Help for package sperrorest

Type:

Package

Title:

Perform Spatial Error Estimation and Variable Importance Assessment

Version:

3.0.5

Description:

Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

License:

GPL-3

URL:

https://giscience-fsu.github.io/sperrorest/, https://github.com/giscience-fsu/sperrorest

BugReports:

https://github.com/giscience-fsu/sperrorest/issues

Depends:

R (≥ 2.10)

Imports:

dplyr, future, future.apply, graphics, ROCR, stats, stringr

Suggests:

knitr, MASS, nnet, parallel, ranger, rmarkdown, rpart, sp, testthat

VignetteBuilder:

knitr

ByteCompile:

true

Encoding:

UTF-8

LazyData:

true

LazyLoad:

yes

RoxygenNote:

7.2.0

NeedsCompilation:

Packaged:

2022-10-12 16:39:19 UTC; yo54pow

Author:

Alexander Brenning

[aut, cre], Patrick Schratz

[aut], Tobias Herrmann

[ctb]

Maintainer:

Alexander Brenning <alexander.brenning@uni-jena.de>

Repository:

CRAN

Date/Publication:

2022-10-16 12:50:02 UTC

Spatial Error Estimation and Variable Importance

Description

This package implements spatial error estimation and permutation-based spatial variable importance using different spatial cross-validation and spatial block bootstrap methods. To cite ‘sperrorest’ in publications, reference the paper by Brenning (2012).

References

Brenning, A. 2012. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: the R package 'sperrorest'. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 23-27 July 2012, p. 5372-5375.

Brenning, A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6): 853-862.

Russ, G. & A. Brenning. 2010a. Data mining in precision agriculture: Management of spatial information. In 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010; Dortmund; 28 June - 2 July 2010. Lecture Notes in Computer Science, 6178 LNAI: 350-359.

Russ, G. & A. Brenning. 2010b. Spatial variable importance assessment for yield prediction in Precision Agriculture. In Advances in Intelligent Data Analysis IX, Proceedings, 9th International Symposium, IDA 2010, Tucson, AZ, USA, 19-21 May 2010. Lecture Notes in Computer Science, 6065 LNCS: 184-195.

Add distance information to resampling objects

Description

Add distance information to resampling objects

Usage

add.distance(object, ...)

## S3 method for class 'resampling'
add.distance(object, data, coords = c("x", "y"), ...)

## S3 method for class 'represampling'
add.distance(object, data, coords = c("x", "y"), mode = "future", ...)

Arguments

object

resampling or represampling object.

...

Additional arguments to dataset_distance and add.distance.resampling, respectively.

data

data.frame containing at least the columns specified by coords

coords

(ignored by partition_cv)

mode

Use future.apply::future_lapply() for parallelized execution if mode = "future", and lapply for sequential execution otherwise (mode = "sequential")

Details

Nearest-neighbour distances are calculated for each sample in the test set. These ⁠nrow(???$test)⁠ nearest-neighbour distances are then averaged. Aggregation methods other than mean can be chosen using the fun argument, which will be passed on to dataset_distance.

Value

A resampling or represampling object containing an additional. ⁠$distance⁠ component in each resampling object. The distance component is a single numeric value indicating, for each train / test pair, the (by default, mean) nearest-neighbour distance between the two sets.

Examples

# Muenchow et al. (2012), see ?ecuador
nsp.parti <- partition_cv(ecuador)
sp.parti <- partition_kmeans(ecuador)
nsp.parti <- add.distance(nsp.parti, data = ecuador)
sp.parti <- add.distance(sp.parti, data = ecuador)
# non-spatial partioning: very small test-training distance:
nsp.parti[[1]][[1]]$distance
# spatial partitioning: more substantial distance, depending on number of
# folds etc.
sp.parti[[1]][[1]]$distance

Resampling objects with repetition, i.e. sets of partitionings or bootstrap samples

Description

Functions for handling represampling objects, i.e. lists of resampling objects.

Usage

as.represampling(object, ...)

## S3 method for class 'list'
as.represampling(object, ...)

## S3 method for class 'represampling'
print(x, ...)

is_represampling(object)

Arguments

object

object of class represampling, or a list to be coerced to this class.

...

currently not used.

x

object of class represampling.

Details

represampling objects are (names) lists of resampling objects. Such objects are typically created by partition_cv, partition_kmeans, represampling_disc_bootstrap and related functions.

