nb_updated_grid: Function: Grid search to estimate predicted values and...

In acweisha/gFPCAClassif: Classification of Binary Functional Data

Description

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

Usage

nb_updated_grid(
  scores,
  s_mat_hat_train,
  classes,
  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
)

Arguments

scores	N x K matrix of 1st level scores in the training set
s_mat_hat_train	Latent state matrix of estimated values for the training set accounts
classes	Group labels vector of length N
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	Latent state matrix of estimated values for the testing set accounts
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	Lag of the gAR models
static_train	Covariates for the classifier in the training set accounts
static_test	Covariates for the classifier in the testing set accounts

Value

Predicted group for each individual

acweisha/gFPCAClassif documentation built on Dec. 18, 2021, 10:23 p.m.