| Title: | Spatially Explicit Capture-Recapture by Inverse Prediction |
|---|---|
| Description: | Estimates the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. Models incorporating distance-dependent detection are fitted by simulation and inverse prediction as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>. |
| Authors: | Murray Efford [aut, cre]
|
| Maintainer: | Murray Efford <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.4.1 |
| Built: | 2024-11-12 06:58:28 UTC |
| Source: | CRAN |
Functions to estimate the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. ipsecr addresses ‘difficult’ models that strictly cannot be fitted by maximum likelihood in the package secr (Efford 2022). The classic example concerns discrete-time data from single-catch traps.
| Package: | ipsecr |
| Type: | Package |
| Version: | 1.4.1 |
| Date: | 2024-01-15 |
| License: | GNU General Public License Version 2 or later |
Spatially explicit capture–recapture is a set of methods for studying marked animals distributed in space. Data comprise the locations of detectors (described in an object of class ‘traps’), and the detection histories of individually marked animals. Individual histories are stored in an object of class ‘capthist’ that includes the relevant ‘traps’ object.
Models for population density (animals per hectare) and detection are defined in ipsecr using symbolic formula notation. The set of possible models overlaps with secr (some models for varying detection parameters are excluded, e.g., ~t, ~b). Density models may include spatial trend. Habitat is distinguished from nonhabitat with an object of class ‘mask’.
Models are fitted in ipsecr by simulation and inverse prediction
(Efford 2004, 2023). A model fitted with ipsecr.fit is an object
of class ipsecr. Generic methods (plot, print, summary, etc.) are
provided.
A link at the bottom of each help page takes you to the help index. The vignette includes worked examples.
The analyses in ipsecr extend those available in the software Density (see www.otago.ac.nz/density/ for the most recent version of Density). Help is available on the ‘DENSITY | secr’ forum at www.phidot.org and the Google group secrgroup. Feedback on the software is also welcome, including suggestions for additional documentation or new features consistent with the overall design.
‘Inverse prediction’ uses methods from multivariate calibration (Brown
1982). The goal is to estimate population density (D) and the parameters
of a detection function (usually g0 or lambda0 and sigma) by ‘matching’ statistics
from proxyfn(capthist) (the target vector) to statistics
from simulations of a 2-D population using the postulated detection
model. Statistics (see Note) are defined by the proxy function,
which should return a vector equal in length to the number of parameters
(default np = 3). Simulations of the 2-D population use either internal C++ code or sim.popn. The simulated 2-D distribution of animals is Poisson by default.
The simulated population is sampled with internal C++ code, sim.capthist, or a user-specified function. Simulations match the detector type (e.g., ‘single’ or ‘multi’) and detector layout specified in traps(capthist), including allowance for varying effort if the layout has a usage attribute.
Simulations are usually conducted on a factorial experimental design in parameter space - i.e. at the vertices of a cuboid ‘box’ centred on the working values of the parameters, plus an optional number of centre points.
A multivariate linear model is fitted to predict each vector of simulated proxies from the known parameter values. Simulations are performed at each design point until a specified precision is reached, up to a user-specified maximum number.
Once a model with sufficient precision has been obtained, a new working vector of parameter estimates is ‘predicted’ by inverting the linear model and applying it to the target vector. A working vector is accepted as the final estimate when it lies within the box; this reduces the bias from using a linear approximation to extrapolate a nonlinear function. If the working vector lies outside the box then a new design is centred on value for each parameter in the working vector.
Once a final estimate is accepted, further simulations are conducted to estimate the variance-covariance matrix. These also provide a parametric bootstrap sample to evaluate possible bias.
See Efford et al. (2004) for an early description of the method, and Efford et al. (2005) for an application.
If not provided, the starting values are determined automatically
with the **secr** function makeStart.
Linear measurements are assumed to be in metres and density in animals
per hectare (10 000 ).
If ncores > 1 the parallel package is used to create
worker processes on multiple cores (see Parallel for more).
Murray Efford [email protected]
Brown, P. J. (1982) Multivariate calibration. Journal of the Royal Statistical Society, Series B 44, 287–321.
Efford, M. G. (2004) Density estimation in live-trapping studies. Oikos 106, 598–610.
Efford, M. G. (2022) secr: Spatially explicit capture–recapture models. R package version 4.5.8. https://CRAN.R-project.org/package=secr/
Efford, M. G. (2023) ipsecr: An R package for awkward spatial capture–recapture data. Methods in Ecology and Evolution In review.
Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation by spatially explicit capture–recapture: likelihood-based methods. In: D. L. Thompson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer. Pp. 255–269.
Efford, M. G., Dawson, D. K. and Robbins C. S. (2004) DENSITY: software for analysing capture-recapture data from passive detector arrays. Animal Biodiversity and Conservation 27, 217–228.
Efford, M. G., Warburton, B., Coleman, M. C. and Barker, R. J. (2005) A field test of two methods for density estimation. Wildlife Society Bulletin 33, 731–738.
Otis, D. L., Burnham, K. P., White, G. C. and Anderson, D. R. (1978) Statistical inference from capture data on closed animal populations. Wildlife Monographs 62.
proxy.ms
ipsecr.fit,
secr.fit,
capthist,
mask
The function ipsecr.fit allows many options. Some of these are used
infrequently and have been bundled as a single argument details
to simplify the documentation. They are described here in two groups:
tuning parameters are listed in the following table, and more exotic options
follow, listed alphabetically.
| Parameter | Default | Description |
| boxsize1 | 0.2 | scalar or vector of length np for size of design |
| boxsize2 | 0.05 | as for boxsize1; used from second box onwards |
| centre | 3 | number of centre points in simulation design |
| min.nsim | 20 | minimum number of simulations per point |
| max.nsim | 200 | maximum number of simulations per point |
| dev.max | 0.002 | tolerance for precision of points in proxy space (see below) |
| var.nsim | 2000 | number of additional simulations to estimate variance-covariance matrix |
| min.nbox | 2 | minimum number of attempts to `frame' solution |
| max.nbox | 5 | maximum number of attempts to `frame' solution |
| max.ntries | 2 | maximum number of attempts at each simulation |
dev.max defines a stopping rule: simulations are added in blocks of
details$min.nsim until a measure of precision is less than dev.max for all proxies.
If a vector of length 2, the first element applies to the first box and the second to all later boxes. The measure of precision may be the standard error on the link scale (details$boxtype = "absolute") or the coefficient of variation (details$boxtype = "relative").
binomN (default 0 = Poisson) integer code for distribution of counts.
boxtype (default "absolute") switches between specifying box size as additive on the transformed parameter scale ("absolute") and relative on the transformed parameter scale ("relative").
CHmethod (default "internal") chooses between internal C++ code, the secr function sim.capthist, and a user-provided R function with arguments "traps", "popn", "detectfn", "detectpar", and "noccasions". See also popmethod.
contrasts (default NULL) bmay be used to specify the coding of factor predictors. The value should be suitable for the 'contrasts.arg' argument of model.matrix. See ‘Trend across sessions’ in secr-multisession.pdf for an example.
debug (default FALSE) is used only for debugging. In ordinary use it should not be
changed from the default.
distribution (default "poisson") specifies the distribution of the number of
individuals detected ; this may be conditional on the number in the
masked area ("binomial") or unconditional ("poisson").
distribution affects the sampling variance of the estimated
density. The component ‘distribution’ may also
take a numeric value larger than nrow(capthist), rather than "binomial"
or "poisson".
extraparam is a list of starting values for extra 'real' parameters that may be needed for some user-specified models. See the vignette for explanation and an example.
factorial (default "full") chooses between "full" or "fractional" design. Fractional requires the package **FrF2** (Groemping 2014).
forkonunix (default TRUE) switches the cluster type generated by makeCluster between FORK and PSOCK.
FrF2args (default NULL) provides a list of arguments defining a fractional design.
ignorenontarget (default FALSE) may be used to ignore
non-target information (the capthist attribute 'nontarget'). The default is to model non-target information if it is present.
ignoreusage (default FALSE) may be used to ignore usage (varying effort)
information in the traps component. The default is to include usage in the model.
keep.sim (default FALSE) when TRUE causes ipsecr.fit to save additional output, specifically lists (one component per box) of the simulations and parameters for each box, and the final variance simulations.
newdetector (default NULL) may be used to override the detector type of
the traps object embedded in the capthist passed to ipsecr.fit.
Nmax (default 1e4) maximum allowed population size for simulations.
nontargettype (default "exclusive") chooses among options "exclusive", "truncated", "erased, "independent", and "dependent". See vignette.
popmethod (default "internal") chooses between internal C++ code, the secr function sim.popn, and a user-provided R function with arguments 'mask', 'D' (density per cell of mask) and 'N' (number of individuals to simulate). See also CHmethod.
savecall (default TRUE) determines whether the function call is included in the output.