In r-repeated k-fold cross-validation, for example, the corresponding represampling object has length r, and each of its r resampling objects has length k.

as.resampling_list coerces object to class represampling while coercing its elements to resampling objects. Some validity checks are performed.

Value

as.represampling methods return an object of class represampling with the contents of object.

Examples

# Muenchow et al. (2012), see ?ecuador
# Partitioning by elevation classes in 200 m steps:
fac <- factor(as.character(floor(ecuador$dem / 300)))
summary(fac)
parti <- as.resampling(fac)
# a list of lists specifying sets of training and test sets,
# using each factor at a time as the test set:
str(parti)
summary(parti)

Resampling objects such as partitionings or bootstrap samples

Description

Create/coerce and print resampling objects, e.g., partitionings or bootstrap samples derived from a data set.

Usage

as.resampling(object, ...)

## Default S3 method:
as.resampling(object, ...)

## S3 method for class 'factor'
as.resampling(object, ...)

## S3 method for class 'list'
as.resampling(object, ...)

validate.resampling(object)

is.resampling(x, ...)

## S3 method for class 'resampling'
print(x, ...)

Arguments

object

depending on the function/method, a list or a vector of type factor defining a partitioning of the dataset.

...

currently not used.

x

object of class resampling.

Details

A resampling object is a list of lists defining a set of training and test samples.

In the case of k-fold cross-validation partitioning, for example, the corresponding resampling object would be of length k, i.e. contain k lists. Each of these k lists defines a training set of size n(k-1)/k (where n is the overall sample size), and a test set of size n/k. The resampling object does, however, not contain the data itself, but only indices between 1 and n identifying the selection (see Examples).

Another example is bootstrap resampling. represampling_bootstrap with argument oob = TRUE generates represampling objects with indices of a bootstrap sample in the train component and indices of the out-of-bag sample in the test component (see Examples below).

as.resampling.factor: For each factor level of the input variable, as.resampling.factor determines the indices of samples in this level (= test samples) and outside this level (= training samples). Empty levels of object are dropped without warning.

as.resampling_list checks if the list in object has a valid resampling object structure (with components train and test etc.) and assigns the class attribute 'resampling' if successful.

Value

as.resampling methods: An object of class resampling.

Examples

# Muenchow et al. (2012), see ?ecuador

# Partitioning by elevation classes in 200 m steps:
parti <- factor(as.character(floor(ecuador$dem / 200)))
smp <- as.resampling(parti)
summary(smp)
# Compare:
summary(parti)

# k-fold (non-spatial) cross-validation partitioning:
parti <- partition_cv(ecuador)
parti <- parti[[1]] # the first (and only) resampling object in parti
# data corresponding to the test sample of the first fold:
str(ecuador[parti[[1]]$test, ])
# the corresponding training sample - larger:
str(ecuador[parti[[1]]$train, ])

# Bootstrap training sets, out-of-bag test sets:
parti <- represampling_bootstrap(ecuador, oob = TRUE)
parti <- parti[[1]] # the first (and only) resampling object in parti
# out-of-bag test sample: approx. one-third of nrow(ecuador):
str(ecuador[parti[[1]]$test, ])
# bootstrap training sample: same size as nrow(ecuador):
str(ecuador[parti[[1]]$train, ])

Alphanumeric tile names

Description

Functions for generating and handling alphanumeric tile names of the form 'X2:Y7' as used by partition_tiles and represampling_tile_bootstrap.

Usage

as.tilename(x, ...)

## S3 method for class 'numeric'
as.tilename(x, ...)

## S3 method for class 'tilename'
as.character(x, ...)

## S3 method for class 'tilename'
as.numeric(x, ...)

## S3 method for class 'character'
as.tilename(x, ...)

## S3 method for class 'tilename'
print(x, ...)

Arguments

x

object of class tilename, character, or numeric (of length 2).

...

additional arguments (currently ignored).

Value

object of class tilename, character, or numeric vector of length 2

Examples

tnm <- as.tilename(c(2, 3))
tnm # 'X2:Y3'
as.numeric(tnm) # c(2,3)

Calculate mean nearest-neighbour distance between point datasets

Description

dataset_distance calculates Euclidean nearest-neighbour distances between two point datasets and summarizes these distances using some function, by default the mean.

Usage

dataset_distance(
  d1,
  d2,
  x_name = "x",
  y_name = "y",
  fun = mean,
  method = "euclidean",
  ...
)

Arguments

d1

a data.frame with (at least) columns with names given by x_name and y_name; these contain the x and y coordinates, respectively.

d2

see d1 - second set of points

x_name

name of column in d1 and d2 containing the x coordinates of points.

y_name

same for y coordinates

fun

function to be applied to the vector of nearest-neighbor distances of d1 from d2.

method

type of distance metric to be used; only 'euclidean' is currently supported.

