, Andrew Parnell [aut]
| Title: | Stable Isotope Bayesian Ellipses in R |
|---|---|
| Description: | Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. |
| Authors: | Andrew Jackson [aut, cre]
, Andrew Parnell [aut]
|
| Maintainer: | Andrew Jackson <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.1.9 |
| Built: | 2024-11-12 06:52:22 UTC |
| Source: | CRAN |
This function adds an ellipse based on means and covariance to an existing plot. The ellipse can be scaled so as to represent any prediction interval of the data you wish, or alternatively any confidence interval of the bivariate means.
addEllipse( mu, sigma, m = NULL, n = 100, p.interval = NULL, ci.mean = FALSE, small.sample = FALSE, do.plot = TRUE, ... )addEllipse( mu, sigma, m = NULL, n = 100, p.interval = NULL, ci.mean = FALSE, small.sample = FALSE, do.plot = TRUE, ... )
mu |
a vector of length two specifying the bivariate means |
sigma |
a 2x2 covariance matrix for the data |
m |
is the sample size of the dataset on which the ellipse is to be
plotted. This is only informative if calculating the confidence interval of
the bivariate mean, which requires a correction of |
n |
the number of data points to be used to plot the ellipse. More
points makes for a smoother ellipse, especially if it has high
eccentricity. Defaults to |
p.interval |
the quantile to be used to construct a prediction ellipse
that contains p.interval proportion of the data. By default,
|
ci.mean |
a logical that determines whether the ellipse drawn is a
prediction ellipse of the entire data, or a confidence interval of the
bivariate means. Defaults to |
small.sample |
a logical that determines whether or not the small sample size correction is to be applied (TRUE) or not (FALSE). Defaults to FALSE. This allows SEAc rather than SEA to be plotted, but the correction can be applied to any percentile ellipse. |
do.plot |
A logical that determines whether plotting should occur (TRUE and default) or not (FALSE). Setting to false is useful if you want to access the coordinates of the ellipse in order to calculate overlap between ellipses for example. |
... |
additional arguments as a list to be passed to |
A n x 2 matrix comprising the x and y coordinates of the
ellipse.
#-- NOT RUN -- # data(demo.siber.data) # my.siber.data <- createSiberObject(demo.siber.data) # communityMetricsML(my.siber.data) # -- END --#-- NOT RUN -- # data(demo.siber.data) # my.siber.data <- createSiberObject(demo.siber.data) # communityMetricsML(my.siber.data) # -- END --
Plots the posterior densities
allCentroidVectors(centroids, upper = TRUE, do.plot = TRUE, ...)allCentroidVectors(centroids, upper = TRUE, do.plot = TRUE, ...)
centroids |
the list containing distance and angle matrices as returned
by |
upper |
a logical determining whether to plot the upper or lower triangle of angles. Defaults to TRUE which is the upper triangle and returns the angle from the second ellipse to the first by centering on the first centroid. |
do.plot |
a logical indicating whether plotting should be done or not. Defaults to TRUE. |
... |
additional arguments to pass onwards, not currently implemented. |
A nice plot. You can get the corresponding matrices used to generate the plots if you ask for it nicely: the_data <- plotCentroidVectors(centroids)
A 5 column matrix containing isotopic estimates for 251 geese collected at 8 different time points. The first column indicates the time point group, the second and third are d15N (Nitrogen) and d13C (Carbon) isotopic values for the Geese plasma, the third and fourth are d15N and d13C values for the Geese cells. Note that these are raw values; they have not undergone fractionation correction.
data(allgroups)data(allgroups)
A data frame with 251 observations on the following 5 variables.
GroupGroup number / time point
d15NPld15N plasma
d13CPld13C plasma
d15NCed15N cells
d13CCed13C cells
#see siarmenu() and option 9 for a demo using part of this data#see siarmenu() and option 9 for a demo using part of this data
This function loops over the posterior distribution of group means within each community and generates the corresponding Bayesian estimate of the 6 Layman metrics.
bayesianLayman(mu.post)bayesianLayman(mu.post)
mu.post |
a list of length n.communities, with each list element
containing the estimated means of the groups comprising that community. The
typical workflow to generate mu.post follows. The
Bayesian ellipses are fitted using |
A list of length n.communities, with each element containing a matrix of 6 columns, each representing the Bayesian posterior distribution of the 6 Layman metrics for each of the posterior draws recorded by the fitting process (i.e. which determines the number of rows in this matrix).
This function loops over the posterior distribution of the means and covariances matrices of two specified groups.
bayesianOverlap( ellipse1, ellipse2, ellipses.posterior, draws = 10, p.interval = 0.95, n = 100, do.plot = FALSE )bayesianOverlap( ellipse1, ellipse2, ellipses.posterior, draws = 10, p.interval = 0.95, n = 100, do.plot = FALSE )
ellipse1 |
character code of the form |
ellipse2 |
same as |
ellipses.posterior |
a list of posterior means and covariances fitted
using |
draws |
an integer specifying how many of the posterior draws are to be
used to estimate the posterior overlap. Defaults to |
p.interval |
the prediction interval used to scale the ellipse as per
|
n |
the number of points on the edge of the ellipse used to define it.
