The `germinationmetrics` Package: A Brief Introduction"

out_type <- knitr::opts_knit$get("rmarkdown.pandoc.to")

r = getOption("repos")
r["CRAN"] = "https://cran.rstudio.com/"
#r["CRAN"] = "https://cloud.r-project.org/"
#r["CRAN"] = "https://ftp.iitm.ac.in/cran/"
options(repos = r)

# Workaround for missing pandoc in CRAN OSX build machines
out_type <- ifelse(out_type == "", "latex", out_type)

# Workaround for missing pandoc in Solaris build machines
out_type <- ifelse(identical (out_type, vector(mode = "logical", length = 0)),
                   "latex", out_type)
switch(out_type,
    html = {cat("<p> ICAR-National Bureau of Plant Genetic Resources, New Delhi.</p>")},
    latex = cat("\\begin{center}
 ICAR-National Bureau of Plant Genetic Resources, New Delhi.

\\end{center}" )
)

\begin{center} \vspace{6pt} \hrule \end{center}

knitr::opts_chunk$set(
  comment = "",
  fig.cap = ""
)

\tableofcontents

\begin{wrapfigure}{r}{0.35\textwidth} \vspace{-20pt} \begin{center} \includegraphics[width=0.33\textwidth]{r system.file("extdata", "germinationmetrics.png", package = "germinationmetrics")} \end{center} \vspace{-20pt} \end{wrapfigure}

logo

Overview

The package germinationmetrics is a collection of functions which implements various methods for describing the time-course of germination in terms of single-value germination indices as well as fitted curves.

The goal of this vignette is to introduce the users to these functions and get started in describing sequentially recorded germination count data. This document assumes a basic knowledge of R programming language.

Installation

The package can be installed using the following functions:

# Install from CRAN
install.packages('germinationmetrics', dependencies=TRUE)

# Install development version from Github
devtools::install_github("aravind-j/germinationmetrics")

Then the package can be loaded using the function

library(germinationmetrics)

\clearpage

Version History

# Fetch release version
# rver <- ifelse(test = gsub("(.\\.)(\\d+)(\\..)", "", getNamespaceVersion("germinationmetrics")) == "",
#                yes = getNamespaceVersion("germinationmetrics"),
#                no = as.vector(available.packages()["germinationmetrics",]["Version"]))
rver <- "0.1.5"

The current version of the package is r rver. The previous versions are as follows.

Table 1. Version history of germinationmetrics R package.

if (requireNamespace("RCurl", quietly = TRUE) & requireNamespace("httr", quietly = TRUE) & requireNamespace("XML", quietly = TRUE)) {
pkg <- "germinationmetrics"
link <- paste0("https://cran.r-project.org/src/contrib/Archive/", pkg, "/")
# cafile <- system.file("CurlSSL", "cacert.pem", package = "RCurl")
# page <- httr::GET(link, httr::config(cainfo = cafile))
page <- httr::GET(link)
page <- httr::content(page, as = 'text')
# page <- RCurl::getURL(link)

VerHistory <- XML::readHTMLTable(page)[[1]][,2:3]
colnames(VerHistory) <- c("Version", "Date")
VerHistory <- VerHistory[VerHistory$Version != "Parent Directory",]
VerHistory <- VerHistory[!is.na(VerHistory$Version), ]
VerHistory$Date <- as.Date(VerHistory$Date)
VerHistory$Version <- gsub("germinationmetrics_", "", VerHistory$Version)
VerHistory$Version <- gsub(".tar.gz", "", VerHistory$Version)

VerHistory <- VerHistory[order(VerHistory$Date), c("Version", "Date")]
rownames(VerHistory) <- NULL
} else {
print("Packages 'RCurl', 'httr' and 'XML' are required to generate this table")
}

knitr::kable(VerHistory)

To know detailed history of changes use news(package='germinationmetrics').

Germination count data

Typically in a germination test, the germination count data of a fixed number of seeds is recorded at regular intervals for a definite period of time or until all the seeds have germinated. These germination count data can be either partial or cumulative (Table 2).

