R/deviation_test.r

Defines functions print.deviation_test deviation_test

Documented in deviation_test print.deviation_test

#' Deviation test
#'
#' Crop the curve set to the interval of distances [r_min, r_max],
#' calculate residuals, scale the residuals and perform a deviation test
#' with a chosen deviation measure.
#' The deviation tests are well known in spatial statistics; in \pkg{GET} they are
#' provided for comparative purposes. Some (maximum type) of the deviation test
#' have their corresponding envelope tests available, see Myllymäki et al., 2017
#' (and 'unscaled', 'st' and 'qdir' in \code{\link{global_envelope_test}}).
#'
#'
#' The deviation test is based on a test function \eqn{T(r)}{T(r)} and it works as follows:
#'
#' 1) The test function estimated for the data, \eqn{T_1(r)}{T_1(r)}, and for nsim simulations
#' from the null model, \eqn{T_2(r), ...., T_{nsim+1}(r)}{T_2(r), ...., T_{nsim+1}(r)}, must be saved in 'curve_set'
#' and given to the deviation_test function.
#'
#' 2) The deviation_test function then
#'\itemize{
#'   \item Crops the functions to the chosen range of distances \eqn{[r_{\min}, r_{\max}]}{[r_min, r_max]}.
#'   \item If the curve_set does not consist of residuals (see \code{\link{residual}}),
#'     then the residuals \eqn{d_i(r) = T_i(r) - T_0(r)}{d_i(r) = T_i(r) - T_0(r)} are calculated, where \eqn{T_0(r)}{T_0(r)} is the
#'     expectation of \eqn{T(r)}{T(r)} under the null hypothesis.
#'     If use_theo = TRUE, the theoretical value given in the curve_set$theo is used for
#'     as \eqn{T_0(r)}{T_0(r)}, if it is given. Otherwise, \eqn{T_0(r)}{T_0(r)} is estimated by the mean of \eqn{T_j(r)}{T_j(r)},
#'     \eqn{j=2,...,nsim+1}{j=2,...,nsim+1}.
#'   \item Scales the residuals. Options are
#'         \itemize{
#'           \item 'none' No scaling. Nothing done.
#'           \item 'q' Quantile scaling.
#'           \item 'qdir' Directional quantile scaling.
#'           \item 'st' Studentised scaling.
#'         }
#'         See for details Myllymäki et al. (2013).
#'   \item Calculates the global deviation measure \eqn{u_i}{u_i}, \eqn{i=1,...,nsim+1}{i=1,...,nsim+1}, see options
#'         for 'measure'.
#'         \itemize{
#'           \item 'max' is the maximum deviation measure
#'
#'           \deqn{u_i = \max_{r \in [r_{\min}, r_{\max}]} | w(r)(T_i(r) - T_0(r))|}{%
#'                 u_i = max_(r in [r_min, r_max]) | w(r)(T_i(r) - T_0(r)) |}
#'
#'           \item 'int2' is the integral deviation measure
#'
#'           \deqn{u_i = \int_{r_{\min}}^{r_{\max}} ( w(r)(T_i(r) - T_0(r)) )^2 dr}{%
#'                 u_i = int_([r_min, r_max]) ( w(r)(T_i(r) - T_0(r)) )^2 dr}
#'
#'           \item 'int' is the 'absolute' integral deviation measure
#'
#'           \deqn{u_i = \int_{r_{\min}}^{r_{\max}} |w(r)(T_i(r) - T_0(r))| dr}{%
#'                 u_i = int_([r_min, r_max]) | w(r)(T_i(r) - T_0(r)) | dr}
#'
#'         }
#'   \item Calculates the p-value.
#'}
#'
#' Currently, there is no special way to take care of the same values of \eqn{T_i(r)}{T_i(r)}
#' occuring possibly for small distances. Thus, it is preferable to exclude from
#' the test the very small distances r for which ties occur.
#'
#'
#' @inheritParams crop_curves
#' @inheritParams residual
#' @param curve_set A residual curve_set object. Can be obtained by using
#'   residual().
#' @param measure The deviation measure to use. Default is 'max'. Must be
#'   one of the following: 'max', 'int' or 'int2'.
#' @param scaling The name of the scaling to use. Options include 'none',
#'   'q', 'qdir' and 'st'. 'qdir' is default.
#' @param savedevs Logical. Should the global rank values k_i, i=1,...,nsim+1 be returned? Default: FALSE.
#' @return If 'savedevs=FALSE' (default), the p-value is returned.
#' If 'savedevs=TRUE', then a list containing the p-value and calculated deviation measures
#' \eqn{u_i}{u_i}, \eqn{i=1,...,nsim+1}{i=1,...,nsim+1} (where \eqn{u_1}{u_1} corresponds to the data pattern) is returned.
#' @references
#' Myllymäki, M., Grabarnik, P., Seijo, H. and Stoyan. D. (2015). Deviation test construction and power comparison for marked spatial point patterns. Spatial Statistics 11: 19-34. doi: 10.1016/j.spasta.2014.11.004
#'
#' Myllymäki, M., Mrkvička, T., Grabarnik, P., Seijo, H. and Hahn, U. (2017). Global envelope tests for spatial point patterns. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79: 381–404. doi: 10.1111/rssb.12172
#' @export
#' @examples
#' ## Testing complete spatial randomness (CSR)
#' #-------------------------------------------
#' if(require("spatstat.core", quietly=TRUE)) {
#'   pp <- unmark(spruces)
#'   \donttest{nsim <- 999}
#'   \dontshow{nsim <- 19}
#'   # Generate nsim simulations under CSR, calculate L-function for the data and simulations
#'   env <- envelope(pp, fun="Lest", nsim=nsim, savefuns=TRUE, correction="translate")
#'   # The deviation test using the integral deviation measure
#'   res <- deviation_test(env, measure='int')
#'   res
#'   # or
#'   res <- deviation_test(env, r_min=0, r_max=7, measure='int2')
#' }
#'
deviation_test <- function(curve_set, r_min = NULL, r_max = NULL,
        use_theo = TRUE, scaling = 'qdir',
        measure = 'max', savedevs=FALSE) {
  curve_set <- crop_curves(curve_set, r_min = r_min, r_max = r_max)
  curve_set <- residual(curve_set, use_theo = use_theo)
  curve_set <- scale_curves(curve_set, scaling = scaling)
  devs <- deviation(curve_set, measure = measure)
  p <- estimate_p_value(devs[1], devs[-1])
  if(savedevs) res <- list(p=p, devs=devs, call=match.call())
  else res <- list(p=p, call=match.call())
  class(res) <- 'deviation_test'
  res
}

#' Print method for the class 'deviation_test'
#'
#' @param x an 'deviation_test' object
#' @param ... Ignored.
#' @export
print.deviation_test <- function(x, ...) {
  with(x, cat("p-value of the test: ", p, "\n", sep=""))
}

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GET documentation built on March 21, 2021, 9:06 a.m.