FitSigma.batch: Seed viability curve fitting to estimate multiple values of...

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

Description

Fit seed viability/survival curve to estimate multiple values of the seed lot constant (\mjseqnK_i) and the period to lose unit probit viability (\mjseqn\sigma) according to a grouping variable. \loadmathjax

Usage

FitSigma.batch(data, group, ...)

Arguments

data	A data frame with the seed viability data recorded periodically. It should possess columns with data on Viability percentage (to be indicated by the argument viability.percent), Sample size (to be indicated by the argument samp.size), Storage period (to be indicated by the argument storage.period) and Grouping variable (to be indicated by the argument group).
group	The name of the column in data with grouping variable as a character string.
...	Arguments to be passed on to FitSigma.

Value

A list of class FitSigma.batch with the following components:

data	A data frame with the data used for computing the models.
models	A data frame with the group-wise values of model parameters, \mjseqnK_i and \mjseqn\sigma and the fit statistics.

See Also

FitSigma

Examples

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

plot(df$storageperiod, df$viabilitypercent, col = df$crop)
legend(10, 60, legend=levels(df$crop),
       col = c("black", "red", "green"), pch = 1)

#----------------------------------------------------------------------------
# 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)
model1a

#----------------------------------------------------------------------------
# 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)
model1b

#----------------------------------------------------------------------------
# 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)
model2a

#----------------------------------------------------------------------------
# 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)
model2b

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