simmr_ffvb: Run a 'simmr_input' object through the Fixed Form Variational...

In simmr: A Stable Isotope Mixing Model

Run a simmr_input object through the Fixed Form Variational Bayes(FFVB) function

Description

This 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.

Usage

simmr_ffvb(
  simmr_in,
  prior_control = list(mu_0 = rep(0, simmr_in$n_sources), sigma_0 = 1),
  ffvb_control = list(n_output = 3600, S = 100, P = 10, beta_1 = 0.9, beta_2 = 0.9, tau =
    100, eps_0 = 0.0225, t_W = 50)
)

Arguments

simmr_in	An object created via the function simmr_load
prior_control	A list of values including arguments named mu_0 (prior for mu), and sigma_0 (prior for sigma).
ffvb_control	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)

Value

An object of class simmr_output with two named top-level components:

input	The simmr_input object given to the simmr_ffvb function
output	A set of outputs produced by the FFVB function. These can be analysed using the summary.simmr_output and plot.simmr_output functions.

Author(s)

Andrew Parnell <[e-mail]>, Emma Govan

References

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.

See Also

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.

Examples

## Not run: 
## 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))

## End(Not run)

simmr documentation built on Oct. 16, 2024, 5:08 p.m.