Predict from a fitted PLS-SEM model

pls_predict(object, ...)

# S4 method for class 'PlsModel'
pls_predict(
  object,
  approach = c("earliest", "direct"),
  newdata = NULL,
  std.ord.exp = FALSE,
  benchmark = "R2",
  benchmark.vars = c("endog", "exog", "all"),
  ...
)

Arguments

object: A fitted PlsModel object.
...: Additional arguments passed to internal helpers.
approach: Prediction approach. If approach = "earliest" (default), then only indicators of exogenous benchmark.vars are used for prediction. If approach = "direct", then all indicators are used.
newdata: Optional new data matrix/data frame.
std.ord.exp: Logical; standardize ordinal expectation scores.
benchmark: Benchmark type(s). Either length 1 (recycled) or one entry per indicator (optionally named). Supported: "r2", "rmse", "mae", "q2_predict", "acc", "ord_mae".
benchmark.vars: What predictions should be benchmarked? If benchmark.vars = "endog" (default), preidction benchmarks are applied to indicators of endogenous benchmark.vars. If benchmark.vars = "exog", preidction benchmarks are applied to indicators of exogenous benchmark.vars. If benchmark.vars = "all", preidction benchmarks are applied to all of the indicators in the model.

Value

A PlsSemPredict object with matrices and benchmark results.