...

additional arguments to fun.

Details

Nearest-neighbour distances are calculated for each point in d1, resulting in a vector of length nrow(d1), and fun is applied to this vector.

Value

depends on fun; typically (e.g., mean) a numeric vector of length 1

Examples

df <- data.frame(x = rnorm(100), y = rnorm(100))
dataset_distance(df, df) # == 0

J. Muenchow's Ecuador landslide data set

Description

Data set created by Jannes Muenchow, University of Erlangen-Nuremberg, Germany. These data should be cited as Muenchow et al. (2012) (see reference below). This publication also contains additional information on data collection and the geomorphology of the area. The data set provided here is (a subset of) the one from the 'natural' part of the RBSF area and corresponds to landslide distribution in the year 2000.

Format

a data.frame with point samples of landslide and non-landslide locations in a study area in the Andes of southern Ecuador.

References

Muenchow, J., Brenning, A., Richter, M., 2012. Geomorphic process rates of landslides along a humidity gradient in the tropical Andes. Geomorphology, 139-140: 271-284. doi:10.1016/j.geomorph.2011.10.029

Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6): 853-862. doi:10.5194/nhess-5-853-2005

Examples

data(ecuador)
str(ecuador)
library(rpart)
ctrl <- rpart.control(cp = 0.02)
fit <- rpart(slides ~ dem + slope + hcurv + vcurv +
  log.carea + cslope, data = ecuador, control = ctrl)
par(xpd = TRUE)
plot(fit, compress = TRUE, main = "Muenchows landslide data set")
text(fit, use.n = TRUE)

Default error function

Description

Calculate a variety of accuracy measures from observations and predictions of numerical and categorical response variables.

Usage

err_default(obs, pred)

Arguments

obs

factor, logical, or numeric vector with observations

pred

factor, logical, or numeric vector with predictions. Must be of same type as obs with the exception that pred may be numeric if obs is factor or logical ('soft' classification).

Value

A list with (currently) the following components, depending on the type of prediction problem:

'hard' classification: Misclassification error, overall accuracy; if two classes, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), kappa
'soft' classification: area under the ROC curve, error and accuracy at a obs>0.5 dichotomization, false-positive rate (FPR; 1-specificity) at 70, 80 and 90 percent sensitivity, true-positive rate (sensitivity) at 80, 90 and 95 percent specificity.
regression: Bias, standard deviation, mean squared error, MAD (mad), median, interquartile range (IQR) of residuals

Note

NA values are currently not handled by this function, i.e. they will result in an error.

Examples

obs <- rnorm(1000)
# Two mock (soft) classification examples:
err_default(obs > 0, rnorm(1000)) # just noise
err_default(obs > 0, obs + rnorm(1000)) # some discrimination
# Three mock regression examples:
err_default(obs, rnorm(1000)) # just noise, but no bias
err_default(obs, obs + rnorm(1000)) # some association, no bias
err_default(obs, obs + 1) # perfect correlation, but with bias

Identify small partitions that need to be fixed.

Description

get_small_tiles identifies partitions (tiles) that are too small according to some defined criterion / criteria (minimum number of samples in tile and/or minimum fraction of entire dataset).

Usage

get_small_tiles(tile, min_n = NULL, min_frac = 0, ignore = c())

Arguments

tile

factor: tile/partition names for all samples; names must be coercible to class tilename, i.e. of the form 'X4:Y2' etc.

min_n

integer (optional): minimum number of samples per partition_

min_frac

numeric >0, <1: minimum relative size of partition as percentage of sample.

ignore

character vector: names of tiles to be ignored, i.e. to be retained even if the inclusion criteria are not met.

