prep CRAN submission

DESCRIPTION

@@ -1,13 +1,9 @@
 Package: amt
 Type: Package
 Title: Animal Movement Tools
-Version: 0.0.1.9000
-Authors@R: c(person("Johannes","Signer", role = c("aut", "cre"), email = "jmsigner@gmail.com"))
-Description: A collection of functions to work with animal movement data. 
-  The functionality includes methods to resample movement to regular sampling rates, 
-  calculate track statistics (e.g. step lengths, speed, or turning angles), 
-  prepare data for fitting habitat selection analyses (resource and step selection functions), 
-  and interface other packages.
+Version: 0.0.2.0
+Authors@R: c(person("Johannes","Signer", role = c("aut", "cre"), email = "jsigner@gwdg.de"))
+Description: amt (animal movement tools), provides a collection of functions to work with animal movement data. The functionality includes methods to resample movement to regular sampling rates, calculate track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses (resource and step-selection functions), and simulation of space-use from fitted step-selection functions.
 License: GPL-3
 Encoding: UTF-8
 LazyData: true

NAMESPACE

@@ -10,7 +10,6 @@ S3method(arrange,steps)
 S3method(arrange,track_xy)
 S3method(arrange,track_xyt)
 S3method(as_bcpa,track_xyt)
-S3method(as_ctmm,track_xyt)
 S3method(as_ltraj,track_xy)
 S3method(as_ltraj,track_xyt)
 S3method(as_move,track_xy)
@@ -119,6 +118,8 @@ S3method(ta_distr,random_steps)
 S3method(ta_params,fit_clogit)
 S3method(ta_params,random_steps)
 S3method(tac,track_xy)
+S3method(time_of_day,steps)
+S3method(time_of_day,track_xyt)
 S3method(to,track_xyt)
 S3method(tot_dist,track_xy)
 S3method(track_resample,track_xyt)
@@ -131,7 +132,6 @@ export(adjust_kappa)
 export(adjust_scale)
 export(adjust_shape)
 export(as_bcpa)
-export(as_ctmm)
 export(as_ltraj)
 export(as_move)
 export(as_moveHMM)
@@ -143,7 +143,6 @@ export(centroid)
 export(coords)
 export(cum_dist)
 export(cumulative_ud)
-export(day_night)
 export(diff_x)
 export(diff_y)
 export(direction_abs)
@@ -153,9 +152,12 @@ export(distance_to_centers)
 export(extract_covariates)
 export(filter_min_n_burst)
 export(fit_clogit)
+export(fit_issf)
 export(fit_logit)
+export(fit_rsf)
 export(fit_sl_dist)
 export(fit_sl_dist_base)
+export(fit_ssf)
 export(fit_ta_dist)
 export(fit_ta_dist_base)
 export(from)
@@ -170,7 +172,6 @@ export(hr_locoh_k)
 export(hr_mcp)
 export(inspect)
 export(intensity_use)
-export(issf_to_ud)
 export(mk_track)
 export(movement_kernel)
 export(msd)
@@ -179,7 +180,6 @@ export(plot_sl)
 export(random_points)
 export(random_steps)
 export(remove_capture_effect)
-export(screen_relocations)
 export(simulate_ud)
 export(sinuosity)
 export(sl_distr)
@@ -196,10 +196,10 @@ export(ta_distr)
 export(ta_kappa)
 export(ta_params)
 export(tac)
+export(time_of_day)
 export(to)
 export(tot_dist)
 export(track)
-export(track_immobile_base)
 export(track_resample)
 export(transform_coords)
 export(velocity)

R/adjust_parameters.R

@@ -1,16 +1,12 @@
 #' Adjust parameters
 #'
-#' Functions aiding parameter adjustment after fitting an integrated step
-#' selection function (iSSF).
+#' Functions for parameter adjustment after fitting an integrated step-selection function (iSSF).
 #'
-#' Currently the shape and scale parameter of a Gamma distribution and the
-#' concentration parameter (=kappa) of a von-Mises distribution can be adjusted.
-#' When fitting a integrated step selection model, the tentative shape parameter
-#' of the Gamma distribution of the step length can be adjusted with the
-#' coefficients of the log of step lengths, the scale of the Gamma distribution
-#' can be adjusted with the step lengths, and the kappa parameter of the von
-#' Mises distribution can be adjusted with the coefficient for the cosine of the
-#' turn angle.
+#' The shape and scale parameter of a gamma distribution, and the
+#' concentration parameter (=kappa) of a von-Mises distribution can be adjusted. The following adjustments are possible:
+#' 1. The shape parameter of a gamma distribution fitted to the observed step lengths, can be adjusted with the coefficient for the log of the step lenghts.
+#' 2. The scale parameter of a gamma distribution fitted to the observed step lengths, can be adjusted with the coefficient for the step lenghts.
+#' 3. The concentration parameter of fa von Mises distribution fitted to the observed turning angle, can be adjusted with the coefficient for the cosine of turning angles.
 #'
 #' @examples
 #' # Using the deer data set

