kzs (version 1.2.0)

argkzs: Argument Limits for KZS

Description

This function calculates the value for which the arguments delta and h in the KZS function are bounded above or below by.

Usage

argkzs(data, x)

Arguments

data

a data frame of paired values X and Y representing pairs (Xi, Yi ), i = 1,... ,n and X, Y are real values. This should be the data frame that is to be used with KZS.

x

an integer specifying the position of the column in the data frame containing the one dimensional input variable, X, coordinates.

Value

• a list containing two elements:
• delta
  the bounding value for the argument delta
• h
  the bounding value for the argument h

Details

In the KZS function, the argument delta is the physical range of smoothing in terms of unit values of X; the argument h is a scale reading of all outcomes of the algorithm. More specifically, h is the interval width of a uniform scale overlaying the X axis. The purpose of this function is to give an upper and/or lower bound on the values of delta and h so that users may select appropriate values that satisfy all restrictions. This function eliminates any guess-work involved in choosing a satisfying value for delta and h and should be used prior to KZS in order to save time and increase efficiency of use.

See Also

kzs

Examples

#This example uses the same data from the KZS example

  # Define the time sequence
  t <- seq(from = -round(400*pi), to = round(400*pi), by = .25) 

  # Positive t (includes time = 0)
  tp <- seq(from = 0, to = round(400*pi), by = .25)	

  # Negative t	      
  tn <- seq(from = -round(400*pi), to = -.25, by = .25)                                                       

  # Positive side of signal
  signalp <- 0.5*sin(sqrt((2*pi*abs(tp))/200))

  # Negative side of signal             
  signaln <- 0.5*sin(-sqrt((2*pi*abs(tn))/200))

  # Appending into one signal           
  signal <- append(signaln, signalp, after = length(tn))    

  # Randomly generate noise from the standard normal distribution
  et <- rnorm(length(t), mean = 0, sd = 1)

  # Add the noise to the signal
  yt <- et + signal

  # Data frame of (t,yt) 
  pts <- data.frame(cbind(t,yt))

  argkzs(pts, 1)