Value

character vector: names of tiles that are considered 'small' according to these criteria

Examples

# Muenchow et al. (2012), see ?ecuador
# Rectangular partitioning without removal of small tiles:
parti <- partition_tiles(ecuador, nsplit = c(10, 10), reassign = FALSE)
summary(parti)
length(parti[[1]])
# Same in factor format for the application of get_small_tiles:
parti_fac <- partition_tiles(ecuador,
  nsplit = c(10, 10), reassign = FALSE,
  return_factor = TRUE
)
get_small_tiles(parti_fac[[1]], min_n = 20) # tiles with less than 20 samples
parti2 <- partition_tiles(ecuador,
  nsplit = c(10, 10), reassign = TRUE,
  min_n = 20, min_frac = 0
)
length(parti2[[1]]) # < length(parti[[1]])

Fruit-tree crop classification: the Maipo dataset

Description

This dataset is from a case study on fruit-tree crop classification using a satellite image time series. The dataset should be cited as Pena & Brenning (2015), reference below. There are 7713 grid cells from 400 fields in this dataset, which makes it necessary to apply spatial cross-validation at the field level for model assessment (see partition_factor_cv()).

Format

a data.frame with point samples (grid cells) of crop type and Landsat-derived remote sensing features (spectral bands and vegetation indices). In addition, UTM x/y coordinates and a factor variable indicating which field a grid cell belongs to. Spectral bands are coded as bij where i represents the image date (early to late season, dates see paper), and j is the band number.

References

Pena, M.A., Brenning, A. (2015). Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile. Remote Sensing of Environment, 171: 234-244.

Partition the data for a (non-spatial) cross-validation

Description

partition_cv creates a represampling object for length(repetition)-repeated nfold-fold cross-validation.

Usage

partition_cv(
  data,
  coords = c("x", "y"),
  nfold = 10,
  repetition = 1,
  seed1 = NULL,
  return_factor = FALSE
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

(ignored by partition_cv)

nfold

number of partitions (folds) in nfold-fold cross-validation partitioning

repetition

numeric vector: cross-validation repetitions to be generated. Note that this is not the number of repetitions, but the indices of these repetitions. E.g., use repetition = c(1:100) to obtain (the 'first') 100 repetitions, and repetition = c(101:200) to obtain a different set of 100 repetitions.

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

return_factor

if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)

Details

This function does not actually perform a cross-validation or partition the data set itself; it simply creates a data structure containing the indices of training and test samples.

Value

If return_factor = FALSE (the default), a represampling object. Specifically, this is a (named) list of length(repetition) resampling objects. Each of these resampling objects is a list of length nfold corresponding to the folds. Each fold is represented by a list of containing the components train and test, specifying the indices of training and test samples (row indices for data). If return_factor = TRUE (mainly used internally), a (named) list of length length(repetition). Each component of this list is a vector of length nrow(data) of type factor, specifying for each sample the fold to which it belongs. The factor levels are factor(1:nfold).

Examples

data(ecuador)
## non-spatial cross-validation:
resamp <- partition_cv(ecuador, nfold = 5, repetition = 5)
# plot(resamp, ecuador)
# first repetition, second fold, test set indices:
idx <- resamp[["1"]][[2]]$test
# test sample used in this particular repetition and fold:
ecuador[idx, ]

Partition the data for a stratified (non-spatial) cross-validation

Description

partition_cv_strat creates a set of sample indices corresponding to cross-validation test and training sets.

Usage

partition_cv_strat(
  data,
  coords = c("x", "y"),
  nfold = 10,
  return_factor = FALSE,
  repetition = 1,
  seed1 = NULL,
  strat
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations

nfold

number of partitions (folds) in nfold-fold cross-validation partitioning

return_factor

if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)

repetition

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

strat

character: column in data containing a factor variable over which the partitioning should be stratified; or factor vector of length nrow(data): variable over which to stratify

Value

A represampling object, see also partition_cv(). partition_strat_cv, however, stratified with respect to the variable data[,strat]; i.e., cross-validation partitioning is done within each set data[data[,strat]==i,] (i in levels(data[, strat])), and the ith folds of all levels are combined into one cross-validation fold.

Examples

data(ecuador)
parti <- partition_cv_strat(ecuador,
  strat = "slides", nfold = 5,
  repetition = 1
)
idx <- parti[["1"]][[1]]$train
mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE")
# always == 1
# Non-stratified cross-validation:
parti <- partition_cv(ecuador, nfold = 5, repetition = 1)
idx <- parti[["1"]][[1]]$train
mean(ecuador$slides[idx] == "TRUE") / mean(ecuador$slides == "TRUE")
# close to 1 because of large sample size, but with some random variation

Leave-one-disc-out cross-validation and leave-one-out cross-validation

Description

partition_disc partitions the sample into training and tests set by selecting circular test areas (possibly surrounded by an exclusion buffer) and using the remaining samples as training samples (leave-one-disc-out cross-validation). partition_loo creates training and test sets for leave-one-out cross-validation with (optional) buffer.