Defaults to |
do.plot |
logical switch to determine whether the corresponding ellipses
should be plotted or not. A use-case would be in conjunction with a low
numbered |
A data.frame comprising three columns: the area of overlap, the area
of the first ellipse and the area of the second ellipse and as many rows as
specified by draws.
This function loops over each community, determines the centre of mass
(centroid) of each of the groups comprising the community using the basic
base::mean() function independently on the marginal x and y vectors,
and calculates the corresponding 6 Layman metrics based on these points.
communityMetricsML(siber)communityMetricsML(siber)
siber |
a siber object as created by |
A 6 x m matrix of the 6 Layman metrics of dX_range, dY_range, TA, CD, MNND and SDNND in rows, for each community by column
data(demo.siber.data) my.siber.data <- createSiberObject(demo.siber.data) communityMetricsML(my.siber.data)data(demo.siber.data) my.siber.data <- createSiberObject(demo.siber.data) communityMetricsML(my.siber.data)
A dataset of concentration dependent corrections for 4 food sources of brent geese. Intended for use in a Stable Isotope Mixing Model.
data(concdepdemo)data(concdepdemo)
A 5 column, 4 row data.frame object containing the concentration dependence data for the geese1demo and geese2demo datasets. The first column Source is a factor determining the name of the source. The second and third columns are the mean d13C and mean d15N concentration values for each source respectively. Columns 3 and 5 are the standard deviations but these are not currently implemented in either simmr or MixSIAR stable isotope mixing models. Note that the order of the isotope data has been swapped since siar in order to present d13C as the first isotope and hence on the x-axis by default.
Rich Inger
A dataset of estimated trophic discrimination factors for brent geese. The data assume the same TDF for each food source. Intended for use in a Stable Isotope Mixing Model.
data(correctionsdemo)data(correctionsdemo)
A 5 column, 4 row data.frame object containing the trophic discrimination factors for brent geese consumers relative to 4 of their food sources (in Ireland). The first column Source is a factor determining the name of the source. The second and third columns are the mean d13C and mean d15N TDF values for each source respectively. Columns 3 and 5 are the standard deviations of the d13C and d15N TDF values respectively. Note that the order of the isotope data has been swapped since siar in order to present d13C as the first isotope and hence on the x-axis by default.
Rich Inger
This function takes raw isotope data and creates a SIBER object which contains information in a structured manner that enables other functions to loop over groups and communities, fit Bayesian ellipses, and afterwards, generate various plots, and additional analyses on the posterior distributions.
data.in |
Specified In a basic R data.frame or matrix comprising 4 columns. The first two of which are typically isotope tracers, then the third is a column that indicates the group membership, and the fourth column indicates the community membership of an observation. Communities labels should be entered as sequential numbers. As of v2.0.1 group labels can be entered as strings and/or numbers and need not be sequential. |
A siber list object, that contains data that helps with various model fitting and plotting.
original.data The original data as
passed into this function
iso.summary The max, min, mean and
median of the isotope data useful for plotting
sample.sizes The
number of observations tabulated by group and community
raw.data A list object of length equal to the number of communities
data(demo.siber.data) my.siber.data <- createSiberObject(demo.siber.data) names(my.siber.data)data(demo.siber.data) my.siber.data <- createSiberObject(demo.siber.data) names(my.siber.data)
Data for two communities, created by generateSiberData() used
to generate the vignette and illustrates the main functionality of SIBER.
data(demo.siber.data)data(demo.siber.data)
An object of class "data.frame" containing four variables.
The first and second variables are generic isotopes called iso1
and iso2. The third variable group identifies which group
within a community an observation belongs. Group are required to be
integers in sequential order starting at 1 and numbering should
restart within each community. The fourth variable community
identifies which community an observation belongs, and again is required
to be an integer in sequential order staring at 1.
Andrew Jackson
Data for two communities, created by generateSiberData() used
to generate the vignette and illustrates the main functionality of SIBER.
data(demo.siber.data.2)data(demo.siber.data.2)
An object of class "data.frame" containing four variables.
The first and second variables are generic isotopes called iso1
and iso2. The third variable group identifies which group
within a community an observation belongs. Group are required to be
integers in sequential order starting at 1 and numbering should
restart within each community. The fourth variable community
identifies which community an observation belongs, and again is required
to be an integer in sequential order staring at 1.
