Description Usage Arguments Value See Also Examples
View source: R/FitSigma.batch.R
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
1 |
data |
A data frame with the seed viability data recorded periodically. It should possess columns with data on
|
group |
The name of the column in |
... |
Arguments to be passed on to |
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. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | 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
|
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