Usage

partition_disc(
  data,
  coords = c("x", "y"),
  radius,
  buffer = 0,
  ndisc = nrow(data),
  seed1 = NULL,
  return_train = TRUE,
  prob = NULL,
  replace = FALSE,
  repetition = 1
)

partition_loo(data, ndisc = nrow(data), replace = FALSE, ...)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

radius

radius of test area discs; performs leave-one-out resampling if radius <0.

buffer

radius of additional 'neutral area' around test area discs that is excluded from training and test sets; defaults to 0, i.e. all samples are either in the test area or in the training area.

ndisc

Number of discs to be randomly selected; each disc constitutes a separate test set. Defaults to nrow(data), i.e. one disc around each sample.

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

return_train

If FALSE, returns only test sample; if TRUE, also the training area.

prob

optional argument to sample.

replace

optional argument to sample: sampling with or without replacement?

repetition

see partition_cv; however, see Note below: repetition should normally be ⁠= 1⁠ in this function.

...

arguments to be passed to partition_disc

Value

A represampling object. Contains length(repetition) resampling objects. Each of these contains ndisc lists with indices of test and (if return_train = TRUE) training sets.

Note

Test area discs are centered at (random) samples, not at general random locations. Test area discs may (and likely will) overlap independently of the value of replace. replace only controls the replacement of the center point of discs when drawing center points from the samples.

radius < 0 does leave-one-out resampling with an optional buffer. radius = 0 is similar except that samples with identical coordinates would fall within the test area disc.

References

Brenning, A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6): 853-862.

Examples

data(ecuador)
parti <- partition_disc(ecuador,
  radius = 200, buffer = 200,
  ndisc = 5, repetition = 1:2
)
# plot(parti,ecuador)
summary(parti)

# leave-one-out with buffer:
parti.loo <- partition_loo(ecuador, buffer = 200)
summary(parti)

Partition the data for a (non-spatial) leave-one-factor-out cross-validation based on a given, fixed partitioning

Description

partition_factor creates a represampling object, i.e. a set of sample indices defining cross-validation test and training sets.

Usage

partition_factor(
  data,
  coords = c("x", "y"),
  fac,
  return_factor = FALSE,
  repetition = 1
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

fac

either the name of a variable (column) in data, or a vector of type factor and length nrow(data) that contains the partitions to be used for defining training and test samples.

return_factor

if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)

repetition

Value

A represampling object, see also partition_cv for details.

Note

In this partitioning approach, all repetitions are identical and therefore pseudo-replications.

Examples

data(ecuador)
# I don't recommend using this partitioning for cross-validation,
# this is only for demonstration purposes:
breaks <- quantile(ecuador$dem, seq(0, 1, length = 6))
ecuador$zclass <- cut(ecuador$dem, breaks, include.lowest = TRUE)
summary(ecuador$zclass)
parti <- partition_factor(ecuador, fac = "zclass")
# plot(parti,ecuador)
summary(parti)

Partition the data for a (non-spatial) k-fold cross-validation at the group level

Description

partition_factor_cv creates a represampling object, i.e. a set of sample indices defining cross-validation test and training sets, where partitions are obtained by resampling at the level of groups of observations as defined by a given factor variable. This can be used, for example, to resample agricultural data that is grouped by fields, at the agricultural field level in order to preserve spatial autocorrelation within fields.

Usage

partition_factor_cv(
  data,
  coords = c("x", "y"),
  fac,
  nfold = 10,
  repetition = 1,
  seed1 = NULL,
  return_factor = FALSE
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

fac

either the name of a variable (column) in data, or a vector of type factor and length nrow(data) that defines groups or clusters of observations.

nfold

number of partitions (folds) in nfold-fold cross-validation partitioning

repetition

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

return_factor

if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)

Value

A represampling object, see also partition_cv for details.

Note

In this partitioning approach, the number of factor levels in fac must be large enough for this factor-level resampling to make sense.

Partition samples spatially using k-means clustering of the coordinates

Description

partition_kmeans divides the study area into irregularly shaped spatial partitions based on k-means (kmeans) clustering of spatial coordinates.