Andrew Jackson
Back-transforms a bivariate siber ellipse fitted to z-scored data to the original location and scale. Not intended for direct call by users.
ellipseBackTransform(jags.output, siber, idx.community, idx.group)ellipseBackTransform(jags.output, siber, idx.community, idx.group)
jags.output |
a mcmc.list object of posterior samples created by
|
siber |
a siber object as created by createSiberObject. |
idx.community |
an integer specifying which community to back-transform. |
idx.group |
an integer specifying which group to back-transform. |
A 6 x n matrix representing the back-transformed posterior
distributions of the bivariate normal distribution for a specified group
within a specified community, where n is the number of
posterior draws in the saved sample. The first four columns are the
covariance matrix Sigma in vector format. This vector converts to the
covariance matrix as matrix(v[1:4], nrow = 2, ncol = 2). The
remaining two columns are the back-transformed means.
Takes a
ellipseInOut(Z, p = 0.95, r = NULL)ellipseInOut(Z, p = 0.95, r = NULL)
Z |
the |
p |
the percentile of the ellipse to be tested. |
r |
a manually defined radius of the circle to be used. Setting |
A logical vector indicating whether the point is inside or outside the circle
Takes a vector x and transforms the points onto the same geometry of
a normalised ellipse given by the inverse of the covariance matrix
SigSqrt and the location mu.
ellipsoidTransform(x, SigSqrt, mu)ellipsoidTransform(x, SigSqrt, mu)
x |
the vector of data points to be transformed |
SigSqrt |
the inverse of the covariance matrix |
mu |
the vector of means of the ellipse |
A vector of transformed data points
siberMVN
This function extracts the posterior means from a call to
siberMVN() which can then be used to calculate Bayesian layman
metrics. This function is designed to create an array of posterior means
that is more easily interrogated for plotting and summary statistics.
extractPosteriorMeans(siber, post)extractPosteriorMeans(siber, post)
siber |
a siber object as created by |
post |
a list containing the posterior estimated parameters fitted to
each group within every community. See |
A list of length n.communities with each entry representing a
n.draws * 2 * n.groups array of rows equal to the number of posterior
samples, 2 columns representing the two means of the multivariate data and
n.groups the number of groups within the focal community.
This function contains and defines the jags model script used to fit a bivariate normal distribution to a vector of x and y data. Although not intended for direct calling by users, it presents a quick way to fit a model to a single group of data. Advanced users should be able to manipulate the contained jags model to fit more complex models using different likelihoods, such as multivariate lognormal distributions, multivariate gamma distributions etc...
fitEllipse(x, y, parms, priors, id = NULL)fitEllipse(x, y, parms, priors, id = NULL)
x |
a vector of data representing the x-axis |
y |
a vector of data representing the y-axis |
parms |
a list containing four items providing details of the
|
priors |
a list of three items specifying the priors to be passed to the jags model.
|
id |
a character string to prepend to the raw saved jags model output.
This is typically passed on from the calling function
|
A mcmc.list object of posterior samples created by jags.
x <- stats::rnorm(50) y <- stats::rnorm(50) parms <- list() parms$n.iter <- 2 * 10^3 parms$n.burnin <- 500 parms$n.thin <- 2 parms$n.chains <- 2 priors <- list() priors$R <- 1 * diag(2) priors$k <- 2 priors$tau.mu <- 1.0E-3 fitEllipse(x, y, parms, priors)x <- stats::rnorm(50) y <- stats::rnorm(50) parms <- list() parms$n.iter <- 2 * 10^3 parms$n.burnin <- 500 parms$n.thin <- 2 parms$n.chains <- 2 priors <- list() priors$R <- 1 * diag(2) priors$k <- 2 priors$tau.mu <- 1.0E-3 fitEllipse(x, y, parms, priors)
A dataset for a single group of geese (as consumers) for two isotope tracers. Intended for use in a Stable Isotope Mixing Model.
data(geese1demo)data(geese1demo)
A 2 column, 9 row matrix containing the plasma data for the first group of geese. Columns are in the order d13C and d15N. Retained here as legacy from now defunct package siar. Note that the order of the data has been swapped since siar in order to present d13C as the first isotope and hence on the x-axis by default.
Rich Inger
A dataset for a single group of geese (as consumers) for two isotope tracers. Intended for use in a Stable Isotope Mixing Model.
data(geese2demo)data(geese2demo)
A 3 column, 251 row matrix containing the plasma data for the 8 groups of gees as consumers. Columns are in the order Group which is an integer that determines which of the 8 groups the observation belongs. The second and third columns are d13C and d15N values derived from the blood plasma for each observation. Retained here as legacy from now defunct package siar. Note that the order of the isotope data has been swapped since siar in order to present d13C as the first isotope and hence on the x-axis by default.
Rich Inger
This is a helper function that creates a sequence of points on a circle of
radius r as a resolution determined by n. It is not intended
for direct calling, and is used by the ellipse plotting function
addEllipse(). NB not an exported function.
genCircle(n = 100, r)genCircle(n = 100, r)
n |
the number of points to create around the circle. Defaults to 100. |
r |
the radius of the circle to create. |
A 2 x n matrix of x and y coordinates of points on a circle.
