project | R Documentation |

Project the posterior of the reference model onto the parameter space of a single submodel consisting of a specific combination of predictor terms or (after variable selection) onto the parameter space of a single or multiple submodels of specific sizes.

project( object, nterms = NULL, solution_terms = NULL, refit_prj = TRUE, ndraws = 400, nclusters = NULL, seed = sample.int(.Machine$integer.max, 1), regul = 1e-04, ... )

`object` |
An object which can be used as input to |

`nterms` |
Only relevant if |

`solution_terms` |
If not |

`refit_prj` |
A single logical value indicating whether to fit the
submodels (again) ( |

`ndraws` |
Only relevant if |

`nclusters` |
Only relevant if |

`seed` |
Pseudorandom number generation (PRNG) seed by which the same
results can be obtained again if needed. If |

`regul` |
A number giving the amount of ridge regularization when projecting onto (i.e., fitting) submodels which are GLMs. Usually there is no need for regularization, but sometimes we need to add some regularization to avoid numerical problems. |

`...` |
Arguments passed to |

Arguments `ndraws`

and `nclusters`

are automatically truncated at
the number of posterior draws in the reference model (which is `1`

for
`datafit`

s). Using less draws or clusters in `ndraws`

or `nclusters`

than
posterior draws in the reference model may result in slightly inaccurate
projection performance. Increasing these arguments affects the computation
time linearly.

If the projection is performed onto a single submodel (i.e.,
`length(nterms) == 1 || !is.null(solution_terms)`

), an object of class
`projection`

which is a `list`

containing the following elements:

`dis`

Projected draws for the dispersion parameter.

`kl`

The KL divergence from the submodel to the reference model.

`weights`

Weights for the projected draws.

`solution_terms`

A character vector of the submodel's predictor terms, ordered in the way in which the terms were added to the submodel.

`submodl`

A

`list`

containing the submodel fits (one fit per projected draw).`p_type`

A single logical value indicating whether the reference model's posterior draws have been clustered for the projection (

`TRUE`

) or not (`FALSE`

).`refmodel`

The reference model object.

If the projection is performed onto more than one submodel, the output from
above is returned for each submodel, giving a `list`

with one element for
each submodel.

if (requireNamespace("rstanarm", quietly = TRUE)) { # Data: dat_gauss <- data.frame(y = df_gaussian$y, df_gaussian$x) # The "stanreg" fit which will be used as the reference model (with small # values for `chains` and `iter`, but only for technical reasons in this # example; this is not recommended in general): fit <- rstanarm::stan_glm( y ~ X1 + X2 + X3 + X4 + X5, family = gaussian(), data = dat_gauss, QR = TRUE, chains = 2, iter = 500, refresh = 0, seed = 9876 ) # Variable selection (here without cross-validation and with small values # for `nterms_max`, `nclusters`, and `nclusters_pred`, but only for the # sake of speed in this example; this is not recommended in general): vs <- varsel(fit, nterms_max = 3, nclusters = 5, nclusters_pred = 10, seed = 5555) # Projection onto the best submodel with 2 predictor terms (with a small # value for `nclusters`, but only for the sake of speed in this example; # this is not recommended in general): prj_from_vs <- project(vs, nterms = 2, nclusters = 10, seed = 9182) # Projection onto an arbitrary combination of predictor terms (with a small # value for `nclusters`, but only for the sake of speed in this example; # this is not recommended in general): prj <- project(fit, solution_terms = c("X1", "X3", "X5"), nclusters = 10, seed = 9182) }

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