R/auto.homo.test.R

In SIMle: Estimation and Inference for General Time Series Regression

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#' Automated time-homogeneity test 
#' @description This function utilizes Simultaneous Confidence Regions (SCR) for the automated execution of time-homogeneity tests with chosen bases.
#' @param ts ts is the data set which is a time series data typically
#' @param c the maximum value of number of basis for time input
#' @param d the maximum value of number of basis for variate input
#' @param b_time type of basis for time input 
#' @param b_timese type of basis for variate input
#' @param mp_type select type of mapping function, "algeb" indicates algebraic mapping on the real line. "logari" represents logarithmic mapping on the real line
#' @param ops Criteria for choosing the number of bases are provided by the package, offering four options: "AIC," "BIC," "CV," and "Kfold," each corresponding to a specific Criteria 
#' @param m the window size for the simultaneous confidence region procedure, with the default being 'MV,' which stands for the Minimum Volatility method 
#' @param fix_num fix_num indicates fixed value for time 
#' @param r indicates number of variate 
#' @param s s is a positive scaling factor, the default is 1
#' @param per the percentage for test set used in "CV" option 
#' @param k the number of fold used in "Kfold" option
#' @param upper upper The upper bound for the variate basis domain. The default value is 10. When "algeb" or "logari" is chosen, the domain is automatically set from -upper to upper 
#'        
#'
#' @return A list is returned, containing dataframes with three columns each. The first column pertains to input values, the second column contains values of the estimated function 
#'         along with their upper and lower bounds, which are used for time-homogeneity testing. The third column serves as a factor indicating the types corresponding to the values in 
#'         the second column.
#' @export





auto.homo.test <- function(ts, c, d, b_time, b_timese, mp_type, ops, m = "MV", fix_num = 0, r = 1, s = 1, per = 0, k = 0, upper = 10){
  
  best_cd = best_cd.auto.fit(ts, c, d, b_time, b_timese, mp_type, ops, r = r, s = s, per = per, k = k)
  res = homo.test(ts, as.numeric(best_cd[1]), as.numeric(best_cd[2]), m, b_time, b_timese, mp_type,  fix_num = fix_num, r = r, s = s, upper = upper)
  
  return(res)

}