This function simulates data for a single community by sampling from a normal distribution with different means for each group within some specified boundaries.
generateSiberCommunity( n.groups = 3, community.id = 1, n.obs = 30, mu.range = c(-1, 1, -1, 1), wishSigmaScale = 1 )generateSiberCommunity( n.groups = 3, community.id = 1, n.obs = 30, mu.range = c(-1, 1, -1, 1), wishSigmaScale = 1 )
n.groups |
the an integer specifying the number of groups to simulate. Defaults to 3. |
community.id |
an integer identifying the community's ID number. Defaults to 1. |
n.obs |
the number of observations to draw per group. |
mu.range |
a vector of length 4, specifying the mix and max x and y
values to sample means from. Group means are sampled from a uniform
distribution within this range. The first two entries are the min and max of
the x-axis, and the second two the min and max of the y-axis. Defaults to
|
wishSigmaScale |
is a simple multiplier for the call to
|
A data.frame object comprising a column of x and y data, a group identifying column and a community identifying column, all of which are numeric.
This function simulates data for a specified number of communities. It is a
wrapper function for generateSiberCommunity().
generateSiberData( n.groups = 3, n.communities = 2, n.obs = 30, mu.range = c(-1, 1, -1, 1), wishSigmaScale = 1 )generateSiberData( n.groups = 3, n.communities = 2, n.obs = 30, mu.range = c(-1, 1, -1, 1), wishSigmaScale = 1 )
n.groups |
the an integer specifying the number of groups per community to simulate. Defaults to 3. |
n.communities |
the number of communities to simulate data for. Defaults to 2. |
n.obs |
the number of observations to draw per group. |
mu.range |
a vector of length 4, specifying the mix and max x and y
values to sample means from. Group means are sampled from a uniform
distribution within this range. The first two entries are the min and max
of the x-axis, and the second two the min and max of the y-axis. Defaults
to |
wishSigmaScale |
is a simple multiplier for the call to
|
A data.frame object comprising a column of x and y data, a group identifying column and a community identifying column, all of which are numeric.
generateSiberData()generateSiberData()
This function simulates data for a single group by sampling from a normal distribution with different means for each group within some specified boundaries.
generateSiberGroup(mu.range = c(-1, 1, -1, 1), n.obs = 30, wishSigmaScale = 1)generateSiberGroup(mu.range = c(-1, 1, -1, 1), n.obs = 30, wishSigmaScale = 1)
mu.range |
a vector of length 4, specifying the mix and max x and y values to sample means from. Group means are sampled from a uniform distribution within this range. The first two entries are the min and max of the x-axis, and the second two the min and max of the y-axis. |
n.obs |
the number of observations to draw per group. Defaults to 30. |
wishSigmaScale |
is a simple multiplier for the call to
|
A data.frame object comprising a column of x and y data, a group identifying column and a community identifying column, all of which are numeric.
# generateSiberGroup()# generateSiberGroup()
This function loops over each group within each community and calculates the convex hull total area, Standard Ellipse Area (SEA) and its corresponding small sample size corrected version SEAc based on the maximum likelihood estimates of the means and covariance matrices of each group.
groupMetricsML(siber)groupMetricsML(siber)
siber |
a siber object as created by createSiberObject. |
A 3 x m matrix of the 6 Layman metrics of dX_range, dY_range, TA, CD, MNND and SDNND in rows, where each column is a different group nested within a community.
data(demo.siber.data) my.siber.data <- createSiberObject(demo.siber.data) groupMetricsML(my.siber.data)data(demo.siber.data) my.siber.data <- createSiberObject(demo.siber.data) groupMetricsML(my.siber.data)
Given the coordinates of a convex hull (i.e. a polygon), this function
calculates its area. Not intended for direct use outside of
siberConvexhull().
hullArea(x, y)hullArea(x, y)
x |
a vector of x-axis data |
y |
a vector of y-axis data |
a scalar representing the area of the convex hull in units of
x * y; i.e. most commonly in permille squared for isotope data.
This function packs a punch and makes a pretty figure.
kapow(cd = 7, ng = 25, n = 50, sc = 10, do.plot = TRUE)kapow(cd = 7, ng = 25, n = 50, sc = 10, do.plot = TRUE)
cd |
sets the random seed to this |
ng |
the number of ellipses to draw |
n |
the number of data points to simulate per group, but never displayed |
sc |
the scaling factor the rwishart sigma called by
|
do.plot |
a logical indicating whether the plot should be printed (defaults to TRUE). |
A ggplot object
This function takes two x and y vectors, and calculates the corresponding
6 Layman metrics based on these points. Note that for generality, the
original metrics of dC_range and dN_range have been renamed dX_range and
dY_range respectively. These modified names represent the x and y axes in
terms of the order in which the data have been entered, and relate typically
to how one plots the data. These x and y vectors could represent the means
of the group members comprising a community as is preferred under the SIBER
model framework. However, one could use them to calculate the point estimates
of the 6 Layman metrics for an entire group of data. In fact, you are free
to pass this function any set of x and y data you wish.
