| indictest {spatialwarnings} | R Documentation |
Significance-assessment of spatial early-warning signals
Description
Assess the significance of spatial early-warning indicators
Usage
indictest(x, nulln = 999, null_method = "perm", null_control = NULL, ...)
Arguments
x |
A spatial warning object such as one produced by the |
nulln |
The number of values to compute to produce the null distribution |
null_method |
The method used to produce the null values (see Details) |
null_control |
List of arguments used to control the generation of null matrices. If NULL, then arguments then sensible defaults are chosen (see Details) |
... |
Additional arguments are ignored |
Details
indictest is used to test the significance of early-warning signals
against 'null matrices', which represent the expected spatial structure
in the absence of the biological process of interest.
For a given indicator, a null distribution is obtained by producing a set
of 'null' matrices, from which indicator values are recomputed. This
produces a null distribution of nulln indicator values against
which the observed value is tested.
Several methods are available to produce the set of null matrices. If
null_method is set to "perm", the original matrix is reshuffled
to obtain a null matrix. If null_method is set to "intercept", then
a generalized linear model of the form 'y ~ 1' (where y represents the
values of the matrix) is fitted, then values are drawn from this model. If
null_method is set to "smooth", then a smooth surface is fitted
based on a generalized additive model (using gam) to
the matrix, then values are drawn from this model. When using the
"intercept" or "smooth" null models, it is important to make sure the
model 'family' corresponds to the type of values present in the matrix. By
default, if a matrix contains TRUE/FALSE values, a 'binomial()' family is
used, otherwise a 'gaussian()' family is used. More information about null
models is available in the spatialwarnings FAQ.
Please note that specific null methods may exists for some indicators, such as
flowlength. These are often based on
analytical approximation and allow faster computations.
If a matrix has attributes, then these are preserved and passed to the function used to compute the indicator value, except when using the null method 'perm', in which case matrix attributes are discarded.
The list null_control can be used to adjust the computation of
null matrices. It can have the following components:
'family' The family used in the model used to produce the null matrices. Typically, it is one of
binomial(),binomial(), etc.'qinf' The lower quantile to compute from the null distribution and display in summaries/plots. A numeric value between 0 and 1.
'qsup' The upper quantile to compute from the null distribution and display in summaries/plots. A numeric value between 0 and 1.
Value
An object with a class ending in *_sews_test, whose exact
class depends on the input object. plot, summary methods are
available to display the results of computations, and additional methods
may be available depending on the input object (e.g. see
patchdistr_sews_plot).
References
Kefi, S., Guttal, V., Brock, W.A., Carpenter, S.R., Ellison, A.M., Livina, V.N., et al. (2014). Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns. PLoS ONE, 9, e92097
See Also
generic_sews, spectral_sews,
kbdm_sews,
compute_indicator, flowlength_sews