Table 2 : A typical germination count data.

data <- data.frame(intervals = 1:14,
                   counts = c(0, 0, 0, 0, 4, 17, 10, 7, 1, 0, 1, 0, 0, 0))
data$cumulative.counts = cumsum(data$counts)

knitr::kable(data)

The time-course of germination can be plotted as follows.

data <- data.frame(intervals = 1:14,
                   counts = c(0, 0, 0, 0, 4, 17, 10, 7, 1, 0, 1, 0, 0, 0))

# Partial germination counts
x <- data$counts
# Cumulative germination counts
y <- cumsum(x)
# Time intervals of observations
int <- data$intervals

plot(int, cumsum(x))

Single-value germination indices

The details about the single-value germination indices implemented in germinationmetrics are described in Table 3.

\newpage \blandscape

\rowcolors{2}{gray!25}{white} \renewcommand{\arraystretch}{1.3}

Table 3 : Single-value germination indices implemented in germinationmetrics. \footnotesize

load("Indices.rda")

indices$`Function` <- paste("`", indices$`Function`, "`", sep = "")

# indices$Details <- gsub("\\r\\n\\r\\n", "$$\\\\, $$", indices$Details)
# indices$Details <- gsub("$$\\, $$$$", "$$", indices$Details, fixed = TRUE)
# indices$Details <- paste(indices$Details, "$$\\\\, $$", sep = "")

indices$Details <- gsub("\\r\\n", "\\\\\n", indices$Details)

#indices$Details <- gsub("\\r\\n\\r\\n", "##", indices$Details)
#indices$Details <- gsub("##", "\\\n", indices$Details)

indices$Reference <- gsub("\\[|\\]", "", indices$Reference)
#indices <- indices[-27,]
if (requireNamespace("pander", quietly = TRUE) & Sys.which('pandoc') != "" & Sys.which('pandoc-citeproc') != ""){
  pander::pander(indices, style='grid',
               split.cell = c(15, 18, 50, 10, 12, 8),
               justify = c('left'), split.table = Inf, keep.line.breaks = TRUE,
               use.hyphening = FALSE, missing = "")
} else {
  print("Package 'pander' and pandoc are required to generate this table")
}

\elandscape

\newpage

Examples

# Function saved in functions.R file
getCode <- function(myFunction, myPackage) {
  out <- example(myFunction, myPackage, ask = FALSE, character.only = TRUE,
          prompt.prefix = "", give.lines = TRUE)
  maxn <- grep(pattern = "### ** Examples", x = out, fixed = TRUE)
  out <- out[-(1:maxn)]

  out <- out[!(out %in% c("## No test: ", "## End(No test)"))]

  out <- gsub("^##D ", "", out)

  return(out)
}
library(germinationmetrics)
require(germinationmetrics)

GermPercent()


FirstGermTime(), LastGermTime(), PeakGermTime(), TimeSpreadGerm()


t50()


MeanGermTime(), VarGermTime(), SEGermTime(), CVGermTime()


MeanGermRate(), CVG(), VarGermRate(), SEGermRate(), GermRateRecip()


GermSpeed(), GermSpeedAccumulated(), GermSpeedCorrected()


GermSpeed(), GermSpeedAccumulated(), GermSpeedCorrected()


WeightGermPercent()


MeanGermPercent(), MeanGermNumber()


TimsonsIndex(), GermRateGeorge()


GermIndex()


EmergenceRateIndex()


PeakValue(), GermValue()


CUGerm()


GermSynchrony(), GermUncertainty()


Non-linear regression analysis

Several mathematical functions have been used to fit the cumulative germination count data and describe the germination process by non-linear regression analysis. They include functions such as Richard's, Weibull, logistic, log-logistic, gaussian, four-parameter hill function etc. Currently germinationmetrics implements the four-parameter hill function to fit the count data and computed various associated metrics.