YonX (default TRUE) switches between regression of simulated proxy values Y on controlled parameter values X, or the reverse (which is not fully implemented).
Groemping, U. (2014). R Package FrF2 for Creating and Analyzing Fractional Factorial 2-Level Designs. Journal of Statistical Software, 56, 1–56. https://www.jstatsoft.org/article/view/v056i01.
Functions called internally by ipsecr. These are exported and may be called separately for testing.
proxy.ms(capthist, model = NULL, trapdesigndata = NULL, ...) detectionDesignData(capthist, byoccasion = FALSE, ...) proxyfn1(capthist, N.estimator = c("n", "null","zippin","jackknife"), ...) simpop(mask, D, N, details = list(), ...) simCH(traps, popn, detectfn, detparmat, noccasions, NT = NULL, details = list(), ...) rpsv(capthist) rpsvi(capthist)proxy.ms(capthist, model = NULL, trapdesigndata = NULL, ...) detectionDesignData(capthist, byoccasion = FALSE, ...) proxyfn1(capthist, N.estimator = c("n", "null","zippin","jackknife"), ...) simpop(mask, D, N, details = list(), ...) simCH(traps, popn, detectfn, detparmat, noccasions, NT = NULL, details = list(), ...) rpsv(capthist) rpsvi(capthist)
capthist |
secr capthist object |
model |
named list of model formulae (see |
trapdesigndata |
dataframe with one row for each detector and session |
... |
other arguments, mostly unused |
byoccasion |
logical; if TRUE the output rows are repeated for each occasion |
N.estimator |
character name of closed-population estimator |
mask |
secr mask object |
D |
numeric density in each mask cell |
N |
integer number of animals to simulate |
traps |
detector locations as secr traps object |
popn |
animal locations as secr popn object |
detectfn |
integer code for detection function (see detectfn) |
detparmat |
numeric matrix of detection parameter values |
noccasions |
integer number of sampling occasions |
NT |
numeric hazard of non-target interference at each detector |
details |
list with optional additional named arguments |
proxy.ms is the default proxyfn used by ipsecr.fit. When used internally by ipsecr.fit, ‘model’ and ‘trapdesigndata’ are passed automatically. The ... argument of proxy.ms may be used to pass arguments to addCovariates, especially ‘spatialdata’. Function detectionDesignData is used internally to construct design data for non-constant detection models (lambda0, sigma), used in the glm 'data' argument. The capthist argument for detectionDesignData should always be a list (wrap a single-session capthist in list()).
simpop is used by ipsecr.fit for popmethod 'internal'. It is faster and simpler than the secr function sim.popn. The details component 'distribution' is a character value that may be ‘poisson’ (default) or 'even.
simCH is used by ipsecr.fit for CHmethod 'internal'. It is faster and simpler than the secr function sim.capthist, and optionally simulates non-target interference. The argument detparmat is an individual x parameter matrix, with parameters in the order usual for detectfn.
D and NT are matrices with one column per session.
proxyfn1 is a simple proxy function included mostly for historical reasons. It updates the function of Efford (2004) by log-transforming N, using a complementary log-log transformation instead of odds for p, and using log(RPSV(capthist)) for sigma. If you're interested, look at the code.
rpsv(capthist) is equivalent to secr RPSV(capthist, CC = TRUE). rpsvi(capthist) returns a vector of individual-specific rpsv.
proxy.ms – a numeric vector of length >= 3 corresponding to proxies for a wide range of models, including multi-session density and non-target interference models.
detectionDesignData – a dataframe with one row per individual per session (byoccasion = FALSE) or one row per individual per occasion per session (byoccasion = TRUE), ordered by session, occasion and individual. Columns include x and y coordinates of the individual's centroid, session, and any individual covariates.
proxyfn1 – a numeric vector of length 3 corresponding to proxies for population size, capture probability intercept and scale of detection.
simpop – a popn object.
simCH – a single-session capthist object.
rpsv – scalar
rpsvi – vector, one element per animal
proxyfn0 was removed in version 1.2.0.
Efford, M. G. (2004) Density estimation in live-trapping studies. Oikos 106, 598–610.
proxy.ms(captdata)proxy.ms(captdata)
These functions are no longer available in ipsecr.
# Defunct in 1.2.0 (2022-08) proxyfn0()# Defunct in 1.2.0 (2022-08) proxyfn0()
proxyfn0 was removed without warning in ipsecr 1.2.0.
Use proxyfn1 or proxy.ms.
These functions will be removed from future versions of ipsecr.