Usage

partition_kmeans(
  data,
  coords = c("x", "y"),
  nfold = 10,
  repetition = 1,
  seed1 = NULL,
  return_factor = FALSE,
  balancing_steps = 1,
  order_clusters = TRUE,
  ...
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

nfold

number of cross-validation folds, i.e. parameter k in k-means clustering.

repetition

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

return_factor

if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)

balancing_steps

if ⁠> 1⁠, perform nfold-means clustering balancing_steps times, and pick the clustering that minimizes the Gini index of the sample size distribution among the partitions. The idea is that 'degenerate' partitions will be avoided, but this also has the side effect of reducing variation among partitioning repetitions. More meaningful constraints (e.g., minimum number of positive and negative samples within each partition should be added in the future.

order_clusters

if TRUE, clusters are ordered by increasing x coordinate of center point.

...

additional arguments to kmeans.

Value

A represampling object, see also partition_cv for details.

Note

Default parameter settings may change in future releases.

References

Brenning, A., Long, S., & Fieguth, P. (2012). Detecting rock glacier flow structures using Gabor filters and IKONOS imagery. Remote Sensing of Environment, 125, 227-237. doi:10.1016/j.rse.2012.07.005

Examples

data(ecuador)
resamp <- partition_kmeans(ecuador, nfold = 5, repetition = 2)
# plot(resamp, ecuador)

Partition the study area into rectangular tiles

Description

partition_tiles divides the study area into a specified number of rectangular tiles. Optionally small partitions can be merged with adjacent tiles to achieve a minimum number or percentage of samples in each tile.

Usage

partition_tiles(
  data,
  coords = c("x", "y"),
  dsplit = NULL,
  nsplit = NULL,
  rotation = c("none", "random", "user"),
  user_rotation,
  offset = c("none", "random", "user"),
  user_offset,
  reassign = TRUE,
  min_frac = 0.025,
  min_n = 5,
  iterate = 1,
  return_factor = FALSE,
  repetition = 1,
  seed1 = NULL
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations

dsplit

optional vector of length 2: equidistance of splits in (possibly rotated) x direction (dsplit[1]) and y direction (dsplit[2]) used to define tiles. If dsplit is of length 1, its value is recycled. Either dsplit or nsplit must be specified.

nsplit

optional vector of length 2: number of splits in (possibly rotated) x direction (nsplit[1]) and y direction (nsplit[2]) used to define tiles. If nsplit is of length 1, its value is recycled.

rotation

indicates whether and how the rectangular grid should be rotated; random rotation is only between -45 and +45 degrees.

user_rotation

if rotation='user', angles (in degrees) by which the rectangular grid is to be rotated in each repetition. Either a vector of same length as repetition, or a single number that will be replicated length(repetition) times.

offset

indicates whether and how the rectangular grid should be shifted by an offset.

user_offset

if offset='user', a list (or vector) of two components specifying a shift of the rectangular grid in (possibly rotated) x and y direction. The offset values are relative values, a value of 0.5 resulting in a one-half tile shift towards the left, or upward. If this is a list, its first (second) component refers to the rotated x (y) direction, and both components must have same length as repetition (or length 1). If a vector of length 2 (or list components have length 1), the two values will be interpreted as relative shifts in (rotated) x and y direction, respectively, and will therefore be recycled as needed (length(repetition) times each).

reassign

logical (default TRUE): if TRUE, 'small' tiles (as per min_frac and min_n arguments and get_small_tiles) are merged with (smallest) adjacent tiles. If FALSE, small tiles are 'eliminated', i.e. set to NA.

min_frac

numeric >=0, <1: minimum relative size of partition as percentage of sample; argument passed to get_small_tiles. Will be ignored if NULL.

min_n

integer >=0: minimum number of samples per partition; argument passed to get_small_tiles. Will be ignored if NULL.

iterate

argument to be passed to tile_neighbors

return_factor

if FALSE (default), return a represampling object; if TRUE (used internally by other sperrorest functions), return a list containing factor vectors (see Value)

repetition

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

Value

A represampling object. Contains length(repetition) resampling objects as repetitions. The exact number of folds / test-set tiles within each resampling objects depends on the spatial configuration of the data set and possible cleaning steps (see min_frac, min_n).

Note

Default parameter settings may change in future releases. This function, especially the rotation and shifting part of it and the algorithm for cleaning up small tiles is still a bit experimental. Use with caution. For non-zero offsets (⁠offset!='none')⁠), the number of tiles may actually be greater than nsplit[1]*nsplit[2] because of fractional tiles lurking into the study region. reassign=TRUE with suitable thresholds is therefore recommended for non-zero (including random) offsets.