laymanMetrics(x, y)laymanMetrics(x, y)
x |
a vector of locations in the x-axis direction. |
y |
a vector of locations in the y-axis direction. |
A vector of the 6 Layman metrics of dX_range, dY_range, TA, CD, MNND and SDNND
x <- stats::runif(10) y <- stats::runif(10) laymanMetrics(x, y)x <- stats::runif(10) y <- stats::runif(10) laymanMetrics(x, y)
This function uses the ML estimated means and covariances matrices of two specified groups to calculate the area of overlap.
maxLikOverlap( ellipse1, ellipse2, siber.object, p.interval = 0.95, n = 100, do.plot = FALSE )maxLikOverlap( ellipse1, ellipse2, siber.object, p.interval = 0.95, n = 100, do.plot = FALSE )
ellipse1 |
character code of the form |
ellipse2 |
same as |
siber.object |
an object created by |
p.interval |
the prediction interval used to scale the ellipse as per
|
n |
the number of points on the edge of the ellipse used to define it.
Defaults to |
do.plot |
logical switch to determine whether the corresponding ellipses
should be plotted or not. A use-case would be in conjunction with a low
numbered |
A vector comprising three columns: the area of overlap, the area of
the first ellipse and the area of the second ellipse and as many rows as
specified by draws.
# load in the included demonstration dataset data("demo.siber.data") siber.example <- createSiberObject(demo.siber.data) # The first ellipse is referenced using a character string representation # where in "x.y", "x" is the community, and "y" is the group within that # community. ellipse1 <- "1.2" # Ellipse two is similarly defined: community 1, group3 ellipse2 <- "1.3" # the overlap betweeen the corresponding 95% prediction ellipses is given by: ellipse95.overlap <- maxLikOverlap(ellipse1, ellipse2, siber.example, p.interval = 0.95, n = 100)# load in the included demonstration dataset data("demo.siber.data") siber.example <- createSiberObject(demo.siber.data) # The first ellipse is referenced using a character string representation # where in "x.y", "x" is the community, and "y" is the group within that # community. ellipse1 <- "1.2" # Ellipse two is similarly defined: community 1, group3 ellipse2 <- "1.3" # the overlap betweeen the corresponding 95% prediction ellipses is given by: ellipse95.overlap <- maxLikOverlap(ellipse1, ellipse2, siber.example, p.interval = 0.95, n = 100)
A dataset of multiple isotopes per individual mongooses nested within packs where the goal is to understand isotopic niche occupancy of individuals with respect to their own pack.
data(mongoose)data(mongoose)
A 4 column, 783 row data.frame object containing unique individual mongoose identifiers in the first column "indiv.id"; an integer identifier for the pack to which each individual belongs in "pack"; Delta 13 Carbon values "c13; and Delta 15 Nitrogen values in "n15". See the paper Sheppard et al 2018 doi:10.1111/ele.12933 for more details, although N.B. the data here are provided as an example, not as a reproducible analysis of that paper.
Harry Marshall
This function loops over each community and plots the convex hull based on the centres of each of the groups that make up the community. See the demonstration scripts for example implementation.
plotCommunityHulls( siber, plot.args = list(col = 1, lty = 2), iso.order = c(1, 2), ... )plotCommunityHulls( siber, plot.args = list(col = 1, lty = 2), iso.order = c(1, 2), ... )
siber |
a siber object as created by createSiberObject.R |
plot.args |
a list of plotting arguments with the following suggested,
but non-exhaustive inputs. Additional plotting arguments for passing to the
internal call to
|
iso.order |
a vector of length 2, either c(1,2) or c(2,1). The order determines which of the columns of raw data are plotted on the x (1) or y (2) axis. N.B. this will be deprecated in a future release, and plotting order will be achieved at point of data-entry. |
... |
additional arguments for passing to |
Convex hulls, drawn as lines on an existing figure.
This function loops over each community and group within, and plots an ellipse around the data. See demonstration scripts for more examples.
plotGroupEllipses(siber, plot.args = list(), iso.order = c(1, 2), ...)plotGroupEllipses(siber, plot.args = list(), iso.order = c(1, 2), ...)
siber |
a siber object as created by createSiberObject |
plot.args |
a list of plotting arguments for passing to
|
iso.order |
a vector of length 2, either |
... |
additional arguments to be passed to |
Ellipses, drawn as lines on an existing figure.
