nb_updated_grid_cat: Function: Grid search to estimate predicted values and...
In acweisha/gFPCAClassif: Classification of Binary Functional Data

Function: Grid search to estimate predicted values and estimate the values of h

nb_updated_grid_cat(
  scores,
  s_mat_hat_train,
  classes,
  cat_covariates_train,
  cat_covariates_test,
  prior_g,
  scores_test,
  s_mat_hat_test,
  alpha_js = NA,
  min.h = 0.4,
  max.h = 1.6,
  CV = 5,
  n_grid = 5,
  return_h = F,
  return_prob = F,
  P_max = 3,
  static_train = NA,
  static_test = NA
)

`scores`	N x K matrix of 1st level scores in the training set
`s_mat_hat_train`	Matrix of estimated S data for training
`classes`	Group labels vector of length N
`cat_covariates_train`	dataframe of categorical variables in the training set
`cat_covariates_test`	dataframe of categorical variables in the testing set
`prior_g`	vector of prior probability of being in each group, sums up to 1
`scores_test`	N_test x K matrix of scores in the testing set
`s_mat_hat_test`	Matrix of estimated S data for testing
`alpha_js`	Estimated probability of latent states across the realizations
`min.h`	min possible value for the multiplier
`max.h`	maximum possible value for the multiplier
`CV`	Number of folds for cross validation
`n_grid`	number of values between min.h and max.h to search over
`return_h`	T/F to return the value of the multiplier
`return_prob`	T/F to return group Bayes classifier probability for each individual in testing set
`P_max`	number of lags in the gAR model
`static_train`	Covariates for the classifier in the training set accounts
`static_test`	Covariates for the classifier in the testing set accounts