Principal Variable Analysis (PVA) (Cumming and Wooff, 2007) selects a subset 
 from a set of the variables such that the variables in the subset are as 
 uncorrelated as possible, in an effort to ensure that all aspects of the 
 variation in the data are covered.

manip

data

Assists in the plotting and functional smoothing of traits
measured over time and the extraction of features from these traits,
implementing the SET (Smoothing and Extraction of Traits) method
described in Brien et al. (2020) Plant Methods, 16. Smoothing of
growth trends for individual plants using natural cubic smoothing
splines or P-splines is available for removing transient effects and
segmented smoothing is available to deal with discontinuities in
growth trends. There are graphical tools for assessing the adequacy
of trait smoothing, both when using this and other packages, such as
those that fit nonlinear growth models. A range of per-unit (plant,
pot, plot) growth traits or features can be extracted from the
data, including single time points, interval growth rates and other
growth statistics, such as maximum growth or days to maximum growth.
The package also has tools adapted to inputting data from
high-throughput phenotyping facilities, such from a Lemna-Tec
Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/>
for more information). The package 'growthPheno' can also be
installed from <http://chris.brien.name/rpackages/>.

Chris Brien

growthPheno

Functional Analysis of Phenotypic Growth Data to Smooth and
Extract Traits

PVA function

<dl> <dt>obj</dt>
<dd>A <code><a href="/link/data.frame?package=growthPheno&version=2.1.23" data-mini-rdoc="growthPheno::data.frame">data.frame</a></code> containing the columns of variables from which the 
 selection is to be made.</dd> <dt>...</dt>
<dd>allows passing of arguments to other functions</dd></dl>

Arguments

Author

Selects a subset of variables using Principal Variable Analysis (PVA) — PVA

<dl>

 <dt>obj</dt>
<dd>A <code><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></code> containing the columns of variables from which the 
 selection is to be made.</dd>

 <dt>...</dt>
<dd>allows passing of arguments to other functions</dd>

</dl>

PVA: Selects a subset of variables using Principal Variable Analysis (PVA)

Description

Usage

Value

Arguments

Author

Details

References

See Also