This function loops over each community and group within, and plots a convex hull around the data. N.B. use of convex hulls to compare isotopic niche width among groups within or between communities is not recommended owing to strong sample size bias. Use of ellipse area is recommended instead. This feature is provided for illustrative purposes only, and because some people have expressed a desire for this feature for figure generation. See demonstration scripts for more examples.
plotGroupHulls(siber, plot.args = NULL, iso.order = c(1, 2), ...)plotGroupHulls(siber, plot.args = NULL, iso.order = c(1, 2), ...)
siber |
a siber object as created by createSiberObject |
plot.args |
a list of plotting arguments for passing to
|
iso.order |
a vector of length 2, either |
... |
additional arguments to be passed to |
A series of convex hulls added to an existing plot.
This function takes a SIBER object as created by
createSiberObject, and loops over communities and their groups,
creating a scatterplot, and adding ellipses and hulls as desired. Ellipses can be
added to groups, while convex hulls can be added at both the group and
community level (the former for illustrative purposes only, with no
analytical tools in SIBER to fit Bayesian hulls to individual groups. This is
not mathematically possible in a Bayesian framework.).
plotSiberObject( siber, iso.order = c(1, 2), ax.pad = 1, hulls = TRUE, community.hulls.args = NULL, ellipses = TRUE, group.ellipses.args = NULL, group.hulls = FALSE, group.hulls.args = NULL, bty = "L", xlab = "Isotope 1", ylab = "Isotope 2", las = 1, x.limits = NULL, y.limits = NULL, points.order = 1:25, ... )plotSiberObject( siber, iso.order = c(1, 2), ax.pad = 1, hulls = TRUE, community.hulls.args = NULL, ellipses = TRUE, group.ellipses.args = NULL, group.hulls = FALSE, group.hulls.args = NULL, bty = "L", xlab = "Isotope 1", ylab = "Isotope 2", las = 1, x.limits = NULL, y.limits = NULL, points.order = 1:25, ... )
siber |
a siber object as created by |
iso.order |
a vector of length 2, either |
ax.pad |
a padding amount to apply to the x-axis either side of the extremes of the data. Defaults to 1. |
hulls |
a logical defaulting to |
community.hulls.args |
a list of plotting arguments to pass to
|
ellipses |
a logical defaulting to TRUE determining whether or not an ellipse should be drawn around each group within each community. |
group.ellipses.args |
a list of plotting arguments to pass to
|
group.hulls |
a logical defaulting to FALSE determining whether or not convex hulls should be drawn around each group within each community. |
group.hulls.args |
a list of plotting options to pass to
|
bty |
a string specifying the box type for the plot. See
|
xlab |
a string for the x-axis label. |
ylab |
a string for the y-axis label. |
las |
a scalar determining the rotation of the y-axis labels. Defaults
to horizontal with |
x.limits |
allows you to specify a two-element vector of lower and upper x-axis limits. Specifying this argument over-rides the automatic plotting and ax.pad option. Defaults to NULL. |
y.limits |
allows you to specify a two-element vector of lower and upper y-axis limits. Specifying this argument over-rides the automatic plotting and ax.pad option. Defaults to NULL. |
points.order |
a vector of integers specifying the order of point types
to use. See |
... |
additional arguments to be passed to |
An isotope scatterplot.
Takes a i x d matrix of points where d is the dimension of the
space considered, and i is the number of points and returns
TRUE or FALSE for whether each point is inside or outside a
d-dimensional ellipsoid defined by a covariance matrix Sigma and
vector of means mu.
pointsToEllipsoid(X, Sigma, mu)pointsToEllipsoid(X, Sigma, mu)
X |
the |
Sigma |
the |
mu |
the vector of means of the ellipse of length |
A matrix of transformed data points corresponding to X
X <- matrix(runif(200,-2.5, 2.5), ncol = 2, nrow = 100) SIG <- matrix(c(1,0,0,1), ncol = 2, nrow = 2) mu <- c(0, 0) Z <- pointsToEllipsoid(X, SIG, mu) test <- ellipseInOut(Z, p = 0.95) plot(X, col = test + 1, xlim = c(-3, 3), ylim = c(-3, 3), asp = 1) addEllipse(mu, SIG, p.interval = 0.95)X <- matrix(runif(200,-2.5, 2.5), ncol = 2, nrow = 100) SIG <- matrix(c(1,0,0,1), ncol = 2, nrow = 2) mu <- c(0, 0) Z <- pointsToEllipsoid(X, SIG, mu) test <- ellipseInOut(Z, p = 0.95) plot(X, col = test + 1, xlim = c(-3, 3), ylim = c(-3, 3), asp = 1) addEllipse(mu, SIG, p.interval = 0.95)
This function loops over each posterior draw of a single group's Bayesian
bivariate ellipse and calculates the Standard Ellipse Area (SEA) for each
draw, thereby generating a distribution of SEA estimates. Not intended for
direct calling outside of siberEllipses().
posteriorSEA(post)posteriorSEA(post)
post |
a matrix of posterior covariance matrices and mean estimates for
a bivariate ellipse. In SIBER, this is typically one list element of the
object returned by |
A vector of posterior Bayesian Standard Ellipse Areas (SEA_B)
This function loops over each group within each community and calculates the vector in polar form between the estimated centroids of each ellipse to each other ellipse.
siberCentroids(corrected.posteriors)siberCentroids(corrected.posteriors)
corrected.posteriors |
the Bayesian ellipses as returned by
|
A list containing two arrays, one r contains the pairwise
distances between ellipse centroids in as the first two dimensions, with
the third dimension containing the same for each posterior draw defining
the ellipse. The second array theta has the same structure and
contains the angle in radians (from 0 to 2*pi) between the pairs. A third
object labels refers to which community.group combination is in
each of the first two dimensions of the arrays.