Examples

data(ecuador)
set.seed(42)
parti <- partition_tiles(ecuador, nsplit = c(4, 3), reassign = FALSE)
# plot(parti,ecuador)
# tile A4 has only 55 samples
# same partitioning, but now merge tiles with less than 100 samples to
# adjacent tiles:
parti2 <- partition_tiles(ecuador,
  nsplit = c(4, 3), reassign = TRUE,
  min_n = 100
)
# plot(parti2,ecuador)
summary(parti2)
# tile B4 (in 'parti') was smaller than A3, therefore A4 was merged with B4,
# not with A3
# now with random rotation and offset, and tiles of 2000 m length:
parti3 <- partition_tiles(ecuador,
  dsplit = 2000, offset = "random",
  rotation = "random", reassign = TRUE, min_n = 100
)
# plot(parti3, ecuador)
summary(parti3)

Plot spatial resampling objects

Description

plot.represampling displays the partitions or samples corresponding arising from the resampling of a data set.

Usage

## S3 method for class 'represampling'
plot(x, data, coords = c("x", "y"), pch = "+", wiggle_sd = 0, ...)

## S3 method for class 'resampling'
plot(x, ...)

Arguments

x

a represampling resp. resampling object.

data

a data.frame of samples containing at least the x and y coordinates of samples as specified by coords.

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

pch

point symbol (to be passed to points).

wiggle_sd

'wiggle' the point locations in x and y direction to avoid overplotting of samples drawn multiple times by bootstrap methods; this is a standard deviation (in the units of the x/y coordinates) of a normal distribution and defaults to 0 (no wiggling).

...

additional arguments to plot.

Note

This function is not intended for samples obtained by resampling with replacement (e.g., bootstrap) because training and test points will be overplotted in that case. The size of the plotting region will also limit the number of maps that can be displayed at once, i.e., the number of rows (repetitions) and fields (columns).

Examples

data(ecuador)
# non-spatial cross-validation:
resamp <- partition_cv(ecuador, nfold = 5, repetition = 1:2)
# plot(resamp, ecuador)
# spatial cross-validation using k-means clustering:
resamp <- partition_kmeans(ecuador, nfold = 5, repetition = 1:2)
# plot(resamp, ecuador)

remove_missing_levels

Description

Accounts for missing factor levels present only in test data but not in train data by setting values to NA

Usage

remove_missing_levels(fit, test_data)

Arguments

fit

fitted model on training data

test_data

data to make predictions for

Value

data.frame with matching factor levels to fitted model

Examples

foo <- data.frame(
  response = rnorm(3),
  predictor = as.factor(c("A", "B", "C"))
)
model <- lm(response ~ predictor, foo)
foo.new <- data.frame(predictor = as.factor(c("A", "B", "C", "D")))
predict(model, newdata = remove_missing_levels(
  fit = model,
  test_data = foo.new
))

Non-spatial bootstrap resampling

Description

represampling_bootstrap draws a bootstrap random sample (with replacement) from data.

Usage

represampling_bootstrap(
  data,
  coords = c("x", "y"),
  nboot = nrow(data),
  repetition = 1,
  seed1 = NULL,
  oob = FALSE
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

nboot

Size of bootstrap sample

repetition

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

oob

logical (default FALSE): if TRUE, use the out-of-bag sample as the test sample; if FALSE, draw a second bootstrap sample of size nboot independently to obtain a test sample.

Value

A represampling object. This is a (named) list containing length(repetition). resampling objects. Each of these contains only one list with indices of training and test samples. Indices are row indices for data.

Examples

data(ecuador)
# only 10 bootstrap repetitions, normally use >=100:
parti <- represampling_bootstrap(ecuador, repetition = 10)
# plot(parti, ecuador) # careful: overplotting occurs
# because some samples are included in both the training and
# the test sample (possibly even multiple times)

Overlapping spatial block bootstrap using circular blocks

Description

represampling_disc_bootstrap performs a spatial block bootstrap by resampling at the level of rectangular partitions or 'tiles' generated by partition_tiles.

Usage

represampling_disc_bootstrap(
  data,
  coords = c("x", "y"),
  nboot,
  repetition = 1,
  seed1 = NULL,
  oob = FALSE,
  ...
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

nboot

number of bootstrap samples; you may specify different values for the training sample (nboot[1]) and for the test sample (nboot[2]).

repetition

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

oob

logical (default FALSE): if TRUE, use the out-of-bag sample as the test sample (the complement of the nboot[1] test set discs, minus the buffer area as specified in the ... arguments to partition_disc); if FALSE, draw a second bootstrap sample of size nboot independently to obtain a test sample (sets of overlapping discs drawn with replacement).

