INLAutils (version 0.0.5)

INLAstep: Perform stepwise variable selection with INLA

Description

Run forwards or backwards stepwise variable selection with INLA regression. Only fixed effects will be considered for removal. Use holdout data or within sample data to test new models. Choose the level of interactions and power terms to expand the model formula to. Force part of the model to remain in the model (e.g. the spatial term and intercept).

Usage

INLAstep(fam1 = "gaussian", dataf, spde = NULL, in_stack = NULL,
  invariant = "0 + Intercept", direction = c("forwards", "backwards"),
  y = NULL, y2 = NULL, include = NULL, powerl = 1, inter = 1,
  thresh = 2, num.threads = 1, ...)

Arguments

fam1

String defining the likelihood familiy.

dataf

A dataframe including covariates and response data.

spde

An spde model object for the spatial component.

in_stack

An inla.data.stack object containing all needed data.

invariant

The part of the formula that should not change (e.g. the intercept and the spatial component.)

direction

string 'forwards' for forward variable selection and 'backwards' for backwards variable elimination.

y

String determining the response variable.

y2

String determining the name of the test response data.

include

Vector of integers to determine which columns in dataf should be used. If NULL, use all columns except y and y2.

powerl

Integer up to 3 determining which power terms to include.

inter

Integer up to 3 determining how many levels of intereactions to include.

thresh

Threshold difference in DIC for whether a new model should replace the old model.

num.threads

How many threads to use for INLA computation.

...

Further arguments to INLA::inla function.

Examples

# NOT RUN {
  library(INLA)
  data(Epil)
  stack <- INLA::inla.stack(data = list(y = Epil$y),
                           A = list(1),
                           effects = list(data.frame(Intercept = 1, Epil[3:5])))
                      
 
  result <- INLAstep(fam1 = "poisson", 
                     Epil,
                     in_stack = stack,
                     invariant = "0 + Intercept",
                     direction = 'backwards',
                     include = 3:5,
                     y = 'y',
                     y2 = 'y',
                     powerl = 1,
                     inter = 1,
                     thresh = 2)

  autoplot(result$best_model, which = c(1, 5), CI = TRUE)
# }