Predict method for plmm class — predict.plmm • plmmr

Predict method for plmm class

Usage

# S3 method for class 'plmm'
predict(
  object,
  newX,
  type = c("blup", "coefficients", "vars", "nvars", "lp"),
  X = NULL,
  lambda,
  idx = seq_along(object$lambda),
  ...
)

Arguments

object: An object of class 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, all indices are returned.
...: Additional optional arguments

Value

Depends on the type - see Details

Details

The 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 (default, 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 the data into our prediction of the outcome.
coefficients: returns the estimated beta coefficients.
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 <- plmm(design = train_design)

# make predictions for all lambda values
 pred1 <- predict(object = fit, newX = test$X, type = "lp")
 pred2 <- predict(object = fit, newX = test$X, type = "blup", X = train$X)

# look at mean squared prediction error
mspe <- apply(pred1, 2, function(c){crossprod(test$y - c)/length(c)})
min(mspe)
#> [1] 2.813242

mspe_blup <- apply(pred2, 2, function(c){crossprod(test$y - c)/length(c)})
min(mspe_blup) # BLUP is better
#> [1] 2.126016

# compare the MSPE of our model to a null model, for reference
# null model = intercept only -> y_hat is always mean(y)
crossprod(mean(test$y) - test$y)/length(test$y)
#>          [,1]
#> [1,] 6.381748