Description Usage Arguments Value See Also Examples
View source: R/plot.FitSigma.batch.R
plot.FitSigma.batch
plots the group-wise fitted seed
viability/survival curves from a FitSigma.batch
object as an object of
class ggplot
.
1 2 |
x |
An object of class |
limits |
logical. If |
grid |
logical. If |
... |
Default plot arguments. |
The plot of the seed viability curves as an object of class
ggplot
.
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")]
#----------------------------------------------------------------------------
# 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)
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