sidbp


These data have been simulated with respect to the descriptive
statistics given in the bifactorial hypertension clinical trial reported
by Hung (2000). Various distributional properties have been realized for
normal and skewed cases with equal variances as well as with linearly
increasing variances. The latter means that the coefficient of variation
is held constant over the treatment groups. The group defining variable
is named <code>cb</code> in the data set. It has got the levels
<code>(0,0)</code>, <code>(1,0)</code>, <code>...</code>, <code>(2,3)</code> according to the respective dose
combinations. The data vectors with the different distributional
properties are <code>ynrmhom</code>, <code>ynrmhet</code>, <code>yloghom</code> and <code>yloghet</code>.


datasets

This package makes global and multiple inferences for
given bi- and trifactorial clinical trial designs using
bootstrap methods and a classical approach.

Peter Frommolt

bifactorial

Inferences for bi- and trifactorial trial designs

sidbp function

A data frame with 738 observations on the following 5 variables.
  <dl>
    <dt><code>cb</code></dt><dd>a factor with levels <code>(0,0)</code>,
      <code>(0,1)</code>, <code>...</code>, <code>(2,3)</code></dd> <dt><code>ynrmhom</code></dt><dd>A vector of normal and homoscedastic data.</dd> <dt><code>ynrmhet</code></dt><dd>A vector of normal and heteroscedastic data.</dd> <dt><code>yloghom</code></dt><dd>A vector of lognormal and homoscedastic data.</dd> <dt><code>yloghet</code></dt><dd>A vector of lognormal and heteroscedastic data.</dd> </dl> 

sidbp: Data of sitting distolic blood pressure (SiDBP)

Description

Arguments

Format

References

Examples