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Walkthrough blocking on a covariate

Source: R/walkthrough_blocking.R
walkthrough_blocking.Rd

This function produces a step-by-step demonstration of how researchers can 'block' on a continuous covariate and how they can analyse the data of a randomised block design.

Usage

walkthrough_blocking(
  n = 10,
  diff = 0,
  sd = 1,
  rho = 0.8,
  showdata = FALSE,
  M = NULL
)

Arguments

n

The number of data points per group.

diff

The boost that participants in the intervention group receive.

sd

The standard deviation of the normal distributions from which the data are drawn.

rho

The correlation between the covariate and the outcome (pre-intervention) in the population.

showdata

Do you want to output a dataframe containing the plotted data (TRUE) or not (FALSE, default)?

M

NULL (default) when using exhaustive randomisation testing; else set to the number of Monte Carlo runs desired.

Details

Data are generated from a normal distribution with the requested standard deviation; a covariate is also generated. The data points are then grouped in pairs based on their covariate scores. Within each pair, the data points are then randomly assigned to the control or intervention group. Data points in the intervention group receive a boost as specified by 'diff'. Finally, a significance test is ran on the data. This significance test is a randomisation test using the mean difference as the test statistic. The p-value reported is a two-sided one.

If n is larger than 16 and M is not specified, M is set to 65536.

Examples

if (FALSE) { # \dontrun{
walkthrough_blocking(n = 12, diff = 0.2, sd = 1.3, rho = 0.8)

# Save data and double check results
dat <- walkthrough_blocking(n = 12, diff = 0.2, sd = 1.3, rho = 0.8, showdata = TRUE)
anova(lm(score ~ factor(block) + group, data = dat))
} # }