<<<<<<< HEAD Apparent estimates for brier score (MSE) and misclassification rate (OMR). — SemiSupervisedApparentAccuracy • stratifiedSSLApparent estimates for brier score (MSE) and misclassification rate (OMR). — SemiSupervisedApparentAccuracy • stratifiedSSL

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

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

<<<<<<< HEAD 
SemiSupervisedApparentAccuracy(
  basis_labeled,
  basis_unlabeled,
  X_labeled,
  X_unlabeled,
  y,
  beta_SSL,
  beta_imp,
  samp_prob,
  ind_est = NULL,
  resamp_weight = NULL,
  threshold = 0.5
)
=======

Usage

SemiSupervisedApparentAccuracy(
  basis_labeled,
  basis_unlabeled,
  X_labeled,
  X_unlabeled,
  y,
  beta_SSL,
  beta_imp,
  samp_prob,
  ind_est = NULL,
  resamp_weight = NULL,
  threshold = 0.5
)
>>>>>>> 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.

beta_SSL

Numeric vector of regression coefficients.

beta_imp

Numeric vector of regression coefficients for imputation.

samp_prob

Numeric vector of weights.

ind_est

Optional numeric vector to indicate inds for refitting model.

resamp_weight

Numeric vector of resampling weights.

threshold

Threshold for overall misclassification rate.

<<<<<<< HEAD

Value

Semi-supervised MSE and OMR.

=======

Value

Semi-supervised MSE and OMR.

>>>>>>> e32a060aec520f495c0371cd7ee97e715754fa71