projpred: project – R documentation

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projpred

project

Projection to submodels

Description

Perform projection onto submodels of selected sizes or a specified feature combination.

Usage

project(
  object,
  nterms = NULL,
  solution_terms = NULL,
  cv_search = TRUE,
  ndraws = 400,
  nclusters = NULL,
  intercept = NULL,
  seed = NULL,
  regul = 1e-04,
  ...
)

Arguments

`object`	Either a `refmodel`-type object created by get_refmodel or init_refmodel, or an object which can be converted to a reference model using get_refmodel.
`nterms`	Number of terms in the submodel (the variable combination is taken from the `varsel` information). If a list, then the projection is performed for each model size. Default is the model size suggested by the variable selection (see function `suggest_size`). Ignored if `solution_terms` is specified.
`solution_terms`	Variable indices onto which the projection is done. If specified, `nterms` is ignored.
`cv_search`	If TRUE, then the projected coefficients after L1-selection are computed without any penalization (or using only the regularization determined by `regul`). If FALSE, then the coefficients are the solution from the L1-penalized projection. This option is relevant only if L1-search was used. Default is TRUE for genuine reference models and FALSE if `object` is datafit (see init_refmodel).
`ndraws`	Number of posterior draws to be projected. Ignored if `nclusters` is specified. Default is 400.
`nclusters`	Number of clusters in the clustered projection.
`intercept`	Whether to use intercept. Default is `TRUE`.
`seed`	A seed used in the clustering (if `nclusters!=ndraws`). Can be used to ensure same results every time. @param regul Amount of regularization in the projection. Usually there is no need for regularization, but sometimes for some models the projection can be ill-behaved and we need to add some regularization to avoid numerical problems.
`regul`	Ridgre regularization constant to fit the projections.
`...`	Currently ignored.

Value

A list of submodels (or a single submodel if projection was performed onto a single variable combination), each of which contains the following elements:

kl: The KL divergence from the reference model to the submodel.
weights: Weights for each draw of the projected model.
dis: Draws from the projected dispersion parameter.
alpha: Draws from the projected intercept.
beta: Draws from the projected weight vector.
solution_terms: The order in which the variables were added to the submodel.
intercept: Whether or not the model contains an intercept.
family: A modified family-object.

Examples

if (requireNamespace("rstanarm", quietly = TRUE)) {
  ### Usage with stanreg objects
  n <- 30
  d <- 5
  x <- matrix(rnorm(n * d), nrow = n)
  y <- x[, 1] + 0.5 * rnorm(n)
  data <- data.frame(x, y)

  fit <- rstanarm::stan_glm(y ~ X1 + X2 + X3 + X4 + X5, gaussian(),
    data = data, chains = 2, iter = 500)
  vs <- varsel(fit)

  # project onto the best model with 4 variables
  proj4 <- project(vs, nterms = 4)

  # project onto an arbitrary variable combination (variable indices 1, 3 and 5)
  proj <- project(fit, solution_terms = c(1, 3, 5))
}

projpred

Projection Predictive Feature Selection

v2.0.2

GPL-3

Authors

Juho Piironen [aut], Markus Paasiniemi [aut], Alejandro Catalina [cre, aut], Aki Vehtari [aut], Jonah Gabry [ctb], Marco Colombo [ctb], Paul-Christian Bürkner [ctb]

Initial release

project

Description

Usage

Arguments

Value

Examples

projpred

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