R/as_track.R

@@ -1,4 +1,4 @@
-#' Coerce to track.
+#' Coerce to track
 #'
 #' Coerce other classes (currently implemented: `SpatialPoints`) to a `track_xy`.
 #' @export

R/coercion.R

@@ -1,6 +1,6 @@
 #' Coerce a track to other formats.
 #'
-#' Several other packages provides methods to analyse movement data, and `amt` provides coercion methods to some packages.
+#' Several other packages provides methods to analyse movement data, and `amt` provides coercion methods to some packages
 #'
 #' @template track_xy_star
 #' @param id `[numeric,character,factor]` \cr Animal id(s).
@@ -129,20 +129,21 @@ as_bcpa.track_xyt <- function(x, ...) {
 }
 
 # as_ctmm -----------------------------------------------------------------
-#' @export
-#' @rdname coercion
-#' @examples
-#' data(deer)
-#' as_ctmm(deer)
-as_ctmm <- function(x, ...) {
-  UseMethod("as_ctmm", x)
-}
-
-#' @export
-#' @rdname coercion
-as_ctmm.track_xyt <- function(x, ...) {
-  ctmm::as.telemetry(as_move(x, ...))
-}
+# TODO
+# #' @export
+# #' @rdname coercion
+# #' @examples
+# #' data(deer)
+# #' as_ctmm(deer)
+# as_ctmm <- function(x, ...) {
+#   UseMethod("as_ctmm", x)
+# }
+#
+# #' @export
+# #' @rdname coercion
+# as_ctmm.track_xyt <- function(x, ...) {
+#   ctmm::as.telemetry(as_move(x, ...))
+# }
 
 
 # as_moveHMM --------------------------------------------------------------

R/data-animal_sim.R

@@ -1,6 +1,6 @@
 #' Simulated movement data of 1 animal.
 #'
-#' 32400 simulated relocations of an animal.
+#' 32400 simulated relocations of an animal
 #'
 #' @format A `data_frame` with 32400 rows and 3 variables:
 #' \describe{

R/data-sh.R

@@ -1,4 +1,4 @@
-#' Relocations of 1 red deer.
+#' Relocations of 1 red deer
 #'
 #' 1500 GPS relocations of one red deer from northern Germany.
 #'
@@ -12,7 +12,7 @@
 #' @source Verein für Wildtierforschung Dresden und Göttingen e.V.
 "sh"
 
-#' Forest cover.
+#' Forest cover
 #'
 #' Forest cover for the home range of one red deer in northern Germany.
 #'
@@ -23,7 +23,7 @@
 #'   data.
 "sh_forest"
 
-#' Relocations of 1 red deer.
+#' Relocations of 1 red deer
 #'
 #' 826 GPS relocations of one red deer from northern Germany. The data is
 #' already resampled to a regular time interval of 6 hours and the coordinate

R/distance_to_center.R

@@ -1,4 +1,4 @@
-#' Distance to center.
+#' Distance to center
 #'
 #' Distances to frequently used areas. `distance_to_center` calculates the
 #' distance to the home-range center (i.e., the centroid of the `x` and `y`

R/extract_covariates.R

@@ -1,4 +1,4 @@
-#' Extract covariate values.
+#' Extract covariate values
 #'
 #' Extract the covariate values at relocations, or at the beginning or end of
 #' steps.
@@ -39,9 +39,9 @@ extract_covariates.random_points <- function(x, covariates, ...) {
 extract_covariates.steps <- function(x, covariates, where = "end", ...) {
   if (class(covariates) %in% paste0("Raster", c("Layer", "Stack", "Brick"))) {
     if (where == "both") {
-      x_start <- raster::extract(covariates, x[, c("x1_", "y1_")], df = TRUE)[, -1]
+      x_start <- raster::extract(covariates, x[, c("x1_", "y1_")], df = TRUE)[, -1, drop = FALSE]
       names(x_start) <- paste0(names(x_start), "_start")
-      x_end <- raster::extract(covariates, x[, c("x2_", "y2_")], df = TRUE)[, -1]
+      x_end <- raster::extract(covariates, x[, c("x2_", "y2_")], df = TRUE)[, -1, drop = FALSE]
       names(x_end) <- paste0(names(x_end), "_end")
       x_all <- cbind(x_start, x_end)
       x[names(x_all)] <- x_all

R/fit_clogit.R

@@ -7,6 +7,7 @@
 #' @param more `[list]` \cr Optional list that is passed on the output.
 #' @param summary_only `[logical(1)=FALSE]` \cr If `TRUE` only a `broom::tidy` summary of the model is returned.
 #' @param ... Additional arguments, passed to `survival::clogit`.
+#' @name fit_clogit
 #' @export
 
 fit_clogit <- function(data, formula, more = NULL, summary_only = FALSE, ...) {
@@ -39,3 +40,11 @@ summary.fit_clogit <- function(object, ...) {
   base::summary(object$model, ...)
 }
 
+
+#' @rdname fit_clogit
+#' @export
+fit_ssf <- fit_clogit
+
+#' @rdname fit_clogit
+#' @export
+fit_issf <- fit_clogit

R/fit_distr.R

@@ -153,7 +153,7 @@ plot_sl.fit_clogit <- function(x, n = 1000, ...) {
 }
 
 # turning angles ----------------------------------------------------------
-#' Fit a statistical distribution to the turn angles of a track.
+#' Fit a statistical distribution to the turn angles of a track
 #'
 #' @param .tbl `[track_xy,track_xyt]` \cr A track.
 #' @param x `[expression]` \cr The name of the column containing turn angles, usually `ta_`.