No functions are deprecated at this point.
Estimate population density by simulation and inverse prediction (Efford 2004; Efford, Dawson & Robbins 2004). A restricted range of SECR models may be fitted.
ipsecr.fit(capthist, proxyfn = proxy.ms, model = list(D ~ 1, g0 ~ 1, sigma ~ 1), mask = NULL, buffer = 100, detectfn = "HN", binomN = NULL, start = NULL, link = list(), fixed = list(), timecov = NULL, sessioncov = NULL, details = list(), verify = TRUE, verbose = TRUE, ncores = NULL, seed = NULL, ...)ipsecr.fit(capthist, proxyfn = proxy.ms, model = list(D ~ 1, g0 ~ 1, sigma ~ 1), mask = NULL, buffer = 100, detectfn = "HN", binomN = NULL, start = NULL, link = list(), fixed = list(), timecov = NULL, sessioncov = NULL, details = list(), verify = TRUE, verbose = TRUE, ncores = NULL, seed = NULL, ...)
capthist |
secr capthist object including capture data and detector (trap) layout |
proxyfn |
function to compute proxy from capthist for each coefficient (beta parameter) |
model |
list with optional components each symbolically defining a linear
predictor for one real parameter using |
mask |
|
buffer |
scalar mask buffer radius in metres if |
detectfn |
integer code or character string for shape of detection function 0 = halfnormal, 1 = hazard rate etc. – see detectfn |
binomN |
integer code for distribution of counts (see Details) |
start |
vector of initial values for beta parameters, or |
link |
list with optional components corresponding to ‘real’ parameters (e.g., ‘D’, ‘g0’, ‘sigma’), each a character string in {"log", "logit", "identity", "sin"} for the link function of one real parameter |
fixed |
list with optional components corresponding to real parameters giving the scalar value to which the parameter is to be fixed |
timecov |
optional dataframe of values of time (occasion-specific) covariate(s). NOT USED |
sessioncov |
optional dataframe of values of session-specific covariate(s) |
details |
list of additional settings, to control estimation (see Details) |
verify |
logical, if TRUE the input data are checked with |
verbose |
logical, if TRUE then messages are output during execution |
ncores |
integer number of cores to use for parallel processing |
seed |
either NULL or an integer that will be used in a call to |
... |
other arguments passed to proxy function |
The vignette should be consulted for a full exposition.
ncores determines the number of worker processes in a cluster created by makeCluster (default type "FORK" on Unix platforms, otherwise "PSOCK"). If ncores = NULL this defaults to the value from setNumThreads. Simulations are distributed over worker processes using parRapply. There are substantial overheads in running multiple processes: using too many will slow down fitting. With PSOCK clusters (i.e. on Windows) fitting is very often fastest with ncores = 1.
details is used for various specialized settings listed below. These are
also described separately - see details.
| Name | Default | Description |
| boxsize1 | 0.2 | scalar or vector of length np for size of design |
| boxsize2 | 0.05 | as for boxsize1; used from second box onwards |
| boxtype | 'absolute' | `absolute' or `relative' |
| centre | 3 | number of centre points in simulation design |
| dev.max | 0.002 | tolerance for precision of points in predictor space |
| var.nsim | 2000 | number of additional simulations to estimate variance-covariance matrix |
| keep.sim | FALSE | if true then the variance simulations are saved |
| min.nsim | 20 | minimum number of simulations per point |
| max.nsim | 200 | maximum number of simulations per point |
| min.nbox | 2 | minimum number of attempts to `frame' solution |
| max.nbox | 5 | maximum number of attempts to `frame' solution |
| max.ntries | 2 | maximum number of attempts at each simulation |
| distribution | `poisson' | `poisson', `binomial' or `even' |
| binomN | 0 | integer code for distribution of counts (unused) |
| ignorenontarget | FALSE | override nontarget attribute of capthist |
| ignoreusage | FALSE | override usage in traps object of capthist |
| debug | FALSE | stop at arbitrary points in execution (varies) |
| savecall | TRUE | optionally suppress saving of call |
| newdetector | NULL | detector type that overrides detector(traps(capthist)) |
| contrasts | NULL | coding of factor predictors |
| popmethod | `internal' | `internal' or `sim.popn' or a user-provided function |
| CHmethod | `internal' | `internal' or `sim.capthist' or a user-provided function |
| factorial | `full' | `full' or `fractional' design |
| FrF2args | NULL | arguments for FrF2 when factorial = 'fractional' |
| extraparam | NULL | list of starting values for extra parameters (see vignette) |
