In jeksterslabds/boilerplatePackage: This is a Boilerplate Package

library(testthat)
library(boilerplatePackage)
context("Test z.")

Set test parameters

reps <- 5000
n <- 1000
mu <- 100
sigma <- 15

| Variable | Description | Value | |:---------- |:------------------------------------------------------- |-----------:| | reps | Number of simulation replications | r reps | | n | Sample size | r n | | mu | Population mean $\left( \mu \right)$ | r mu | | sigma | Population standard deviation $\left( \sigma \right)$ | r sigma |

Simulation

n <- rep(x = n, times = reps)
x <- lapply(
  X = n,
  FUN = rnorm,
  mean = mu,
  sd = sigma
)
std <- lapply(
  X = x,
  FUN = z,
  mu = mu,
  sigm = sigma
)

Summarize simulation

mean_x <- mean(
  unlist(
    lapply(
      X = x,
      FUN = mean
    )
  )
)
sd_x <- mean(
  unlist(
    lapply(
      X = x,
      FUN = sd
    )
  )
)
mean_z <- mean(
  unlist(
    lapply(
      X = std,
      FUN = mean
    )
  )
)
sd_z <- mean(
  unlist(
    lapply(
      X = std,
      FUN = sd
    )
  )
)

$x$

| Item | Population | Sample | |:-------------------- |-----------:|-------------:| | Mean | r mu | r mean_x | | Standard deviation | r sigma | r sd_x |

$z$

| Item | Population | Sample | |:-------------------- |-----------:|-------------:| | Mean | 0 | r mean_z | | Standard deviation | 1 | r sd_z |

test_that("Mean of z converges to 0 and standard deviation converges to 1", {
  expect_equivalent(
    round(
      x = mean_z,
      digits = 0
    ),
    0
  )
  expect_equivalent(
    round(
      x = sd_z,
      digits = 0
    ),
    1
  )
})

test_that("Mean of x converges to mu and standard deviation converges to sigma", {
  expect_equivalent(
    round(
      x = mean_x,
      digits = 0
    ),
    mu
  )
  expect_equivalent(
    round(
      x = sd_x,
      digits = 0
    ),
    sigma
  )
})

jeksterslabds/boilerplatePackage documentation built on July 15, 2020, 5:01 a.m.