Four-parameter hill function

The four-parameter hill function defined as follows [@el-kassaby_seed_2008].

$$ f(x) = y = y_0 + \frac{ax^b}{x^b+c^b} $$ Where, $y$ is the cumulative germination percentage at time $x$, $y_{0}$ is the intercept on the y axis, $a$ is the asymptote, $b$ is a mathematical parameter controlling the shape and steepness of the germination curve and $c$ is the "half-maximal activation level".

The details of various parameters that are computed from this function are given in Table 4.

Table 4 Germination parameters estimated from the four-parameter hill function.

\rowcolors{2}{gray!25}{white} \renewcommand{\arraystretch}{1.3}

load("Indices.rda")
fphf$Details <- gsub("\\r\\n", "\\\\\n", fphf$Details)
if (requireNamespace("pander", quietly = TRUE) & Sys.which('pandoc') != "" & Sys.which('pandoc-citeproc') != ""){
  pander::pander(fphf, style='grid',
               split.cell = c(15, 50, 10, 10),
               justify = c('left'), split.table = Inf, keep.line.breaks = TRUE,
               use.hyphening = FALSE, missing = "")
} else {
  print("Package 'pander' and pandoc are required to generate this table")
}

Examples

# Function saved in functions.R file
getCode <- function(myFunction, myPackage) {
  out <- example(myFunction, myPackage, ask = FALSE, character.only = TRUE,
          prompt.prefix = "", give.lines = TRUE)
  maxn <- grep(pattern = "### ** Examples", x = out, fixed = TRUE)
  out <- out[-(1:maxn)]

  out <- out[!(out %in% c("## No test: ", "## End(No test)"))]

  out <- gsub("^##D ", "", out)

  return(out)
}
library(germinationmetrics)
require(germinationmetrics)

FourPHFfit()



x <- c(0, 0, 0, 0, 4, 17, 10, 7, 1, 0, 1, 0, 0, 0)
y <- c(0, 0, 0, 0, 4, 21, 31, 38, 39, 39, 40, 40, 40, 40)
int <- 1:length(x)
total.seeds = 50

# From partial germination counts
#----------------------------------------------------------------------------
fit1 <- FourPHFfit(germ.counts = x, intervals = int,
                   total.seeds = 50, tmax = 20)

# From cumulative germination counts
#----------------------------------------------------------------------------
fit2 <- FourPHFfit(germ.counts = y, intervals = int,
                   total.seeds = 50, tmax = 20, partial = FALSE)

# Default plots
plot(fit1)
plot(fit2)

# No labels
plot(fit1, plotlabels = FALSE)
plot(fit2, plotlabels = FALSE)

# Only the FPHF curve
plot(fit1, rog = FALSE, t50.total = FALSE, t50.germ = FALSE,
     tmgr = FALSE, mgt = FALSE, uniformity = FALSE)
plot(fit2, rog = FALSE, t50.total = FALSE, t50.germ = FALSE,
     tmgr = FALSE, mgt = FALSE, uniformity = FALSE)

# Without y axis limits adjustment
plot(fit1, limits = FALSE)
plot(fit2, limits = FALSE)

Wrapper functions

Wrapper functions germination.indices() and FourPHFfit.bulk() are available in the package for computing results for multiple samples in batch from a data frame of germination counts recorded at specific time intervals.

germination.indices()

This wrapper function can be used to compute several germination indices simultaneously for multiple samples in batch.


FourPHFfit.bulk()

This wrapper function can be used to fit the four-parameter hill function for multiple samples in batch.


Multiple fitted curves generated in batch can also be plotted.


Citing germinationmetrics

detach("package:germinationmetrics", unload=TRUE)
suppressPackageStartupMessages(library(germinationmetrics))
cit <- citation("germinationmetrics")
# yr <- format(Sys.Date(), "%Y")
# cit[1]$year <- yr
# oc <- class(cit)
# 
# cit <- unclass(cit)
# attr(cit[[1]],"textVersion") <- gsub("\\(\\)",
#                                      paste("\\(", yr, "\\)", sep = ""),
#                                      attr(cit[[1]],"textVersion"))
# class(cit) <- oc
cit

Session Info

sessionInfo()

References



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germinationmetrics documentation built on Feb. 17, 2021, 5:09 p.m.