
Set of functions to study the hatching success.
HatchingSuccess.lnL(
par,
data,
fixed.parameters = NULL,
column.Incubation.temperature = "Incubation.temperature",
column.Hatched = "Hatched",
column.NotHatched = "NotHatched"
)
Return -log likelihood of the data and the parameters
A set of parameters.
A dataset in a data.frame with a least three columns: Incubation.temperature, Hatched and NotHatched
A set of parameters that must not be fitted.
Name of the column with incubation temperatures
Name of the column with hatched number
Name of the column with not hatched number
Marc Girondot
HatchingSuccess.lnL return -log likelihood of the data and the parameters
Other Hatching success:
HatchingSuccess.MHmcmc()
,
HatchingSuccess.MHmcmc_p()
,
HatchingSuccess.fit()
,
HatchingSuccess.model()
,
logLik.HatchingSuccess()
,
nobs.HatchingSuccess()
,
predict.HatchingSuccess()
if (FALSE) {
library(embryogrowth)
totalIncubation_Cc <- subset(DatabaseTSD,
Species=="Caretta caretta" &
Note != "Sinusoidal pattern" &
!is.na(Total) & Total != 0 &
!is.na(NotHatched) & !is.na(Hatched))
par <- c(S.low=0.5, S.high=0.3,
P.low=25, deltaP=10, MaxHS=0.8)
HatchingSuccess.lnL(par=par, data=totalIncubation_Cc)
g <- HatchingSuccess.fit(par=par, data=totalIncubation_Cc)
HatchingSuccess.lnL(par=g$par, data=totalIncubation_Cc)
t <- seq(from=20, to=40, by=0.1)
CIq <- predict(g, temperature=t)
par(mar=c(4, 4, 1, 1), +0.4)
plot(g)
}
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