R/fit_logit.R

@@ -4,6 +4,7 @@
 #' @param data `[data.frame]` \cr The data used to fit a model.
 #' @param formula `[formula]` \cr The model formula.
 #' @param ... Further arguments passed to `stats::glm`.
+#' @name fit_logit
 #' @export
 fit_logit <- function(data, formula, ...) {
   m <- stats::glm(formula, data = data, family = stats::binomial(link = "logit"), ...)
@@ -22,3 +23,7 @@ coef.fit_logit <- function(object, ...) {
 summary.fit_logit <- function(object, ...) {
   base::summary(object$model, ...)
 }
+
+#' @rdname fit_logit
+#' @export
+fit_rsf <- fit_logit

R/helper.R

@@ -1,4 +1,4 @@
-#' Converts angles to radians.
+#' Converts angles to radians
 #'
 #' @param x `[numeric]`\cr Angles in degrees.
 #' @export

R/movement_kernel.R

@@ -1,6 +1,7 @@
-#' @param scale,shape `[numeric](1)\cr` Scale and scale parameter of the gamma distribution of step lengths.
+#' @param scale,shape `[numeric](1)`\cr Scale and scale parameter of the gamma distribution of step lengths.
 #' @param quant A numeric scalar, quantile of the step-length distribution that is
 #'   the maximum movement distance.
+#' @param template `[RasterLayer,RasterStack]`\cr A raster serving as template for the simulations.
 #' @details  **`movement_kernel()`:** calculates a movement kernel from a fitted
 #'   (i)SSF. The method is currently only implemented for the gamma
 #'   distribution.

R/random_steps.R

@@ -32,9 +32,12 @@ random_steps.steps <- function(x, n_control = 10, sl_distr = "gamma", ta_distr =
 
   sl <- fit_sl_dist_base(x$sl_, distr = sl_distr)
   ta <- fit_ta_dist_base(x$ta_, distr = ta_distr)
-  random_steps_base(x, n_control, sl, ta)
+  xx <- random_steps_base(x, n_control, sl, ta)
+  attr(xx, "crs_") <- attr(x, "crs_")
+  xx
 }
 
+
 random_steps_base <- function(x, n_control, sl, ta) {
   # Generate random points
   ns <- nrow(x)  # number of steps
@@ -101,6 +104,7 @@ rsteps_transfer_attr <- function(from, to) {
   attributes(to)$class <- from$class
   attributes(to)$sl_ <- from$sl_
   attributes(to)$ta_ <- from$ta_
+  attributes(to)$crs_ <- from$crs_
   to
 }
 

R/screen_relocations.R

@@ -1,18 +1,18 @@
-#' Screen relocations
-#'
-#' Function to screen relocations for possible errors. The algorithm was first described by Bjorneraas et al. 2010.
-#'
-#' @param x A `track_xyt`
-#' @param median_crit `[numeric(1)]{>0}` \cr Threshold for the median (in m).
-#' @param mean_crit `[numeric(1)]{>0}` \cr Threshold for the mean (in m).
-#' @param spike_speed `[numeric(1)]{>0}` \cr Threshold spike speed in m/s.
-#' @param spike_cos `[numeric(1)]{>0}` \cr Threshold for the cosine.
-#' @param window `[numeric(1)=10]{>2}` \cr Window width.
-#' @export
-#'
-#' @examples
-#' data(deer)
-#' screen_relocations(deer, 100, 100, 0.1, 0.3, 10)
+# #' Screen relocations
+# #'
+# #' Function to screen relocations for possible errors. The algorithm was first described by Bjorneraas et al. 2010.
+# #'
+# #' @param x A `track_xyt`
+# #' @param median_crit `[numeric(1)]{>0}` \cr Threshold for the median (in m).
+# #' @param mean_crit `[numeric(1)]{>0}` \cr Threshold for the mean (in m).
+# #' @param spike_speed `[numeric(1)]{>0}` \cr Threshold spike speed in m/s.
+# #' @param spike_cos `[numeric(1)]{>0}` \cr Threshold for the cosine.
+# #' @param window `[numeric(1)=10]{>2}` \cr Window width.
+# #' @export
+# #'
+# #' @examples
+# #' data(deer)
+# #' screen_relocations(deer, 100, 100, 0.1, 0.3, 10)
 
 
 screen_relocations <- function(x, median_crit, mean_crit, spike_speed, spike_cos, window) {