simmr_input object through the Fixed Form Variational
Bayes(FFVB) functionR/simmr_ffvb.R
simmr_ffvb.RdThis is the main function of simmr. It takes a simmr_input object
created via simmr_load, runs it in fixed form
Variational Bayes to determine the dietary proportions, and then
outputs a simmr_output object for further analysis and plotting
via summary.simmr_output and plot.simmr_output.
An object created via the function simmr_load
A list of values including arguments named mu_0
(prior for mu), and sigma_0 (prior for sigma).
A list of values including arguments named n_output
(number of rows in theta output), S (number of samples taken at each
iteration of the algorithm), P (patience parameter), beta_1
and beta_2 (adaptive learning weights), tau (threshold for
exploring learning space), eps_0 (fixed learning rate),
t_W (rolling window size)
An object of class simmr_output with two named top-level
components:
The simmr_input object given to the
simmr_ffvb function
A set of outputs produced by
the FFVB function. These can be analysed using the
summary.simmr_output and plot.simmr_output
functions.
Andrew C. Parnell, Donald L. Phillips, Stuart Bearhop, Brice X. Semmens, Eric J. Ward, Jonathan W. Moore, Andrew L. Jackson, Jonathan Grey, David J. Kelly, and Richard Inger. Bayesian stable isotope mixing models. Environmetrics, 24(6):387–399, 2013.
Andrew C Parnell, Richard Inger, Stuart Bearhop, and Andrew L Jackson. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE, 5(3):5, 2010.
simmr_load for creating objects suitable for this
function, plot.simmr_input for creating isospace plots,
summary.simmr_output for summarising output, and
plot.simmr_output for plotting output.
if (FALSE) {
## See the package vignette for a detailed run through of these 4 examples
# Data set 1: 10 obs on 2 isos, 4 sources, with tefs and concdep
data(geese_data_day1)
simmr_1 <- with(
geese_data_day1,
simmr_load(
mixtures = mixtures,
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(simmr_1)
# Print
simmr_1
# FFVB run
simmr_1_out <- simmr_ffvb(simmr_1)
# Print it
print(simmr_1_out)
# Summary
summary(simmr_1_out, type = "correlations")
summary(simmr_1_out, type = "statistics")
ans <- summary(simmr_1_out, type = c("quantiles", "statistics"))
# Plot
plot(simmr_1_out, type = "boxplot")
plot(simmr_1_out, type = "histogram")
plot(simmr_1_out, type = "density")
plot(simmr_1_out, type = "matrix")
# Compare two sources
compare_sources(simmr_1_out, source_names = c("Zostera", "Enteromorpha"))
# Compare multiple sources
compare_sources(simmr_1_out)
#####################################################################################
# A version with just one observation
data(geese_data_day1)
simmr_2 <- with(
geese_data_day1,
simmr_load(
mixtures = mixtures[1, , drop = FALSE],
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(simmr_2)
# FFVB run - automatically detects the single observation
simmr_2_out <- simmr_ffvb(simmr_2)
# Print it
print(simmr_2_out)
# Summary
summary(simmr_2_out)
ans <- summary(simmr_2_out, type = c("quantiles"))
# Plot
plot(simmr_2_out)
plot(simmr_2_out, type = "boxplot")
plot(simmr_2_out, type = "histogram")
plot(simmr_2_out, type = "density")
plot(simmr_2_out, type = "matrix")
#####################################################################################
# Data set 2: 3 isotopes (d13C, d15N and d34S), 30 observations, 4 sources
data(simmr_data_2)
simmr_3 <- with(
simmr_data_2,
simmr_load(
mixtures = mixtures,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means
)
)
# Get summary
print(simmr_3)
# Plot 3 times
plot(simmr_3)
plot(simmr_3, tracers = c(2, 3))
plot(simmr_3, tracers = c(1, 3))
# See vignette('simmr') for fancier axis labels
# FFVB run
simmr_3_out <- simmr_ffvb(simmr_3)
# Print it
print(simmr_3_out)
# Summary
summary(simmr_3_out)
summary(simmr_3_out, type = "quantiles")
summary(simmr_3_out, type = "correlations")
# Plot
plot(simmr_3_out)
plot(simmr_3_out, type = "boxplot")
plot(simmr_3_out, type = "histogram")
plot(simmr_3_out, type = "density")
plot(simmr_3_out, type = "matrix")
################################################################
# Data set 5 - Multiple groups Geese data from Inger et al 2006
# Do this in raw data format - Note that there's quite a few mixtures!
data(geese_data)
simmr_5 <- with(
geese_data,
simmr_load(
mixtures = mixtures,
source_names = source_names,
source_means = source_means,
source_sds = source_sds,
correction_means = correction_means,
correction_sds = correction_sds,
concentration_means = concentration_means,
group = groups
)
)
# Plot
plot(simmr_5,
xlab = expression(paste(delta^13, "C (per mille)", sep = "")),
ylab = expression(paste(delta^15, "N (per mille)", sep = "")),
title = "Isospace plot of Inger et al Geese data"
)
# Run MCMC for each group
simmr_5_out <- simmr_ffvb(simmr_5)
# Summarise output
summary(simmr_5_out, type = "quantiles", group = 1)
summary(simmr_5_out, type = "quantiles", group = c(1, 3))
summary(simmr_5_out, type = c("quantiles", "statistics"), group = c(1, 3))
# Plot - only a single group allowed
plot(simmr_5_out, type = "boxplot", group = 2, title = "simmr output group 2")
plot(simmr_5_out, type = c("density", "matrix"), grp = 6, title = "simmr output group 6")
# Compare sources within a group
compare_sources(simmr_5_out, source_names = c("Zostera", "U.lactuca"), group = 2)
compare_sources(simmr_5_out, group = 2)
# Compare between groups
compare_groups(simmr_5_out, source = "Zostera", groups = 1:2)
compare_groups(simmr_5_out, source = "Zostera", groups = 1:3)
compare_groups(simmr_5_out, source = "U.lactuca", groups = c(4:5, 7, 2))
}