fit.ns | R Documentation |
Estimates parameters for a Neyman-Scott point process by maximising the Palm likelihood. This approach was first proposed by Tanaka et al. (2008) for two-dimensional Thomas processes. Further generalisations were made by Stevenson, Borchers, and Fewster (2019) and Jones-Todd et al. (2019).
fit.ns(
points,
lims,
R,
disp = "gaussian",
child.dist = "pois",
child.info = NULL,
sibling.list = NULL,
edge.correction = "pbc",
start = NULL,
bounds = NULL,
use.bobyqa = FALSE,
trace = FALSE
)
points |
A matrix or list of matrices containing locations of observed points, where each row corresponds to a point and each column corresponds to a dimension. If a list, then the patterns are assumed to be independent and a single process is fitted to all. |
lims |
A matrix or list of matrices with two columns,
corresponding to the upper and lower limits of each dimension,
respectively. If a list, then each matrix provides the limits
for the corresponding pattern in |
R |
Truncation distance for the difference process. |
disp |
A character string indicating the distribution of
children around their parents. Use |
child.dist |
The distribution of the number of children
generated by a randomly selected parent. For a Poisson
distribution, use |
child.info |
A list of further information that is required about the distribution for the number of children generated by parents. See ‘Details’. |
sibling.list |
An optional list that comprises (i) a component
named |
edge.correction |
The method used for the correction of edge
effects. Either |
start |
A named vector of starting values for the model parameters. |
bounds |
A list with named components. Each component should be a vector of length two, giving the upper and lower bounds for the named parameter. |
use.bobyqa |
Logical; if |
trace |
Logical; if |
The parameter D
is the density of parent points, which is
always estimated. Possible additional parameters are
lambda
, the expected number of children generated per
parent (when child.dist = "pois"
).
p
, the proportion of the x
possible children
that are generated (when child.dist = "binomx"
).
kappa
, the average length of the surface phase of a
diving cetacean (when child.dist = "twocamera"
; see
Stevenson, Borchers, and Fewster, 2019).
sigma
, the standard deviation of dispersion along
each dimension (when disp
= "gaussian").
tau
, the maximum distance a child can be from its
parent (when disp
= "uniform").
The "child.info"
argument is required when child.dist
is set to "twocamera"
. It must be a list that comprises (i) a
component named w
, providing the halfwidth of the detection
zone; (ii) a component named b
, providing the halfwidth of
the survey area; (iii) a component named l
, providing the
time lag between cameras (in seconds); and (iv) a component named
tau
, providing the mean dive-cycle duration. See Stevenson,
Borchers, and Fewster (2019) for details.
An R6 reference class object.
Jones-Todd, C. M., Caie, P., Illian, J. B., Stevenson, B. C., Savage, A., Harrison, D. J., and Bown, J. L. (in press). Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis. Statistics in Medicine, 38: 1421–1441.
Stevenson, B. C., Borchers, D. L., and Fewster, R. M. (2019) Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. Biometrics, 75: 326–336.
Tanaka, U., Ogata, Y., and Stoyan, D. (2008) Parameter estimation and model selection for Neyman-Scott point processes. Biometrical Journal, 50: 43–57.
Use coef.palm to extract estimated parameters, and plot.palm to plot the estimated Palm intensity function. Use boot.palm to run a parametric bootstrap, allowing calculation of standard errors and confidence intervals.
See sim.ns to simulate from a Neyman-Scott point process.
## Fitting model to example data.
fit <- fit.ns(example.2D, lims = rbind(c(0, 1), c(0, 1)), R = 0.5)
## Printing estimates.
coef(fit)
## Plotting the estimated Palm intensity.
plot(fit)
## Not run:
## Simulating data and fitting additional models.
set.seed(1234)
## One-dimensional Thomas process.
data.thomas <- sim.ns(c(D = 10, lambda = 5, sigma = 0.025), lims = rbind(c(0, 1)))
## Fitting a model to these data.
fit.thomas <- fit.ns(data.thomas$points, lims = rbind(c(0, 1)), R = 0.5)
## Three-dimensional Matern process.
data.matern <- sim.ns(c(D = 10, lambda = 10, tau = 0.1), disp = "uniform",
lims = rbind(c(0, 1), c(0, 2), c(0, 3)))
## Fitting a model to these data.
fit.matern <- fit.ns(data.matern$points, lims = rbind(c(0, 1), c(0, 2), c(0, 3)),
R = 0.5, disp = "uniform")
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.