growthPheno-pkg: growthPheno

Description

growthPheno

Version: NA

Date: NA

Arguments

Index

For an overview of the use of these functions and an example see below.

(i) Data
`exampleData`	A small data set to use in function examples.
`RiceRaw.dat`	Data for an experiment to investigate a rice
	germplasm panel.
`tomato.dat`	Longitudinal data for an experiment to investigate
	tomato response to mycorrhizal fungi and zinc.
	(ii) Data frame manipulation
	`designFactors`
Adds the factors and covariates for a blocked,
split-plot design.	`getTimesSubset`
Forms a subset of 'responses' in 'data' that
contains their values for the nominated times.	`importExcel`
Imports an Excel imaging file and allows some
renaming of variables.	`longitudinalPrime`
Selects a set variables to be retained in a
data frame of longitudinal data.	`twoLevelOpcreate`
Creates a data.frame formed by applying, for
each response, abinary operation to the values of
two different treatments.
(iii) Plots
`plotAnom`	Identifies anomalous individuals and produces
	longitudinal plots without them and with just them.
`plotCorrmatrix`	Calculates and plots correlation matrices for a
	set of responses.
`plotDeviationsBoxes`	Produces boxplots of the deviations of the observed
	values from the smoothed values over values of x.
`plotImagetimes`	Plots the time within an interval versus the interval.
	For example, the hour of the day carts are imaged
	against the days after planting (or some other
	number of days after an event).
`plotLongitudinal`	Plots longitudinal data for a set of indiividuals
`plotMedianDeviations`	Calculates and plots the median of the deviations
	of the smoothed values from the observed values.
`probeSmoothing`	Compares, for a set of specified values of df and
	different smoothing methods, a response and the smooths
	of it, possibly along with growth rates calculated
	from the smooths.
	(iii) Smoothing
	`fitSpline`
Produce the fits from a natural cubic smoothing
spline applied to a response in a 'data.frame',
and growth rates can be computed using derivatives.	`splitSplines`
Adds the fits, and optionally growth rates computed
from derivatives, after fitting natural cubic
smoothing splines to subsets of a response to a
'data.frame'.
(iv) Growth rate and WUI calculation
`fitSpline`	Produce the fits from a natural cubic smoothing
	spline applied to a response in a 'data.frame',
	and growth rates can be computed using derivatives.
`GrowthRates`	Calculates growth rates (AGR, PGR, RGRdiff)
	between pairs of values in a vector.
`intervalGRaverage`	Calculates the growth rates for a specified
	time interval by taking weighted averages of
	growth rates for times within the interval.
`intervalGRdiff`	Calculates the growth rates for a specified
	time interval.
`splitContGRdiff`	Adds the growth rates calculated continuously
	over time for subsets of a response to a
	'data.frame'.
`splitSplines`	Adds the fits, and optionally growth rates computed
	from derivatives, after fitting natural cubic
	smoothing splines to subsets of a response to a
	'data.frame'.
`WUI`	Calculates the Water Use Index (WUI).
`intervalWUI`	Calculates water use indices (WUI) over a
	specified time interval to a data.frame.
	(v) General calculations
	`anom`
Tests if any values in a vector are anomalous
in being outside specified limits.	`calcLagged`
Replaces the values in a vector with the result
of applying an operation to it and a lagged value.	`calcTimes`
Calculates for a set of times, the time intervals
after an origin time and the position of each
within a time interval	`cumulate`
Calculates the cumulative sum, ignoring the
first element if exclude.1st is TRUE.	`intervalValueCalculate`
Calculates a single value that is a function of
an individual's values for a response over a
specified time interval.	`splitValueCalculate`
Calculates a single value that is a function of
an individual's values for a response.
(vi) Principal variates analysis (PV A)
`intervalPVA`	Selects a subset of variables observed within a
	specified time interval using PVA.
`PVA`	Selects a subset of variables using PVA.
`rcontrib`	Computes a measure of how correlated each
	variable in a set is with the other variable,
	conditional on a nominated subset of them.

Overview

This package can be used to analyse growth data using splines to smooth the trend of individual plant traces over time and then to extract traits for further analysis. This process is called smoothing and extraction of traits (SET) by Brien et al. (2020), who detail the use of `growthPheno` for carrying out the method.

The package `growthPheno` has tools that aid in choosing the degree of smoothing and the selection of traits. There are also functions for importing and orgainizing the data that are generally applicable, although they do have defaults that make them particularly adapted to data from a high-throughput phenotyping facility based on a Lemna-Tec Scananalyzer 3D system.

Data suitable for use with this package consists of columns of data obtained from a set of units (pots, carts or plots) over time. There should be a unique identifier for each unit, which by default is Snapshot.ID.Tag, and variable giving the Days after Planting for each measurement, by default Time.after.Planting..d.. In some cases, it is expected that there will be a column labelled Snapshot.Time.Stamp, which reflects the imaging time from which a particular data value was obtained. For imaging data, the carts/pots may be arranged in a grid of Lanes \(\times\) Positions.

The vignettes Tomato and Rice illustrate this process, the former being the example presented in Bren et al. (2020). Use `vignette("Tomato", package = "growthPheno") or `vignette("Rice", package = "growthPheno") to access either of the vignettes.

References

Brien, C., Jewell, N., Garnett, T., Watts-Williams, S. J., & Berger, B. (2020). Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data. *Plant Methods*, **16**, 36. <http://dx.doi.org/10.1186/s13007-020-00577-6>.

Description

Arguments

Index

Overview

References

See Also