginal() | Calculates marginal effects with different types of conditioning or averaging |