| forkonunix | TRUE | logical choice between FORK and PSOCK cluster types (not Windows) |
An object of class 'ipsecr', a list comprising:
call |
the function call (if details$savecall) |
capthist |
input |
proxyfn |
input |
model |
input |
mask |
input |
detectfn |
input |
start |
input |
link |
input |
fixed |
input |
timecov |
input |
sessioncov |
input |
details |
input |
designD |
list of design data for density |
trapdesigndata |
list of design data for trap-specific models |
parindx |
mapping of coefficients (beta parameters) to real parameters |
vars |
names of covariates in model |
betanames |
names of coefficients |
realnames |
names of 'real' parameters |
code |
integer completion code: 1 successful, 2 target not within final box, 3 exceeded maximum simulations |
beta |
estimates of coefficients on link scale |
beta.vcov |
variance-covariance matrix of estimates |
designbeta |
vertices of final box (design points) |
sim.lm |
last lm model fit |
ip.nsim |
total number of simulations |
var.nsim.OK |
number of successful variance simulations |
simulations |
optional simulation output (see details$keep.sim) |
parameters |
optional simulation input (see details$keep.sim) |
variance.bootstrap |
dataframe summarising simulations for variance estimation |
version |
package version |
starttime |
time execution started |
proctime |
processor time (seconds) |
seed |
RNG state |
(The order and composition of the output list may change).
Efford, M. G. (2004) Density estimation in live-trapping studies. Oikos 106, 598–610.
Efford, M. G., Dawson, D. K. and Robbins C. S. (2004) DENSITY: software for analysing capture-recapture data from passive detector arrays. Animal Biodiversity and Conservation 27, 217–228.
proxy.ms,
predict.ipsecr,
summary.ipsecr
ipsecrdemo <- ipsecr.fit(captdata, ncores = 1, buffer = 100, detectfn = 14, seed = 1237)ipsecrdemo <- ipsecr.fit(captdata, ncores = 1, buffer = 100, detectfn = 14, seed = 1237)
Demonstration data from program Density are provided as a
capthist object (captdata) ready for input to ipsecr.fit.
The fitted models are objects of class ipsecr formed by
ipsecrdemo <- ipsecr.fit(captdata, ncores = 1, detectfn = 'HHN',
seed = 1237, details = list(keep.sim = TRUE))
data(ipsecrdemo)data(ipsecrdemo)
The raw data are 235 fictional captures of 76 animals over 5 occasions in 100 single-catch traps 30 metres apart on a square grid with origin at (365,365).
The fitted model uses a hazard halfnormal detection function and default values of other arguments.
| Object | Description |
| ipsecrdemo | fitted ipsecr model -- null |
Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://www.otago.ac.nz/density/.
capthist, read.capthist, secrdemo
predict(ipsecrdemo)predict(ipsecrdemo)
Internal function used to generate a dataframe containing design data for the base levels of all predictors in an secr object.
## S3 method for class 'ipsecr' makeNewData(object, all.levels = FALSE, ...)## S3 method for class 'ipsecr' makeNewData(object, all.levels = FALSE, ...)
object |
fitted ipsecr model object |
all.levels |
logical; if TRUE then all levels of factors are included |
... |
other arguments (not used) |
makeNewData is used by predict in lieu of
user-specified ‘newdata’. There is seldom any need to call the function
makeNewData directly.
A dataframe with one row for each session and group, and columns for the
predictors used by object$model.
## from previously fitted model makeNewData(ipsecrdemo)## from previously fitted model makeNewData(ipsecrdemo)
Plot detection functions using estimates of parameters in an ipsecr object.
## S3 method for class 'ipsecr' plot(x, newdata = NULL, add = FALSE, sigmatick = FALSE, rgr = FALSE, limits = FALSE, alpha = 0.05, xval = 0:200, ylim = NULL, xlab = NULL, ylab = NULL, ...)## S3 method for class 'ipsecr' plot(x, newdata = NULL, add = FALSE, sigmatick = FALSE, rgr = FALSE, limits = FALSE, alpha = 0.05, xval = 0:200, ylim = NULL, xlab = NULL, ylab = NULL, ...)
x |
an |
newdata |
dataframe of data to form estimates |
add |
logical to add curve(s) to an existing plot |
sigmatick |
logical; if TRUE the scale parameter sigma is shown by a vertical line |
rgr |
logical; if TRUE a scaled curve r.g(r) is plotted instead of g(r) |
limits |
logical; if TRUE pointwise confidence limits are drawn |
alpha |
alpha level for confidence intervals |
xval |
vector of distances at for which detection to be plotted |
ylim |
vector length 2 giving limits of y axis |
xlab |
label for x axis |
ylab |
label for y axis |
... |
arguments to pass to |
newdata is usually NULL, in which case one curve is plotted for
each session and group. Otherwise, predict.ipsecr is used to form
estimates and plot a curve for each row in newdata.
