blood_lead

Children of parents who had worked in a factory where lead was used in
making batteries were matched by age, exposure to traffic, and neighborhood
with children whose parents did not work in lead-related industries. Whole
blood was assessed for lead content yielding measurements in mg/dl

datasets

Create what we call Elemental Graphics for display of
anova results. The term elemental derives from the fact
that each function is aimed at construction of
graphical displays that afford direct visualizations of
data with respect to the fundamental questions that
drive the particular anova methods. This package
represents a modification of the original granova
package; the key change is to use 'ggplot2', Hadley
Wickham's package based on Grammar of Graphics concepts
(due to Wilkinson). The main function is granovagg.1w()
(a graphic for one way ANOVA); two other functions
(granovagg.ds() and granovagg.contr()) are to construct
graphics for dependent sample analyses and
contrast-based analyses respectively. (The function
granova.2w(), which entails dynamic displays of data, is
not currently part of 'granovaGG'.) The 'granovaGG'
functions are to display data for any number of groups,
regardless of their sizes (however, very large data
sets or numbers of groups can be problematic). For
granovagg.1w() a specialized approach is used to
construct data-based contrast vectors for which anova
data are displayed. The result is that the graphics use
a straight line to facilitate clear interpretations
while being faithful to the standard effect test in
anova. The graphic results are complementary to
standard summary tables; indeed, numerical summary
statistics are provided as side effects of the graphic
constructions. granovagg.ds() and granovagg.contr() provide
graphic displays and numerical outputs for a dependent
sample and contrast-based analyses. The graphics based
on these functions can be especially helpful for
learning how the respective methods work to answer the
basic question(s) that drive the analyses. This means
they can be particularly helpful for students and
non-statistician analysts. But these methods can be of
assistance for work-a-day applications of many kinds,
as they can help to identify outliers, clusters or
patterns, as well as highlight the role of non-linear
transformations of data. In the case of granovagg.1w()
and granovagg.ds() several arguments are provided to
facilitate flexibility in the construction of graphics
that accommodate diverse features of data, according to
their corresponding display requirements. See the help
files for individual functions.

Brian A. Danielak

granovaGG

Graphical Analysis of Variance Using ggplot2

Robert M. Pruzek

William E. J. Doane

James E. Helmreich

Jason Bryer

blood_lead function

A dataframe with 33 observations on the following 2 variables, no
 NAs.
<dl><dt>Exposed</dt>
<dd>Blood lead level of exposed child, mg/dl.</dd>
<dt>Control</dt>
<dd>Blood lead level of exposed child, mg/dl.</dd></dl>

Format

Blood lead levels of lead workers' children matched with similar control
children. — blood_lead

A dataframe with 33 observations on the following 2 variables, no
 NAs.
<dl>

<dt>Exposed</dt>
<dd>Blood lead level of exposed child, mg/dl.</dd>


<dt>Control</dt>
<dd>Blood lead level of exposed child, mg/dl.</dd>



</dl>

blood_lead: Blood lead levels of lead workers' children matched with similar control children.

Description

Arguments

Format

References