plot.FitSigma.batch: Plot the fitted seed viability curves from a 'FitSigma.batch'...

In aravind-j/viabilitymetrics: Seed Viability Calculations and Curve Fitting

Description

plot.FitSigma.batch plots the group-wise fitted seed viability/survival curves from a FitSigma.batch object as an object of class ggplot.

Usage

## S3 method for class 'FitSigma.batch'
plot(x, limits = TRUE, grid = FALSE, ...)

Arguments

x	An object of class FitSigma.batch obtained as output from the FitSigma.batch function.
limits	logical. If TRUE, set the limits of y axis (viability percentage) between 0 and 100 in the viability curve plot. If FALSE, limits are set according to the data. Default is TRUE.
grid	logical. If TRUE, a symmetric matrix grid of plots is produced instead of a single plot with multiple curves. Default is FALSE.
...	Default plot arguments.

Value

The plot of the seed viability curves as an object of class ggplot.

See Also

FitSigma.batch

Examples

data(seedsurvival)
df <- seedsurvival[seedsurvival$moistruecontent == 7 &
                     seedsurvival$temperature == 25,
                   c("crop", "storageperiod", "rep",
                     "viabilitypercent", "sampsize")]

#----------------------------------------------------------------------------
# Generalised linear model with probit link function (without cv)
#----------------------------------------------------------------------------
model1a <- FitSigma.batch(data = df, group = "crop",
                          viability.percent = "viabilitypercent",
                          samp.size = "sampsize",
                          storage.period = "storageperiod",
                          generalised.model = TRUE)
plot(model1a)
plot(model1a, grid = TRUE)

#----------------------------------------------------------------------------
# Generalised linear model with probit link function (with cv)
#----------------------------------------------------------------------------
model1b <- FitSigma.batch(data = df, group = "crop",
                          viability.percent = "viabilitypercent",
                          samp.size = "sampsize",
                          storage.period = "storageperiod",
                          generalised.model = TRUE,
                          use.cv = TRUE, control.viability = 98)
plot(model1b)
plot(model1b, grid = TRUE)

#----------------------------------------------------------------------------
# Linear model after probit transformation (without cv)
#----------------------------------------------------------------------------
model2a <- FitSigma.batch(data = df, group = "crop",
                          viability.percent = "viabilitypercent",
                          samp.size = "sampsize",
                          storage.period = "storageperiod",
                          generalised.model = FALSE)
plot(model2a)
plot(model2a, grid = TRUE)

#----------------------------------------------------------------------------
# Linear model after probit transformation (with cv)
#----------------------------------------------------------------------------
model2b <- FitSigma.batch(data = df, group = "crop",
                          viability.percent = "viabilitypercent",
                          samp.size = "sampsize",
                          storage.period = "storageperiod",
                          generalised.model = FALSE,
                          use.cv = TRUE, control.viability = 98)
plot(model2b)
plot(model2b, grid = TRUE)

aravind-j/viabilitymetrics documentation built on May 15, 2021, 9:10 a.m.