If axis labels are not provided they default to ‘Distance (m)’ and ‘Detection probability’ or ‘Detection lambda’.
Approximate confidence limits for g(r) are calculated using a numerical
first-order delta-method approximation to the standard error at each
xval. The distribution of g(r) is assumed to be normal on the logit scale for non-hazard functions (detectfn 0:13). For hazard detection functions (detectfn 14:18) the hazard is assumed (from version 3.1.1) to be distributed normally on the log scale. Limits are back-transformed to the probability scale g(r).
plot.ipsecr invisibly returns a dataframe of the plotted values (or
a list of dataframes in the case that newdata has more than one
row).
Detection functions, plot, ipsecr, detectfnplot
plot (ipsecrdemo, xval = 0:100, ylim = c(0, 0.4))plot (ipsecrdemo, xval = 0:100, ylim = c(0, 0.4))
ipsecr.fit
A 3-D depiction of the design (a box in parameter space) and the resulting simulations (in proxy space).
plot3D.IP(object, box = 1, oldplot = NULL, plotcentre = TRUE, plotfinal = FALSE, zkludge = -0.2)plot3D.IP(object, box = 1, oldplot = NULL, plotcentre = TRUE, plotfinal = FALSE, zkludge = -0.2)
object |
ipsecr object from |
box |
integer number of box to plot |
oldplot |
list containing transofrmations and plot limits from a previous execution |
plotcentre |
logical; if TRUE the centrepoint of the design box is plotted |
plotfinal |
logical; if TRUE the final estimates are plotted as a point in parameter space |
zkludge |
numeric adjustment for base value of z when plotfinal is TRUE |
The function is restricted to single-session models with 3 real parameters.
A 2-panel plot is generated, so the graphics options should allow at least 2 panels
(e.g., par(mfrow = c(1,2)).
Parameters are plotted on the link scale.
The package plot3D is used (Soetaert 2021).
Invisibly returns a list comprising
pmatparm |
pmat used by plot3D for parameter space |
pmatsim |
pmat used by plot3D for proxy space |
pr |
2-row matrix with lower and upper plot limits of each parameter |
sr |
2-row matrix with lower and upper plot limits of each simulated proxy |
Soetaert, K. (2021). plot3D: Plotting Multi-Dimensional Data. R package version 1.4. https://CRAN.R-project.org/package=plot3D
if (requireNamespace("plot3D")) { par(mfrow = c(2,2), oma = c(1,1,3,1)) # plot first box, saving projection and limits for later use oldplot <- plot3D.IP(ipsecrdemo, box = 1) # plot second box, using projections and limits from first box plot3D.IP(ipsecrdemo, box = 2, oldplot, plotfinal = TRUE, zkludge = -0.1) mtext(outer = TRUE, side = 3, line = 0.5, adj = c(0.2,0.8), cex = 1.1, c('Parameter space', 'Proxy space')) }if (requireNamespace("plot3D")) { par(mfrow = c(2,2), oma = c(1,1,3,1)) # plot first box, saving projection and limits for later use oldplot <- plot3D.IP(ipsecrdemo, box = 1) # plot second box, using projections and limits from first box plot3D.IP(ipsecrdemo, box = 2, oldplot, plotfinal = TRUE, zkludge = -0.1) mtext(outer = TRUE, side = 3, line = 0.5, adj = c(0.2,0.8), cex = 1.1, c('Parameter space', 'Proxy space')) }
As described in the vignette, each parameter is matched to a proxy value computed cheaply from the rawdata by the proxy function. This function provides a visual check on that relationship.
plotProxy(parameter = "sigma", proxyfn = proxy.ms, traps, mask, detectfn = "HHN", noccasions = 5, basepar = list(), xvals = NULL, nrepl = 100, add = FALSE, trend = TRUE, points = FALSE, boxplot = TRUE, boxplotargs = list(), link = "log", details = NULL, ...)plotProxy(parameter = "sigma", proxyfn = proxy.ms, traps, mask, detectfn = "HHN", noccasions = 5, basepar = list(), xvals = NULL, nrepl = 100, add = FALSE, trend = TRUE, points = FALSE, boxplot = TRUE, boxplotargs = list(), link = "log", details = NULL, ...)
