ngme: Fit an additive linear mixed effect model over replicates

In ngme2: Linear Latent Non-Gaussian Models with Flexible Distributions

Fit an additive linear mixed effect model over replicates

Description

ngme function performs an analysis of non-gaussian additive models. It does the maximum likelihood estimation via stochastic gradient descent. The prediction of unknown location can be performed by leaving the response variable to be NA. The likelihood is specified by family. The model estimation control can be setted in control using control_opt() function, see ?control_opt for details. See ngme_model_types() for available models.

Usage

ngme(
  formula,
  data,
  family = "normal",
  control_opt = NULL,
  control_ngme = NULL,
  group = NULL,
  replicate = NULL,
  start = NULL,
  moving_window = 1,
  prior_beta = NULL,
  debug = FALSE
)

Arguments

formula	formula
data	a dataframe or a list providing data (Only response variable can contain NA value, NA value in other columns will cause problem)
family	likelihood type, same as measurement noise specification. It can be provided as: a string, e.g., "normal", "nig", "t". an ngme noise object, e.g., noise_normal(), noise_nig(mu = 0, sigma = 1, nu = 1), noise_t(nu=5).
control_opt	control for optimizer. by default it is control_opt(). See ?control_opt for details.
control_ngme	control for ngme model. by default it is control_ngme(). See ?control_ngme for details.
group	factor, used for bivariate model, indicating which group the observation belongs to
replicate	factor, used for divide data into different replicates
start	starting ngme object (usually object from last fit)
moving_window	number of iterations to average the estimation
prior_beta	prior specification for fixed effects ('beta'), created by prior_*() or priors(...).
debug	toggle debug mode

Value

random effects (for different replicate) + models(fixed effects, measuremnt noise, and latent process)

Examples

ngme(
  formula = Y ~ x1 + f(
    x2,
    model = ar1(rho = 0.5),
    noise = noise_nig()
  ) + f(x1,
    model = rw1(),
    noise = noise_normal(),
  ),
  family = noise_normal(sd = 0.5),
  data = data.frame(Y = 1:5, x1 = 2:6, x2 = 3:7),
  control_opt = control_opt(
    estimation = FALSE
  )
)

ngme2 documentation built on May 20, 2026, 9:10 a.m.