R/DataCheck.R

In RPEIF: Computation and Plots of Influence Functions for Risk and Performance Measures

# ================================
# Data Checking
# IF FUnctions Input Parameters
# ================================

# Checking general input parameters for IF functions
DataCheck <- function(returns, evalShape, retVals, nuisPars, k,
                      IFplot, IFprint,
                      prewhiten, ar.prewhiten.order,
                      cleanOutliers, cleanMethod, eff){
  
  # Checking data if IF TS evaluation
  if(!isTRUE(evalShape))
    if(is.null(returns))
      stop("Returns must be provided for the IF TS evaluation.")
  
  # Checking the data for the returns
  if(!is.null(returns)){
    if(!any(c(inherits(returns, "matrix"), inherits(returns, "numeric"), inherits(returns, "xts"), inherits(returns, "zoo")))){
      stop("returns should belong to one of the following classes: matrix, numeric, xts, zoo")
    } else if(any(anyNA(returns), any(is.nan(returns)), any(is.infinite(returns)))){
      stop("returns should not have missing, infinite or nan values")
    } else{
      if(inherits(returns, "matrix")){
        if(ncol(returns)>1){
          stop("returns should be a vector")
        }
        # Force to vector if input was a matrix
        returns <- as.numeric(returns)
      }
    }
  }
  
  # Checking the data for k (range parameter)
  if(!inherits(k, "numeric")){
    stop("k should be numeric")
  } else if(any(!k == floor(k), k <= 0)){
    stop("k should be a positive integer")
  }
  
  # Checking data for prewhitening order
  if (!inherits(ar.prewhiten.order, "numeric")) {
    stop("ar.prewhiten.order should be numeric")
  } else if (any(!ar.prewhiten.order == floor(ar.prewhiten.order), ar.prewhiten.order <= 0)) {
    stop("ar.prewhiten.order should be a positive integer")
  }
  
  # Checking robust cleaning method specified
  if(!(cleanMethod %in% c("locScaleRob")))
    stop("The specified outlier cleaning method is not available.")
  
  # Checking data for the efficiency for robust cleaning with "locScaleRob"
  if(!inherits(eff, "numeric")){
    stop("eff should be numeric")
  } else if(any(eff < 0, eff > 1)) {
    stop("eff should be a numeric value between 0 and 1.")
  }
  
  # Check data for the nuisance parameters
  if(!is.null(nuisPars))
    if(!is.list(nuisPars))
      stop("nuisPars must be a list.")
}

# Checking general input parameters for IF functions (Robust)
DataCheckRob <- function(returns, family, eff,
                         evalShape, retVals, nuisPars, k,
                         IFplot, IFprint,
                         prewhiten, ar.prewhiten.order){
  
  # Checking family for M-estimator of location
  if(!(family %in% c("mopt", "opt", "bisquare")))
    stop("family must be one of: \"mopt\", \"opt\" or \"bisquare\".")
  
  # Checking data for the efficiency for robust cleaning with "locScaleRob"
  if(!inherits(eff, "numeric")){
    stop("eff should be numeric")
  } else if(any(eff < 0, eff > 1)) {
    stop("eff should be a numeric value between 0 and 1.")
  }
  
  # Checking data if IF TS evaluation
  if(!isTRUE(evalShape))
    if(is.null(returns))
      stop("Returns must be provided for the IF TS evaluation.")
  
  # Checking the data for the returns
  if(!is.null(returns)){
    if(!any(c(inherits(returns, "matrix"), inherits(returns, "numeric"), inherits(returns, "xts"), inherits(returns, "zoo")))){
      stop("returns should belong to one of the following classes: matrix, numeric, xts, zoo")
    } else if(any(anyNA(returns), any(is.nan(returns)), any(is.infinite(returns)))){
      stop("returns should not have missing, infinite or nan values")
    } else{
      if(inherits(returns, "matrix")){
        if(ncol(returns)>1){
          stop("returns should be a vector")
        }
        # Force to vector if input was a matrix
        returns <- as.numeric(returns)
      }
    }
  }
  
  # Checking the data for k (range parameter)
  if(!inherits(k, "numeric")){
    stop("k should be numeric")
  } else if(any(!k == floor(k), k <= 0)){
    stop("k should be a positive integer")
  }
  
  # Checking data for prewhitening order
  if (!inherits(ar.prewhiten.order, "numeric")) {
    stop("ar.prewhiten.order should be numeric")
  } else if (any(!ar.prewhiten.order == floor(ar.prewhiten.order), ar.prewhiten.order <= 0)) {
    stop("ar.prewhiten.order should be a positive integer")
  }
  
  # Check data for the nuisance parameters
  if(!is.null(nuisPars))
    if(!is.list(nuisPars))
      stop("nuisPars must be a list.")
}