parameter |
character parameter of interest |
proxyfn |
function to compute vector of proxy values from a capthist object |
traps |
traps object |
mask |
habitat mask object |
detectfn |
numeric or character code for detection function (see detectfn) |
noccasions |
integer number of sampling occasions |
basepar |
named list with central values of parameters |
xvals |
specified values of focal paramater to simulate (optional) |
nrepl |
integer number of simulations |
add |
logical; if TRUE any plot is added to an existing plot |
trend |
logical; if TRUE a least-squares trend line is plotted |
points |
logical; if TRUE each simulated value is plotted |
boxplot |
logical; if TRUE a boxplots is plotted for each level of the focal parameter |
boxplotargs |
list of arguments for |
link |
character link function for transforming parameter x-axis |
details |
not used |
... |
other arguments passed to plot() |
Simulation uses the internal functions simpop and simCH.
boxplotargs may be used to override or add to the arguments of boxplot.
This version of plotProxy() does not allow for interference (NT) and assumes a simple SECR model with only 3 or 4 coefficients corresponding to density D and the parameters of the detection model (lambda0 or g0, sigma and possibly z).
Matching of proxies at the level of ‘beta’ parameters may be enabled in a future version.
The simulated proxy values are returned invisibly as a matrix (nrepl x nlevels).
# try with small number of replicates trps <- traps(captdata) msk <- make.mask(trps, buffer = 100) base <- list(D = 5, lambda0 = 0.2, sigma = 25) out <- plotProxy (parameter = 'D', traps = trps, mask = msk, basepar = base, boxplotargs = list(col='orange'), nrepl = 20)# try with small number of replicates trps <- traps(captdata) msk <- make.mask(trps, buffer = 100) base <- list(D = 5, lambda0 = 0.2, sigma = 25) out <- plotProxy (parameter = 'D', traps = trps, mask = msk, basepar = base, boxplotargs = list(col='orange'), nrepl = 20)
Evaluate a spatially explicit capture–recapture model. That is, compute the ‘real’ parameters corresponding to the ‘beta’ parameters of a fitted model for arbitrary levels of any variables in the linear predictor.
## S3 method for class 'ipsecr' predict(object, newdata = NULL, type = c("response", "link"), se.fit = TRUE, alpha = 0.05, savenew = FALSE, ...)## S3 method for class 'ipsecr' predict(object, newdata = NULL, type = c("response", "link"), se.fit = TRUE, alpha = 0.05, savenew = FALSE, ...)
object |
|
newdata |
optional dataframe of values at which to evaluate model |
type |
character; type of prediction required. The default ("response") provides estimates of the ‘real’ parameters. |
se.fit |
logical for whether output should include SE and confidence intervals |
alpha |
alpha level for confidence intervals |
savenew |
logical for whether newdata should be saved |
... |
other arguments passed to |
The variables in the various linear predictors are described in
secr-models.pdf and listed for the particular model in the
vars component of object.
Optional newdata should be a dataframe with a column for each of
the variables in the model (see ‘vars’ component of object). If
newdata is missing then a dataframe is constructed automatically.
Default newdata are for a naive animal on the first occasion;
numeric covariates are set to zero and factor covariates to their base
(first) level. The argument ‘all.levels’ may be passed
to newdata; if TRUE then the default newdata includes
all factor levels.
realnames may be used to select a subset of parameters.
Standard errors for parameters on the response (real) scale are by the delta method (Lebreton et al. 1992), and confidence intervals are backtransformed from the link scale.
The value of newdata is optionally saved as an attribute.
When se.fit = FALSE, a dataframe identical to newdata except for the addition of one column for each ‘real’ parameter. Otherwise, a list with one component for each row in newdata. Each component is a dataframe with one row for each ‘real’ parameter (density, g0, sigma, b) and columns as below
| link | link function |
| estimate | estimate of real parameter |
| SE.estimate | standard error of the estimate |
| lcl | lower 100(1--alpha)% confidence limit |
| ucl | upper 100(1--alpha)% confidence limit |
When newdata has only one row, the structure of the list is
‘dissolved’ and the return value is one data frame.
For detectpar, a list with the estimated values of detection
parameters (e.g., g0 and sigma if detectfn = "halfnormal"). In the case
of multi-session data the result is a list of lists (one list per
session).
predictDsurface should be used for predicting density at many
points from a model with spatial variation. This deals automatically
with scaling of x- and y-coordinates, and is much faster than
predict.ipsecr. The resulting Dsurface object has its own plot method.
