CV estimates for brier score (MSE) and misclassification rate (OMR).

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CrossValAccuracy(
  basis_labeled,
  basis_unlabeled,
  X_labeled,
  X_unlabeled,
  y,
  samp_prob,
  min_var_weight,
  num_folds = 3,
  reps = 10,
  threshold = 0.5,
  lambda0 = NULL
)

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Usage

CrossValAccuracy(
  basis_labeled,
  basis_unlabeled,
  X_labeled,
  X_unlabeled,
  y,
  samp_prob,
  min_var_weight,
  num_folds = 3,
  reps = 10,
  threshold = 0.5,
  lambda0 = NULL
)

>>>>>>> e32a060aec520f495c0371cd7ee97e715754fa71

Arguments

basis_labeled: Basis matrix for labeled data set.
basis_unlabeled: Basis matrix for unlabeled data set.
X_labeled: Covariate matrix for labeled data set.
X_unlabeled: Covariate matrix for unlabeled data set.
y: Numeric outcome vector.
samp_prob: Numeric vector of weights.
min_var_weight: Numeric vector of minimum variance weights.
num_folds: Scalar indicating number of folds for CV.
reps: Scalar indicating number of repitions for CV.
threshold: Threshold for overall misclassification rate.
lambda0: Initial lambda for imputation model.

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Value

CV semi-supervised and supervised MSE and OMR.

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Value

CV semi-supervised and supervised MSE and OMR.

>>>>>>> e32a060aec520f495c0371cd7ee97e715754fa71