predict_within_cv: Predict method to use in cross-validation (within 'cvf')

In plmmr: Penalized Linear Mixed Models for Correlated Data

Predict method to use in cross-validation (within cvf)

Description

Predict method to use in cross-validation (within cvf)

Usage

predict_within_cv(
  fit,
  testX,
  type,
  fbm = FALSE,
  Sigma_11 = NULL,
  Sigma_21 = NULL
)

Arguments

fit	A list with the components returned by plmm_fit.
testX	A design matrix used for computing predicted values (i.e, the test data).
type	A character argument indicating what type of prediction should be returned. Passed from cvf(), Options are "lp," "coefficients," "vars," "nvars," and "blup." See details.
fbm	Logical: is trainX an FBM object? If so, this function expects that testX is also an FBM. The two X matrices must be stored the same way.
Sigma_11	Variance-covariance matrix of the training data. Extracted from estimated_Sigma that is generated using all observations. Required if type == 'blup'.
Sigma_21	Covariance matrix between the training and the testing data. Extracted from estimated_Sigma that is generated using all observations. Required if type == 'blup'.

Details

Define beta-hat as the coefficients estimated at the value of lambda that minimizes cross-validation error (CVE). Then options for type are as follows:

• 'lp' (default): uses the linear predictor (i.e., product of test data and estimated coefficients) to predict test values of the outcome. Note that this approach does not incorporate the correlation structure of the data.

• 'blup' (acronym for Best Linear Unbiased Predictor): adds to the 'lp' a value that represents the estimated random effect. This addition is a way of incorporating the estimated correlation structure of data into our prediction of the outcome.

Note: the main difference between this function and the predict.plmm() method is that here in CV, the standardized testing data (std_test_X), Sigma_11, and Sigma_21 are calculated in cvf() instead of the function defined here.

Value

A numeric vector of predicted values

plmmr documentation built on April 4, 2025, 12:19 a.m.