summary.cosimmr_output {cosimmr} | R Documentation |
Summarises the output created with cosimmr_ffvb
Description
Produces textual summaries and convergence diagnostics for an object created
with cosimmr_ffvb
. The different
options are: 'quantiles' which produces credible intervals
for the parameters, 'statistics' which produces means and standard
deviations, and 'correlations' which produces correlations between the
parameters.
Usage
## S3 method for class 'cosimmr_output'
summary(
object,
type = c("quantiles", "statistics", "correlations"),
obs = 1,
...
)
Arguments
object |
An object of class |
type |
The type of output required. At least none of quantiles', 'statistics', or 'correlations'. |
obs |
The observation to generate a summary for. Defaults to 1. |
... |
Not used |
Details
The quantile output allows easy calculation of 95 per cent credible intervals of the posterior dietary proportions. The correlations allow the user to judge which sources are non-identifiable.
Value
A list containing the following components:
quantiles |
The quantiles of each parameter from the posterior distribution |
statistics |
The means and standard deviations of each parameter |
correlations |
The posterior correlations between the parameters |
Note that this object is reported silently so will be discarded unless the function is called with an object as in the example below.
Author(s)
Emma Govan <emmagovan@gmail.com> Andrew Parnell
See Also
See cosimmr_ffvb
for creating objects suitable for
this function, and many more examples.
See also cosimmr_load
for creating cosimmr objects,
plot.cosimmr_input
for creating isospace plots,
plot.cosimmr_output
for plotting output.
Examples
# A simple example with 10 observations, 2 tracers and 4 sources
# The data
data(geese_data_day1)
cosimmr_1 <- with(
geese_data_day1,
cosimmr_load(
formula = mixtures ~ 1,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means
)
)
# Plot
plot(cosimmr_1)
# FFVB run
cosimmr_1_out <- cosimmr_ffvb(cosimmr_1)
# Summarise
summary(cosimmr_1_out) # This outputs all the summaries
summary(cosimmr_1_out, type = "quantiles") # Just the diagnostics
# Store the output in an
ans <- summary(cosimmr_1_out,
type = c("quantiles", "statistics")
)