df.rcbd: Create a data frame of randomized complete block design

In pwr4exp: Power Analysis for Research Experiments

Create a data frame of randomized complete block design

Description

Create a data frame of randomized complete block design

Usage

df.rcbd(treatments, label, blocks)

Arguments

treatments	An integer vector where each element represents the number of levels of the corresponding treatment factor. A single integer (e.g., treatments = n) specifies one treatment factor with n levels. When multiple factors are provided, they are arranged in a factorial treatment factor design. For example, treatments = c(2, 3) creates a 2x3 factorial design with the first factor having 2 levels and the second factor having 3 levels.
label	Optional. A list of character vectors, each corresponding to a treatment factor. The name of each vector specifies the factor's name, and its elements provide the labels for that factor's levels. If no labels are provided, default labels will be used. For a single treatment factor, the default is list(trt = c("1", "2", ...)), and for two treatment factors, the default is list(facA = c("1", "2", ...), facB = c("1", "2", ...)). For split-plot designs, the defaults are similar but include the ".main" and ".sub" suffixes for main plot and subplot factors. For example: list(trt.main = c("1", "2", ...), trt.sub = c("1", "2", ...)) and list(facA.main = c("1", "2", ...), facB.main = c("1", "2", ...), facA.sub = c("1", "2", ...), facB.sub = c("1", "2", ...)). Label sets should be arranged so that the main plot factors come first, followed by the subplot factors.
blocks	the number of blocks

Value

a data.frame representing the data structure of the design

pwr4exp documentation built on April 4, 2025, 1:46 a.m.