View source: R/summary.phenology.R
summary.phenology | R Documentation |
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.
## S3 method for class 'phenology'
summary(
object,
resultmcmc = NULL,
season = NULL,
chain = 1,
series = "all",
replicate.CI.mcmc = "all",
replicate.CI = 10000,
level = 0.95,
print = TRUE,
...
)
object |
A result file generated by fit_phenology |
resultmcmc |
A mcmc object |
season |
The number of season to analyze |
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 |
summary.phenology prints the information from a result object.
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.
Marc Girondot marc.girondot@gmail.com
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()
,
phenology()
,
phenology2fitRMU()
,
phenology_MHmcmc()
,
phenology_MHmcmc_p()
,
plot.phenology()
,
plot.phenologymap()
,
plot_delta()
,
plot_phi()
,
print.phenology()
,
print.phenologymap()
,
print.phenologyout()
,
remove_site()
,
result_Gratiot
,
result_Gratiot1
,
result_Gratiot2
,
result_Gratiot_Flat
,
result_Gratiot_mcmc
,
summary.phenologymap()
,
summary.phenologyout()
## 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)
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