This function calculates the area of the convex hull describing a set of bivariate points, and returns other information useful for plotting the hull.
siberConvexhull(x, y)siberConvexhull(x, y)
x |
a vector of x-axis data |
y |
a vector of y-axis data |
A list of length four comprising:
TA the area of the convex hull.
hullX the x-coordinates of the points describing the convex hull.
hullY the y-coordinates of the points describing the convex hull.
ind the indices of the original data in x and y that
form the boundaries of the convex hull.
x <- stats::rnorm(15) y <- stats::rnorm(15) siberConvexhull(x, y)x <- stats::rnorm(15) y <- stats::rnorm(15) siberConvexhull(x, y)
hdr.boxplot
This function is essentially hdrcde::hdr.boxplot() but it more
easily works with matrices of data, where each column is a different variable
of interest. It has some limitations though....
siberDensityPlot( dat, probs = c(95, 75, 50), xlab = "Group", ylab = "Value", xticklabels = NULL, yticklabels = NULL, clr = matrix(rep(grDevices::gray((9:1)/10), ncol(dat)), nrow = 9, ncol = ncol(dat)), scl = 1, xspc = 0.5, prn = F, ct = "mode", ylims = NULL, lbound = -Inf, ubound = Inf, main = "", ylab.line = 2, ... )siberDensityPlot( dat, probs = c(95, 75, 50), xlab = "Group", ylab = "Value", xticklabels = NULL, yticklabels = NULL, clr = matrix(rep(grDevices::gray((9:1)/10), ncol(dat)), nrow = 9, ncol = ncol(dat)), scl = 1, xspc = 0.5, prn = F, ct = "mode", ylims = NULL, lbound = -Inf, ubound = Inf, main = "", ylab.line = 2, ... )
dat |
a matrix of data for which density region boxplots will be constructed and plotted for each column. |
probs |
a vector of credible intervals to represent as box edges.
Defaults to |
xlab |
a string for the x-axis label. Defaults to |
ylab |
a string of the y-axis label. Defaults to '"Value". |
xticklabels |
a vector of strings to override the x-axis tick labels. |
yticklabels |
a vector of strings to override the y-axis tick labels. |
clr |
a matrix of colours to use for shading each of the box regions.
Defaults to greyscale |
scl |
a scalar multiplier to scale the box widths. Defaults to 1. |
xspc |
a scalar determining the amount of spacing between each box. Defaults to 0.5. |
prn |
a logical value determining whether summary statistics of each
column should be printed to screen |
ct |
a string of either |
ylims |
a vector of length two, specifying the lower and upper limits
for the y-axis. Defaults to |
lbound |
a lower boundary to specify on the distribution to avoid the
density kernel estimating values beyond that which can be expected a
priori. Useful for example when plotting dietary proportions which must lie
in the interval |
ubound |
an upper boundary to specify on the distribution to avoid the
density kernel estimating values beyond that which can be expected a
priori. Useful for example when plotting dietary proportions which must lie
in the interval |
main |
a title for the figure. Defaults to blank. |
ylab.line |
a postive scalar indicating the line spacing for rendering
the y-axis label. This is included as using the permille symbol has a
tendency to push the axis label off the plotting window margins. See the
|
... |
further graphical parameters for passing to
|
A new figure window.
: This function will not currently recognise and plot
multimodal distributions, unlike hdrcde::hdr.boxplot(). You
should take care, and plot basic histograms of each variable (column in the
object you are passing) and check that they are
indeed unimodal as expected.
# A basic default greyscale density plot Y <- matrix(stats::rnorm(1000), 250, 4) siberDensityPlot(Y) # A more colourful example my_clrs <- matrix(c("lightblue", "blue", "darkblue", "red1", "red3", "red4", "yellow1", "yellow3", "yellow4", "turquoise", "turquoise3", "turquoise4"), nrow = 3, ncol = 4) siberDensityPlot(Y, clr = my_clrs)# A basic default greyscale density plot Y <- matrix(stats::rnorm(1000), 250, 4) siberDensityPlot(Y) # A more colourful example my_clrs <- matrix(c("lightblue", "blue", "darkblue", "red1", "red3", "red4", "yellow1", "yellow3", "yellow4", "turquoise", "turquoise3", "turquoise4"), nrow = 3, ncol = 4) siberDensityPlot(Y, clr = my_clrs)
This function loops over each group within each community and calculates the posterior distribution describing the corresponding Standard Ellipse Area.
siberEllipses(corrected.posteriors)siberEllipses(corrected.posteriors)
corrected.posteriors |
the Bayesian ellipses as returned by
|
A matrix of with each column containing the posterior estimates of the SEA.
