reproducer (version 0.5.3)

RandomizedBlocksAnalysis: RandomizedBlocksAnalysis

Description

The function performs a heteroscedastic test of a two treatment by J blocks randomized blocks effect size. The data are assumed to be stored in $x$ in list mode. All groups are assumed to be independent. Missing values are not permitted.

Usage

RandomizedBlocksAnalysis(
  x,
  con = c(-0.5, 0.5, -0.5, 0.5),
  alpha = 0.05,
  alternative = "two.sided"
)

Value

The t-test and its associated metrics (i.e., critical value standard error and degrees of freedom) and the estimate of the contrast with its upper and lower confidence interval bounds and p-value.

Arguments

x

the structure holding the data. In list format, for a 2 treatment by J block randomized blocks experiments, there are 2J list elements each one specifying the outcome for a specific block and a specific treatment.

con

is a 2J list containing the contrast coefficients that are used to calculate the mean effect size.

alpha

is the Type 1 error level used for the test of significance (default 0.05)

alternative

The type of statistical test. Valid values are one of c('two.sided', 'greater', 'less')

Author

Barbara Kitchenham and Lech Madeyski

Examples

set.seed(123)
x <- list()
x[[1]] <- rnorm(10, 0, 1)
x[[2]] <- rnorm(10, 0.8, 1)
x[[3]] <- rnorm(10, 0.5, 1)
x[[4]] <- rnorm(10, 1.3, 1)
vec <- c(-1, 1, -1, 1) / 2
RandomizedBlocksAnalysis(x, con = vec, alpha = 0.05)
# $n
# [1] 10 10 10 10
# $test
#      test     crit        se       df
# [1,] 4.432644 2.038622 0.2798104 31.33793
# $psihat
#      psihat  ci.lower ci.upper      p.value
# [1,] 1.2403 0.6698721 1.810728 0.0001062952
# $sig
# [1] TRUE
RandomizedBlocksAnalysis(x,con=vec,alpha=0.05,alternative='greater')
# n
# [1] 10 10 10 10
# $test
#          test     crit        se       df
# [1,] 4.432644 1.694956 0.2798104 31.33793
# $psihat
# psihat  ci.lower ci.upper      p.value
#[1,] 1.2403 0.7660336      Inf 5.314762e-05
# $sig
# [1] TRUE
RandomizedBlocksAnalysis(x,con=-vec,alpha=0.05,alternative='greater')
#$n
#[1] 10 10 10 10
#$test
#          test     crit        se       df
#[1,] -4.432644 1.694956 0.2798104 31.33793
#$psihat
#      psihat  ci.lower ci.upper   p.value
#[1,] -1.2403 -1.714566      Inf 0.9999469
#$sig
#[1] FALSE
x[[5]]=rnorm(10,-0.2,1)
x[[6]]=rnorm(10,0.6,1)
vec=c(1,-1,1,-1,1,-1)/3
RandomizedBlocksAnalysis(x,con=vec,alpha=0.05,alternative='less')
#$n
#[1] 10 10 10 10 10 10
#$test
#          test     crit       se       df
#[1,] -4.946987  1.677021 0.236575 48.29776
#$psihat
#        psihat ci.lower   ci.upper     p.value
#[1,] -1.170334     -Inf -0.7735925 4.76961e-06
#$sig
#[1] TRUE