randomizationInference-package: Flexible Randomization-Based Inference

In randomizationInference: Flexible Randomization-Based Inference

Flexible Randomization-Based Inference

Description

Randomization-based p-values and null intervals for a wide variety of experimental scenarios, with corresponding visualizations.

Details

Package:	randomizationInference
Type:	Package
Version:	1.0.4
Date:	2022-05-17
License:	GPL-2
Main functions:	randTest, randPlot, randInterval

The randomizationInference package conducts randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.

Author(s)

Joseph J. Lee and Tirthankar Dasgupta

Maintainer: Joseph J. Lee <[e-mail]>

References

Wu, C. F. J. and Hamada, M. (2009) Experiments, Planning, Analysis and Optimization (2nd ed), Wiley.

Moore, David S., and George P. McCabe (1989). Introduction to the Practice of Statistics.

Examples

# Completely randomized design example
# with one treatment factor at two levels
w <- c(rep(0, 5), rep(1, 5))
y <- rnorm(10, mean = 0, sd = 1)
# Two-sided test
twoSidedTest <- randTest(y, w, nrand = 50, calcTestStat = diffMeans)
randInterval(twoSidedTest)
randPlot(twoSidedTest)
# One-sided test
oneSidedTest <- randTest(
  y, w,
  nrand = 50,
  calcTestStat = diffMeans, 
  alternative = "greater"
)
# Two=sided test with non-zero null hypothesis
nonZeroTest <- randTest(
  y,
  w,
  nrand = 50,
  calcTestStat = diffMeans, 
  calcPO = constEffect,
  poOptions = list(tau = 2),
  null = 2
)

# Randomized block design example
# with one treatment factor at three levels
x <- rep(1:3, 4)
w_block <- rep(1:4, 3)
y_block <- rnorm(12, mean = x, sd = 1)
blockTest <- randTest(
  y_block,
  w_block,
  nrand = 50, 
  calcTestStat = anovaF,
  calcOptions = list(block = x), 
  randOptions = list(type = "block", block = x)
)
randInterval(blockTest)
randPlot(blockTest)

# 4x4 Latin square example (from the Wu/Hamada reference)
row <- rep(1:4, 4)
col <- c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4))
w_latin <- c(
  "C", "D", "B", "A", "A", "B", "D", "C", 
  "D", "C", "A", "B", "B", "A", "C", "D"
)
y_latin <- c(
  235, 236, 218, 268, 251, 241, 227, 229, 
  234, 273, 274, 226, 195, 270, 230, 225
)
latinTest <- randTest(
  y_latin,
  w_latin,
  nrand = 50, 
  calcTestStat = anovaF, 
  calcOptions = list(row = row, col = col), 
  randOptions = list(type = "Latin", row = row, col = col)
)
randInterval(latinTest)
randPlot(latinTest)

# User-defined randomization example
# Partial randomization: first four assignments are fixed
# Due to physical limitations
# User-defined randomization function
# Input: number of random assignments, function options
# Output: list of random assignments
myRand <- function(nrand, userOptions = NULL){
  w_fixed = c(0, 0, 1, 1)
  lapply(1:nrand, function(i) c(w_fixed, sample(rep(0:1, 5))))
}
w_user <- c(c(0, 0, 1, 1), c(0, 1, 1, 0, 0, 0, 1, 1, 0, 1)) # observed assignment
y_user <- rnorm(14, mean = 0, sd = 1)
userTest <- randTest(
  y_user,
  w_user,
  nrand = 50,
  calcTestStat = diffMeans, 
  randOptions = list(type = "user.defined"),
  userRand = myRand
)
randInterval(userTest)
randPlot(userTest)

# 2^3 factorial design example
# three treatment factors (OT, CP, and ST) at two levels each
OT <- c(-1, -1, -1, -1, 1, 1, 1, 1)
CP <- c(-1, -1, 1, 1, -1, -1, 1, 1)
ST <- rep(c(-1, 1), 4)
w_fac <- cbind(OT, CP, ST)
y_fac <- c(67, 79, 61, 75, 59, 90, 52, 87)
# Testing the main effect of factor "OT"
facTest1 <- randTest(
  y_fac,
  w_fac,
  nrand = 50,
  calcTestStat = diffMeans, 
  calcOptions = list(factor = 1, pair = c(-1, 1))
)
# Testing all three main effects simultaneously
facTest2 <- randTest(
  y_fac,
  w_fac,
  nrand = 50, 
  calcTestStat = diffMeansVector,
  calcOptions = list(
    factors = 1:3, 
    pairs = matrix(rep(c(-1, 1), 3), ncol = 2, byrow = TRUE)
  )
)
# Testing all contrasts simultaneously
w_facNew <- cbind(OT, CP, ST, OT*CP, OT * ST, CP * ST, OT * CP * ST)
facTest3 <- randTest(
  y_fac,
  w_facNew,
  nrand = 50, 
  calcTestStat = diffMeansVector,
  calcOptions = list(
    factors = 1:7, 
    pairs = matrix(rep(c(-1, 1), 7), ncol = 2, byrow = TRUE)
  )
)
randInterval(facTest3)
randPlot(facTest3, plotDim = c(2, 4))

# Reading comprehension pre- and post-test example
data(reading)
# Ignoring blocks
readingTest1 <- randTest(
  y = reading$Diff1,
  w = reading$Group,
  nrand = 50, 
  calcTestStat = anovaF
)
# Testing within-block pairwise effects
readingTest2 <- randTest(
  y = reading$Diff1,
  w = reading$Group,
  nrand = 50, 
  calcTestStat = withinBlockEffects, 
  calcOptions = list(
    block = reading$Block, 
    pairs = rbind(
      c("Basal", "DRTA"),
      c("Basal", "Strat"), 
      c("DRTA", "Strat"),
      c("Basal", "DRTA"), 
      c("Basal", "Strat"),
      c("DRTA", "Strat")
    ), 
    blockindex = c(rep(1, 3), rep(2, 3))
  ), 
  randOptions = list(type = "block", block = reading$Block)
)
randInterval(readingTest2)
randPlot(readingTest2, plotDim = c(2, 3))

randomizationInference documentation built on May 18, 2022, 1:06 a.m.