| summary.phenology {phenology} | R Documentation |
Print the result information from a result object.
Description
The function summary.phenology shows result and estimates confidence interval.
If several years are analyzed, the sum_synthesis result can be obtained only if there is
not a mix of bisextile and non-bisextile years.
Usage
## S3 method for class 'phenology'
summary(
object,
resultmcmc = NULL,
chain = 1,
series = "all",
replicate.CI.mcmc = "all",
replicate.CI = 10000,
level = 0.95,
print = TRUE,
...
)
Arguments
object |
A result file generated by fit_phenology |
resultmcmc |
A mcmc object |
chain |
The number of chain to be used in resultmcmc |
series |
Names of the series to be analyzed or "all" |
replicate.CI.mcmc |
Number of iterations to be used or "all" |
replicate.CI |
Number of replicates for ML resampling |
level |
Level to estimate confidence interval or credibility interval |
print |
Should information be shown |
... |
Not used |
Details
summary.phenology prints the information from a result object.
Value
A list with five objects: synthesis is a data.frame with global estimate of nesting.
details_MCMC, details_ML, details_mean are lists with day by day information for each series, and
sum_synthesis is the synthesis of the sum of all analyzed time-series.
Author(s)
Marc Girondot marc.girondot@gmail.com
See Also
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology2fitRMU(),
phenology_MHmcmc_p(),
phenology_MHmcmc(),
phenology(),
plot.phenologymap(),
plot.phenology(),
plot_delta(),
plot_phi(),
print.phenologymap(),
print.phenologyout(),
print.phenology(),
remove_site(),
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
result_Gratiot,
summary.phenologymap(),
summary.phenologyout()
Examples
## Not run:
library(phenology)
# Read a file with data
data(Gratiot)
# Generate a formatted list nammed data_Gratiot
data_Gratiot<-add_phenology(Gratiot, name="Complete",
reference=as.Date("2001-01-01"), format="%d/%m/%Y")
# Generate initial points for the optimisation
parg<-par_init(data_Gratiot, fixed.parameters=NULL)
# Run the optimisation
result_Gratiot<-fit_phenology(data=data_Gratiot,
fitted.parameters=parg, fixed.parameters=NULL)
data(result_Gratiot)
# Display information from the result
s <- summary(result_Gratiot)
# Using mcmc
s <- summary(object=result_Gratiot, resultmcmc=result_Gratiot_mcmc)
## End(Not run)