The argument ‘scaled’ was removed from both predict methods in version 2.10 as the scaleg0 and scalesigma features had been superceded by other parameterisations.
Lebreton, J.-D., Burnham, K. P., Clobert, J. and Anderson, D. R. (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs 62, 67–118.
predict (ipsecrdemo)predict (ipsecrdemo)
Print results from fitting a spatially explicit capture–recapture model or generate a list of summary values.
## S3 method for class 'ipsecr' print(x, newdata = NULL, alpha = 0.05, call = TRUE, ...) ## S3 method for class 'ipsecr' summary(object, newdata = NULL, alpha = 0.05, ...)## S3 method for class 'ipsecr' print(x, newdata = NULL, alpha = 0.05, call = TRUE, ...) ## S3 method for class 'ipsecr' summary(object, newdata = NULL, alpha = 0.05, ...)
x |
|
object |
|
newdata |
optional dataframe of values at which to evaluate model |
alpha |
alpha level |
call |
logical; if TRUE the call is printed |
... |
other arguments (not used) |
Results from print.ipsecr are potentially complex and depend upon the analysis (see
below). Optional newdata should be a dataframe with a column for
each of the variables in the model. If newdata is missing then a
dataframe is constructed automatically. Default newdata are for
a naive animal on the first occasion; numeric covariates are set to zero
and factor covariates to their base (first) level. Confidence intervals
are 100 (1 – alpha) % intervals.
| call | the function call (optional) |
| version,time | ipsecr version, date and time fitting started, and elapsed time |
| Detector type | `single', `multi', `proximity' etc. |
| Detector number | number of detectors |
| Average spacing | |
| x-range | |
| y-range | |
| New detector type | as fitted when details$newdetector specified |
| N animals | number of distinct animals detected |
| N detections | number of detections |
| N occasions | number of sampling occasions |
| Mask area | |
| Model | model formula for each `real' parameter |
| Fixed (real) | fixed real parameters |
| Detection fn | detection function type (halfnormal or hazard-rate) |
| Distribution | spatial model (details$distribution) |
| N parameters | number of parameters estimated |
| Design points | number of vertices and centre points |
| Simulations per box | total number |
| Beta parameters | coef of the fitted model, SE and confidence intervals |
| vcov | variance-covariance matrix of beta parameters |
| Real parameters | fitted (real) parameters evaluated at base levels of covariates |
The summary method constructs a list of outputs similar to those printed by the print method, but somewhat more concise and re-usable:
| versiontime | ipsecr version, and date and time fitting started |
| traps | detector summary |
| capthist | capthist summary |
| mask | mask summary |
| modeldetails | miscellaneous model characteristics |
| coef | table of fitted coefficients with CI |
| predicted | predicted values (`real' parameter estimates) |
## load & print previously fitted null (constant parameter) model print(ipsecrdemo) summary(ipsecrdemo)## load & print previously fitted null (constant parameter) model print(ipsecrdemo) summary(ipsecrdemo)
Variance-covariance matrix of beta or real parameters from fitted ipsecr model.
## S3 method for class 'ipsecr' vcov(object, realnames = NULL, newdata = NULL, byrow = FALSE, ...)## S3 method for class 'ipsecr' vcov(object, realnames = NULL, newdata = NULL, byrow = FALSE, ...)
object |
ipsecr object output from the function |
realnames |
vector of character strings for names of ‘real’ parameters |
newdata |
dataframe of predictor values |
byrow |
logical for whether to compute covariances among ‘real’ parameters for each row of new data, or among rows for each real parameter |
... |
other arguments (not used) |
By default, returns the matrix of variances and covariances among the estimated model coefficients (beta parameters).
If realnames and newdata are specified, the result is
either a matrix of variances and covariances for each ‘real’ parameter
among the points in predictor-space given by the rows of newdata
or among real parameters for each row of newdata. Failure to
specify newdata results in a list of variances only.
A matrix containing the variances and covariances among beta parameters
on the respective link scales, or a list of among-parameter variance-covariance
matrices, one for each row of newdata, or a list of among-row variance-covariance
matrices, one for each ‘real’ parameter.
vcov, ipsecr.fit, print.ipsecr
## previously fitted ipsecr model vcov(ipsecrdemo)## previously fitted ipsecr model vcov(ipsecrdemo)