| Name : R-granovaGG
| |
| Version : 1.4.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp154.2.1
| Date : 2024-06-07 15:23:21
|
| Group : Development/Libraries/Other
| Source RPM : R-granovaGG-1.4.1-lp154.2.1.src.rpm
|
| Size : 0.15 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Graphical Analysis of Variance Using ggplot2
|
Description :
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.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.4/x86_64 |
