updateVarYC: Variance calculation (updatable) by Youngs and Cramer

In aschersleben/NBCD: Naive Bayes for Online Classification with Concept Drift

Description

Variance calculation (updatable) by Youngs and Cramer

Usage

updateVarYC(x, object = NULL)

Arguments

x	[numeric] vector (finite, no missing values)
object	[ycVar | NULL] list (object from updateVarYC) or NULL

Value

[ycVar]

• $var: (updated) variance via Youngs-Cramer-algorithm

• $n: number of included elements

Alternatives

• Cotton, E.W. (1975), Remark on "Stably Updating Mean and Standard Deviation of Data," Communications of the Association for Computing Machinery, 18, 458.

• Hanson, R.J. (1975), "Stably Updating Mean and Standard Deviation of Data," Communications of the Association for Computing Machinery, 18, 57-58.

• Welford, B.P. (1962), "Note on a Method for Calculating Corrected Sums of Squares and Products," Technometrics, 4, 419-420.

• West, D.H.D. (1979), "Updating Mean and Variance Estimates: An Improved Method," Communications of the Association for Computing Machinery, 22, 532-535.

References

Youngs, Edward A., and Elliot M. Cramer. "Some results relevant to choice of sum and sum-of-product algorithms." Technometrics 13.3 (1971): 657–665, DOI:10.2307/1267176.

Examples

## Not run: 
yc.fun = function(x) {
  yc = NULL
  for (i in seq_along(x)) {
    yc = NBCD:::updateVarYC(x[i], yc)
  }
  return(yc$var)
}
yc.fun(1:10)

## End(Not run)

aschersleben/NBCD documentation built on May 12, 2019, 4:32 a.m.