reproducer (version 0.4.2)

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, a matrix or a data frame. If in list mode, length(x) is assumed to correspond to the total number of groups. All groups are assumed to be independent. Missing values are automatically removed.

Usage

RandomizedBlocksAnalysis(x, con = c(-0.5, 0.5, -0.5, 0.5), alpha = 0.05)

Value

The t-test and its associated metrics (i.e., critical value stansard 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 effet size.

alpha

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

Author

Barbara Kitchenham and Lech Madeyski

Examples

set.seed(123)
x=list()
x[[1]]=stats::rnorm(10,0,1)
x[[2]]=stats::rnorm(10,0.8,1)
x[[3]]=stats::rnorm(10,0.5,1)
x[[4]]=stats::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
# $psihat
#      psihat  ci.lower ci.upper      p.value
# [1,] 1.2403 0.6698721 1.810728 0.0001062952
# dat=c(x[[1]],x[[2]],x[[3]],x[[4]])
# matx=matrix(dat,nrow=10,ncol=4)
# RandomizedBlocksAnalysis(matx,con=c(-1,1,-1,1)/2,alpha=0.05)
#$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