vglmer (version 1.0.5)

sl_vglmer: SuperLearner with (Variational) Hierarchical Models

Description

These functions integrate vglmer (or glmer) into SuperLearner. Most of the arguments are standard for SuperLearner functions.

Usage

SL.vglmer(
  Y,
  X,
  newX,
  formula,
  family,
  id,
  obsWeights,
  control = vglmer_control()
)
# S3 method for SL.vglmer
predict(object, newdata, allow_missing_levels = TRUE, ...)
SL.glmer(Y, X, newX, formula, family, id, obsWeights, control = NULL)
# S3 method for SL.glmer
predict(object, newdata, allow.new.levels = TRUE, ...)
add_formula_SL(learner, env = parent.frame())

Value

The functions here return different types of output. SL.vglmer

and SL.glmer return fitted models with the in-sample predictions as standard for SuperLearner. The predict methods return vectors of predicted values. add_formula_SL creates two objects in the environment (one for estimation model_f and one for prediction

predict.model_f) used for SuperLearner.

Arguments

Y

From SuperLearner: The outcome in the training data set.

X

From SuperLearner: The predictor variables in the training data.

newX

From SuperLearner: The predictor variables in validation data.

formula

The formula used for estimation.

family

From SuperLearner: Currently allows gaussian or binomial.

id

From SuperLearner: Optional cluster identification variable. See SuperLearner for more details.

obsWeights

From SuperLearner: Weights for each observation. Not permitted for SL.vglmer.

control

Control object for estimating vglmer (e.g., vglmer_control) or [g]lmer.

object

Used in predict for SL.glmer and SL.vglmer. A model estimated using either SL.vglmer or SL.glmer.

newdata

Dataset to use for predictions.

allow_missing_levels

Default (TRUE) allows prediction for levels not observed in the estimation data; the value of 0 (with no uncertainty) is used for the corresponding random effect. Note: This default differs from predict.vglmer.

...

Not used; included to maintain compatibility with existing methods.

allow.new.levels

From lme4: Allow levels in prediction that are not in the training data. Default is TRUE for SuperLearner.

learner

Character name of model from SuperLearner. See "Details" for how this is used.

env

Environment to assign model. See "Details" for how this is used.

Details

This documentation describes two types of function.

Estimating Hierarchical Models in SuperLearner: Two methods for estimating hierarchical models are provided one for variational methods (SL.vglmer) and one for non-variational methods (SL.glmer). The accompanying prediction functions are also provided.

Formula with SuperLearner: The vglmer package provides a way to estimate models that require or use a formula with SuperLearner. This allows for a design to be passed that contains variables that are not used in estimation. This can be used as follows (see "Examples"). One calls the function add_formula_SL around the quoted name of a SuperLearner model, e.g. add_formula_SL(learner = "SL.knn"). This creates a new model and predict function with the suffix "_f". This requires a formula to be provided for estimation.

With this in hand, "SL.knn_f" can be passed to SuperLearner with the accompanying formula argument and thus one can compare models with different formula or design on the same ensemble. The env argument may need to be manually specified to ensure the created functions can be called by SuperLearner.

Examples

set.seed(456)

if (requireNamespace('SuperLearner', quietly = TRUE)){
require(SuperLearner)
sim_data <- data.frame(
  x = rnorm(100),
  g = sample(letters, 100, replace = TRUE)
)
sim_data$y <- rbinom(nrow(sim_data), 
  1, plogis(runif(26)[match(sim_data$g, letters)]))
sim_data$g <- factor(sim_data$g)
sl_vglmer <- function(...){SL.vglmer(..., formula = y ~ x + (1 | g))}
SL.glm <- SuperLearner::SL.glm
add_formula_SL('SL.glm')
sl_glm_form <- function(...){SL.glm_f(..., formula = ~ x)}
# \donttest{
   SuperLearner::SuperLearner(
     Y = sim_data$y, family = 'binomial',
     X = sim_data[, c('x', 'g')],
     cvControl = list(V = 2),
     SL.library = c('sl_vglmer', 'sl_glm_form')
   )
# }
}