plmmr (version 4.3.0)

predict.cv_plmm: Predict method for cv_plmm class

Description

Predict method for cv_plmm class

Usage

# S3 method for cv_plmm
predict(
  object,
  newX,
  type = c("blup", "coefficients", "vars", "nvars", "lp"),
  X,
  lambda,
  idx = object$min,
  ...
)

Value

Depends on the type - see Details

Arguments

object

An object of class cv_plmm.

newX

Matrix of values at which predictions are to be made (not used for type = "coefficients", "vars", or "nvars"). This can be either a filebacked big.matrix or a matrix object. Note: Columns of this argument must be named!

type

A character argument indicating what type of prediction should be returned. Options are "lp," "coefficients," "vars," "nvars," and "blup." See details.

X

Optional: if type = 'blup' and the model was fit in-memory, the design matrix used to fit the model represented in object must be supplied. When supplied, this design matrix will be standardized using the center/scale values in object$std_X_details, so please do not standardize this matrix before supplying here. Note: If the model was fit file-backed, then the filepath to the .bk file with this standardized design matrix is returned as std_X in the fit supplied to object.

lambda

A numeric vector of regularization parameter lambda values at which predictions are requested.

idx

Vector of indices of regularization parameter lambda at which predictions are requested. By default, this is the lambda index which minimizes the cross-validation error.

...

Additional optional arguments

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 (linear predictor): uses the product of newX and the beta coefficients of object to predict new values of the outcome. This 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.

• coefficients: returns the estimated beta-hat

• vars: returns the indices of variables (e.g., SNPs) with nonzero coefficients at each value of lambda. EXCLUDES intercept.

• nvars: returns the number of variables (e.g., SNPs) with nonzero coefficients at each value of lambda. EXCLUDES intercept.

Examples

set.seed(123)
train_idx <- sample(1:nrow(admix$X), 100)
# Note: ^ shuffling is important here! Keeps test and train groups comparable.
train <- list(X = admix$X[train_idx,], y = admix$y[train_idx])
train_design <- create_design(X = train$X, y = train$y)

test <- list(X = admix$X[-train_idx,], y = admix$y[-train_idx])
fit <- cv_plmm(design = train_design)

pred1 <- predict(object = fit, newX = test$X, X = train$X) # Minimum CVE lambda
pred2 <- predict(object = fit, newX = test$X, X = train$X, idx = fit$min1se) # 1 SE lambda