mewMean {mewAvg}

Update the moving expanding window average

Description

When desired, the x_mean slot in an S4 object of class mewTyp may be updated to contain the correct moving expanding window (MEW) average (it is not updated by the function mewAccum to save computation). If the slot know_mean is unity, the slot x_mean is up-to-date; otherwise; it is not.

Usage

mewMean(av)

Arguments

av

(class mewTyp) the current state of the MEW average

Value

the updated instance of the argument av

Examples

n_iter <- 100

i_to_print <- 10

results <- matrix(data = double(2*n_iter/i_to_print),
                  nrow = n_iter/i_to_print,
                  ncol = 2)

av <- mewInit(n_bin = 4, n_xx = 2, ff = 0.5)

for (i in 1:n_iter) {

  value <- runif(n=2)
  value[1] <- ((cos(value[1]*2*pi))^2)*(1 - exp(-0.01*i))
  value[2] <- (-((sin(value[2]*2*pi))^2))*(1 - exp(-0.01*i))

  av <- mewAccum(xx = value, av = av)

  if (i%%i_to_print == 0) {

    av <- mewMean(av)
    show(av)
    results[i/i_to_print, ] <- mewGetMean(av)
  }
}

## plot the results

plot(c(1, (n_iter/i_to_print)),
     c(min(results), max(results)),
     type = "n")
points(1:(n_iter/i_to_print), results[, 1])
points(1:(n_iter/i_to_print), results[, 2])

## Now, a larger example, and we pause part way through to assess
## convergence

n_iter <- 1000
av <- mewInit(n_bin = 4, n_xx = 5000, ff = 0.5)
for (i in 1:n_iter) {

  new_samp <- runif(n = 5000)
  av <- mewAccum(xx = new_samp, av = av)
}

av <- mewMean(av = av)

## of course each element of the mean sould converge to 0.5.  After
## 1000 iterations, the first six elements of the mean vector are
show(av)

## run another 1000 iterations
for (i in 1:1000) {

  new_samp <- runif(n = 5000)
  av <- mewAccum(xx = new_samp, av = av)
}

av <- mewMean(av)

## check the mean of the first six elements again
show(av)