create_designs: Creation of Standard Experimental Designs

In pwr4exp: Power Analysis for Research Experiments

Creation of Standard Experimental Designs

Description

These functions facilitate the creation of standard experimental designs commonly used in agricultural studies for power analysis. Unlike mkdesign which requires a pre-existing data frame, these functions allow users to directly specify key design characteristics to generate experimental layouts. Quantitative parameters describing fixed and random effects remain consistent with those in mkdesign.

Usage

designCRD(
  treatments,
  label,
  replicates,
  formula,
  beta = NULL,
  means = NULL,
  sigma2,
  template = FALSE,
  REML = TRUE
)

designRCBD(
  treatments,
  label,
  blocks,
  formula,
  beta = NULL,
  means = NULL,
  vcomp,
  sigma2,
  template = FALSE,
  REML = TRUE
)

designLSD(
  treatments,
  label,
  squares = 1,
  reuse = c("row", "col", "both"),
  formula,
  beta = NULL,
  means = NULL,
  vcomp,
  sigma2,
  template = FALSE,
  REML = TRUE
)

designCOD(
  treatments,
  label,
  squares = 1,
  formula,
  beta = NULL,
  means = NULL,
  vcomp,
  sigma2,
  template = FALSE,
  REML = TRUE
)

designSPD(
  trt.main,
  trt.sub,
  label,
  replicates,
  formula,
  beta = NULL,
  means = NULL,
  vcomp,
  sigma2,
  template = FALSE,
  REML = TRUE
)

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.
replicates	The number of experimental units per treatment in a completely randomized design or the number of experimental units (main plots) per treatment of main plot factors.
formula	A right-hand-side formula specifying the model for testing treatment effects, with terms on the right of ~ , following lme4::lmer syntax for random effects. If not specified, a default formula with main effects and all interactions is used internally.
beta	One of the optional inputs for fixed effects. A vector of model coefficients where factor variable coefficients correspond to dummy variables created using treatment contrast (stats::contr.treatment).
means	One of the optional inputs for fixed effects. A vector of marginal or conditioned means (if factors have interactions). Regression coefficients are required for numerical variables. Either beta or means must be provided, and their values must strictly follow a specific order. A template can be created to indicate the required input values and their order. See mkdesign for more information.
sigma2	error variance.
template	Default is FALSE. If TRUE, a template for beta, means, and vcomp is generated to indicate the required input order.
REML	Specifies whether to use REML or ML information matrix. Default is TRUE (REML).
blocks	The number of blocks.
vcomp	A vector of variance-covariance components for random effects, if present. The values must follow a strict order. See mkdesign.
squares	The number of replicated squares. By default, 1, i.e., no replicated squares.
reuse	A character string specifying how to replicate squares when there are multiple squares. Options are: "row" for reusing row blocks, "col" for reusing column blocks, or "both" for reusing both row and column blocks to replicate a single square.
trt.main	An integer-valued vector specifying the treatment structure at main plot level for a split plot design, similar to treatments.
trt.sub	An integer-valued vector specifying the treatment structure at sub plot level for a split plot design, similar to treatments.

Details

Each function creates a standard design as described below:

designCRD

Completely Randomized Design. By default, the model formula is ~ trt for one factor and ~ facA*facB for two factors, unless explicitly specified. If the label argument is provided, the formula is automatically updated with the specified treatment factor names.

designRCBD

Randomized Complete Block Design. Experimental units are grouped into blocks, with each treatment appearing exactly once per block (i.e., no replicates per treatment within a block). The default model formula is ~ trt + (1|block) for one factor and ~ facA*facB + (1|block) for two factors. If label is provided, the fixed effect parts of the formula are automatically updated with the specified names. The block factor is named "block" and not changeable.

designLSD

Latin Square Design. The default formula is ~ trt + (1|row) + (1|col) for one factor and ~ facA*facB + (1|row) + (1|col) for two factors. If label is provided, the fixed effect parts of the formula are automatically updated with the specified names. The names of row ("row") and column ("col") block factors are not changeable.

designCOD

Crossover Design, which is a special case of LSD with time periods and individuals as blocks. Period blocks are reused when replicating squares. The default formula is ~ trt + (1|subject) + (1|period) for one factor and ~ facA*facB + (1|subject) + (1|period) for two factors. If label is provided, the fixed effect parts of the formula are automatically updated with the specified names. Note that "subject" and "period" are the names for the two blocking factors and cannot be changed.

designSPD

Split Plot Design. The default formula includes the main effects of all treatment factors at both the main and sub-plot levels, their interactions, and the random effects of main plots: ~ . + (1|mainplot). If label is provided, the fixed effect parts of the formula are automatically updated with the specified names. The experimental unit at the main plot level (i.e., the block factor at the subplot level) is always named as "mainplot".

Value

A list object containing all essential components for power calculation. This includes:

• Structural components (deStruct): including the data frame, design matrices for fixed and random effects, variance-covariance matrices for random effects and residuals, etc.

• Internally calculated higher-level parameters (deParam), including variance-covariance matrix of beta coefficients (vcov_beta), variance-covariance matrix of variance parameters (vcov_varpar), gradient matrices (Jac_list), etc.

See Also

mkdesign, pwr.anova, pwr.contrast

Examples

# Evaluate the power of a CRD with one treatment factor
## Create a design object
crd <- designCRD(
  treatments = 4, # 4 levels of one treatment factor
  replicates = 12, # 12 units per level, 48 units totally
  means = c(30, 28, 33, 35), # means of the 4 levels
  sigma2 = 10 # error variance
)

## power of omnibus test
pwr.anova(crd)

## power of contrast
pwr.contrast(crd, which = "trt", contrast = "pairwise") # pairwise comparisons
pwr.contrast(crd, which = "trt", contrast = "poly") # polynomial contrasts

# More examples are available in `vignette("pwr4exp")`
# and on https://an-ethz.github.io/pwr4exp/

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