df.spd

Create data frame for split-plot design

Provides tools for calculating statistical power for experiments
analyzed using linear mixed models. It supports standard designs, including
randomized block, split-plot, and Latin Square designs, while offering flexibility
to accommodate a variety of other complex study designs.

Kai Wang

pwr4exp

Power Analysis for Research Experiments

Mutian Niu

df.spd function

<dl><dt>trt.main</dt>
<dd>an integer-valued vector specifying the treatment structure at
main plot level, similar to <code>df.crd</code>.</dd>
<dt>trt.sub</dt>
<dd>an integer-valued vector specifying the treatment structure at
sub plot level, similar to <code>trt.main</code>.</dd>
<dt>label</dt>
<dd>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
<code>list(trt = c("1", "2", ...))</code>, and for two treatment factors, the default is
<code>list(facA = c("1", "2", ...), facB = c("1", "2", ...))</code>.
For split-plot designs, the defaults are similar but include the ".main" and ".sub" suffixes for main plot and subplot factors.
For example:
<code>list(trt.main = c("1", "2", ...), trt.sub = c("1", "2", ...))</code> and
<code>list(facA.main = c("1", "2", ...), facB.main = c("1", "2", ...),
 facA.sub = c("1", "2", ...), facB.sub = c("1", "2", ...))</code>.
Label sets should be arranged so that the main plot factors come first, followed by the subplot factors.</dd>
<dt>replicates</dt>
<dd>the number of experimental units (main plots) per treatment
of main plot factors.</dd></dl>

Arguments

Create data frame for split-plot design — df.spd

<dl>

<dt>trt.main</dt>
<dd>an integer-valued vector specifying the treatment structure at
main plot level, similar to <code>df.crd</code>.</dd>


<dt>trt.sub</dt>
<dd>an integer-valued vector specifying the treatment structure at
sub plot level, similar to <code>trt.main</code>.</dd>


<dt>label</dt>
<dd>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
<code>list(trt = c("1", "2", ...))</code>, and for two treatment factors, the default is
<code>list(facA = c("1", "2", ...), facB = c("1", "2", ...))</code>.
For split-plot designs, the defaults are similar but include the ".main" and ".sub" suffixes for main plot and subplot factors.
For example:
<code>list(trt.main = c("1", "2", ...), trt.sub = c("1", "2", ...))</code> and
<code>list(facA.main = c("1", "2", ...), facB.main = c("1", "2", ...),
 facA.sub = c("1", "2", ...), facB.sub = c("1", "2", ...))</code>.
Label sets should be arranged so that the main plot factors come first, followed by the subplot factors.</dd>


<dt>replicates</dt>
<dd>the number of experimental units (main plots) per treatment
of main plot factors.</dd>

</dl>

df.spd: Create data frame for split-plot design

Description

Usage

Value

Arguments