tests/testthat/test-mapfit.R

In mapfit: PH/MAP Parameter Estimation

test_that("mapfit.point", {
  RNGkind(kind = "Mersenne-Twister")
  set.seed(1234)

  ## load trace data
  data(BCpAug89)
  BCpAug89s <- head(BCpAug89, 50)

  ## MAP fitting for general MAP
  (result1 <- mapfit.point(map=map(2), x=cumsum(BCpAug89s)))

  ## MAP fitting for MMPP
  (result2 <- mapfit.point(map=mmpp(2), x=cumsum(BCpAug89s)))

  ## MAP fitting for ER-HMM
  (result3 <- mapfit.point(map=erhmm(3), x=cumsum(BCpAug89s)))

  ## marginal moments for estimated MAP
  map.mmoment(k=3, map=result1$model)
  map.mmoment(k=3, map=result2$model)
  map.mmoment(k=3, map=result3$model)

  ## joint moments for estimated MAP
  map.jmoment(lag=1, map=result1$model)
  map.jmoment(lag=1, map=result2$model)
  map.jmoment(lag=1, map=result3$model)

  ## lag-k correlation
  map.acf(map=result1$model)
  map.acf(map=result2$model)
  map.acf(map=result3$model)
})

test_that("phfit.group", {
  RNGkind(kind = "Mersenne-Twister")
  set.seed(1234)

  ## load trace data
  data(BCpAug89)
  BCpAug89s <- head(BCpAug89, 50)

  ## make grouped data
  BCpAug89.group <- hist(cumsum(BCpAug89s),
                           breaks=seq(0, 0.15, 0.005),
                           plot=FALSE)

  ## MAP fitting for general MAP
  (result1 <- mapfit.group(map=map(2),
                          counts=BCpAug89.group$counts,
                          breaks=BCpAug89.group$breaks))
  ## MAP fitting for MMPP
  (result2 <- mapfit.group(map=mmpp(2),
                           counts=BCpAug89.group$counts,
                           breaks=BCpAug89.group$breaks))

  ## MAP fitting with approximate MMPP
  (result3 <- mapfit.group(map=gmmpp(2),
                           counts=BCpAug89.group$counts,
                           breaks=BCpAug89.group$breaks))

  ## marginal moments for estimated MAP
  map.mmoment(k=3, map=result1$model)
  map.mmoment(k=3, map=result2$model)
  map.mmoment(k=3, map=result3$model)

  ## joint moments for estimated MAP
  map.jmoment(lag=1, map=result1$model)
  map.jmoment(lag=1, map=result2$model)
  map.jmoment(lag=1, map=result3$model)

  ## lag-k correlation
  map.acf(map=result1$model)
  map.acf(map=result2$model)
  map.acf(map=result3$model)
})