Intended to calculate the area of an ellipse as a proportion of a group of ellipses represented by their union, i.e. the total area encompassed by all ellipses superimposed.
siberKapow( dtf, isoNames = c("iso1", "iso2"), group = "group", pEll = stats::pchisq(1, df = 2) )siberKapow( dtf, isoNames = c("iso1", "iso2"), group = "group", pEll = stats::pchisq(1, df = 2) )
dtf |
a data.frame object comprising bivariate data as a requirement, and possibly other variables too but these are currently ignored. |
isoNames |
a character vector of length 2 providing the names of the variables containing the x and y data respectively. |
group |
a character vector of length 1 providing the name of the grouping variable on which to calculate the KAPOW ellipse. |
pEll |
the probability ellipse to draw for each group. Defaults to the
Standard Ellipse with |
an object of class spatstat.geom::owin containing the numerically calculated
ellipses and their union along with the raw ellipse boundaries in both raw
and spatstat.geom::owin format.
This function loops over each community and then loops over each group member, fitting a Bayesian multivariate (bivariate in this case) normal distribution to each group of data. Not intended for direct calling by users.
siberMVN(siber, parms, priors)siberMVN(siber, parms, priors)
siber |
a siber object as created by |
parms |
a list containing four items providing details of the
|
priors |
a list of three items specifying the priors to be passed to the jags model.
|
A list of length equal to the total number of groups in all
communities. Each entry is named 1.1 1.2... 2.1.. with the first number
designating the community, and the second number the group within that
community. So, 2.3 would be the third group within the second community.
Each list entry is a 6 x n matrix representing the back-transformed posterior
distributions of the bivariate normal distribution, where n is the number of
posterior draws in the saved sample. The first two columns are the back-
transformed means, and the remaining four columns are the covariance matrix
Sigma in vector format. This vector converts to the covariance matrix as
matrix(v[1:4], nrow = 2, ncol = 2).
This function takes a covariance 2x2 matrix Sigma and returns various metrics relating to the corresponding Standard Ellipse. The function is limited to the 2-dimensional case, as many of the ancillary summary statistics are not defined for higher dimensions (e.g. eccentricity).
sigmaSEA(sigma)sigmaSEA(sigma)
sigma |
a 2x2 covariance ellipse. |
A list comprising the following metrics for summarising the Standard Ellipse
SEA the Standard Ellipse Area (not sample size corrected).
eccentricity a measure of the elongation of the ellipse.
a the length of the semi-major axis.
b the length of the semi-minor axis.
This function is currently based on the eigenvalue and eigenvector approach which is more flexible for higher dimensional problems method for calculating the standard ellipse, and replaces the parametric method used previously in siar and siber.
# A perfect circle sigma <- matrix( c(1, 0, 0, 1), 2, 2) sigmaSEA(sigma)# A perfect circle sigma <- matrix( c(1, 0, 0, 1), 2, 2) sigmaSEA(sigma)
A dataset of isotope observations on 4 food sources of brent geese comprising their mean and standard deviations. Intended for use in a Stable Isotope Mixing Model.
data(sourcesdemo)data(sourcesdemo)
A 5 column, 4 row data.frame object containing 4 different plants and their measurements on 2 different isotopes. The first column Sources is a factor determining the name of the source. The second and third columns are the mean d13C and mean d15N values for each source respectively. Columns 3 and 5 are the standard deviations of the d13C and d15N values respectively. Note that the order of the isotope data has been swapped since siar in order to present d13C as the first isotope and hence on the x-axis by default.
Rich Inger
Plots the posterior densities
specificCentroidVectors(centroids, do.these, upper = TRUE, do.plot = TRUE, ...)specificCentroidVectors(centroids, do.these, upper = TRUE, do.plot = TRUE, ...)
centroids |
the list containing distance and angle matrices as returned
by |
do.these |
a character vector of the pattern used to find paired matches in the matrix of all comparisons. Usually the group names within any of the communities. |
upper |
a logical determining whether to plot the upper or lower triangle of angles. Defaults to TRUE which is the upper triangle and returns the angle from the second ellipse to the first by centering on the first centroid. |
do.plot |
a logical indicating whether plotting should be done or not. Defaults to TRUE. |
... |
additional arguments to pass onwards, not currently implemented. |
A nice plot. You can get the corresponding matrices used to generate the plots if you ask for it nicely: thedata <- plotCentroidVectors(centroids)