tests/testthat/test_estimate_pars.R

In csgillespie/poweRlaw: Analysis of Heavy Tailed Distributions

test_that("Testing estimate_pars function", {
  skip_on_cran()
  l = readRDS("estimate_pars.rds")

  ##Discrete Power-law
  discrete_data = readRDS("discrete_data.RData")
  mt = displ$new(discrete_data)
  mt$setXmin(2)
  est = estimate_pars(mt, pars = seq(2, 3, 0.01))
  expect_equal(est$pars, 2.58, tol = 1e-3)
  est = estimate_pars(mt)
  expect_equal(est$pars, 2.583, tol = 1e-3)

  ##Discrete Poisson
  x = rep(2, 10000)
  mt = dispois$new(x)
  mt$setXmin(0)
  est = estimate_pars(mt)
  expect_equal(est$pars, 2, tol = 1e-3)
  est = estimate_pars(mt, pars = seq(1.5, 2.5, 0.01))
  expect_equal(est$pars, 2, tol = 1e-3)

  ##Discrete Log normal
  x = l[["dislnorm"]]
  mt = dislnorm$new(x)
  mt$setXmin(0)
  est = estimate_pars(mt)
  expect_equal(est$pars, c(1.2202, 0.8577), tol = 1e-2)
  m_pars = expand.grid(seq(0.5, 1.5, 0.05), seq(0.5, 1.5, 0.05))
  est = estimate_pars(mt, m_pars)
  expect_equal(est$pars, c(1.20, 0.85), tol = 1e-3)

  ##CTN Power-law
  ctn_data = readRDS("ctn_data.RData")
  mt = conpl$new(ctn_data)
  mt$setXmin(1.43628)

  est = estimate_pars(mt)
  expect_equal(est$pars, 2.533, tol = 1e-3)

  est = estimate_pars(mt, pars = seq(2.3, 2.7, 0.0001))
  expect_equal(est$pars, 2.53282, tol = 1e-3)


  ##Log normal
  x = l[["conlnorm"]]
  mt = conlnorm$new(x)
  mt$setXmin(0)
  est = estimate_pars(mt)
  expect_equal(est$pars, c(0.988, 1.034), tol = 1e-2)
  m_pars = expand.grid(seq(0.85, 1.25, 0.01), seq(0.85, 1.25, 0.01))
  est = estimate_pars(mt, m_pars)
  expect_equal(est$pars, c(0.99, 1.03), tol = 1e-3)

  ##Exponential
  x = rep(2, 10000)
  mt = conexp$new(x)
  mt$setXmin(0)
  est = estimate_pars(mt)
  expect_equal(est$pars, 1 / 2, tol = 1e-5)
  est = estimate_pars(mt, pars = seq(0.1, 1, 0.01))
  expect_equal(est$pars, 1 / 2, tol = 1e-3)

}
)