...

additional arguments to be passed to partition_disc; note that a buffer argument has not effect if oob=FALSE; see example below

Note

Performs nboot out of nrow(data) resampling of circular discs. This is an overlapping spatial block bootstrap where the blocks are circular.

Examples

data(ecuador)
# Overlapping disc bootstrap:
parti <- represampling_disc_bootstrap(ecuador,
  radius = 200, nboot = 20,
  oob = FALSE
)
# plot(parti, ecuador)
# Note that a 'buffer' argument would make no difference because boostrap
# sets of discs are drawn independently for the training and test sample.
#
# Overlapping disc bootstrap for training sample, out-of-bag sample as test
# sample:
parti <- represampling_disc_bootstrap(ecuador,
  radius = 200, buffer = 200,
  nboot = 10, oob = TRUE
)
# plot(parti,ecuador)

Bootstrap at an aggregated level

Description

represampling_factor_bootstrap resamples partitions defined by a factor variable. This can be used for non-overlapping block bootstraps and similar.

Usage

represampling_factor_bootstrap(
  data,
  fac,
  repetition = 1,
  nboot = -1,
  seed1 = NULL,
  oob = FALSE
)

Arguments

data

data.frame containing at least the columns specified by coords

fac

defines a grouping or partitioning of the samples in data; three possible types: (1) the name of a variable in data (coerced to factor if not already a factor variable); (2) a factor variable (or a vector that can be coerced to factor); (3) a list of factor variables (or vectors that can be coerced to factor); this list must be of length length(repetition), and if it is named, the names must be equal to as.character(repetition); this list will typically be generated by a ⁠partition.*⁠ function with return_factor = TRUE (see Examples below)

repetition

nboot

number of bootstrap replications used for generating the bootstrap training sample (nboot[1]) and the test sample (nboot[2]); nboot[2] is ignored (with a warning) if oob = TRUE. A value of -1 will be substituted with the number of levels of the factor variable, corresponding to an n out of n bootstrap at the grouping level defined by fac.

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

oob

if TRUE, the test sample will be the out-of-bag sample; if FALSE (default), the test sample is an independently drawn bootstrap sample of size nboot[2].

Details

nboot refers to the number of groups (as defined by the factors) to be drawn with replacement from the set of groups. I.e., if fac is a factor variable, nboot would normally not be greater than nlevels(fac), nlevels(fac) being the default as per nboot = -1.

Examples

data(ecuador)
# a dummy example for demonstration, performing bootstrap
# at the level of an arbitrary factor variable:
parti <- represampling_factor_bootstrap(ecuador,
  factor(floor(ecuador$dem / 100)),
  oob = TRUE
)
# plot(parti,ecuador)
# using the factor bootstrap for a non-overlapping block bootstrap
# (see also represampling_tile_bootstrap):
fac <- partition_tiles(ecuador,
  return_factor = TRUE, repetition = c(1:3),
  dsplit = 500, min_n = 200, rotation = "random",
  offset = "random"
)
parti <- represampling_factor_bootstrap(ecuador, fac,
  oob = TRUE,
  repetition = c(1:3)
)
# plot(parti, ecuador)

Spatial block bootstrap at the level of spatial k-means clusters

Description

represampling_kmeans_bootstrap performs a non-overlapping spatial block bootstrap by resampling at the level of irregularly-shaped partitions generated by partition_kmeans.

Usage

represampling_kmeans_bootstrap(
  data,
  coords = c("x", "y"),
  repetition = 1,
  nfold = 10,
  nboot = nfold,
  seed1 = NULL,
  oob = FALSE,
  ...
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

repetition

nfold

see partition_kmeans

nboot

see represampling_factor_bootstrap

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

oob

see represampling_factor_bootstrap

...

additional arguments to be passed to partition_kmeans

Spatial block bootstrap using rectangular blocks

Description

represampling_tile_bootstrap performs a non-overlapping spatial block bootstrap by resampling at the level of rectangular partitions or 'tiles' generated by partition_tiles.

Usage

represampling_tile_bootstrap(
  data,
  coords = c("x", "y"),
  repetition = 1,
  nboot = -1,
  seed1 = NULL,
  oob = FALSE,
  ...
)

Arguments

data

data.frame containing at least the columns specified by coords

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

repetition

nboot

see represampling_factor_bootstrap

seed1

seed1+i is the random seed that will be used by set.seed in repetition i (i in repetition) to initialize the random number generator before sampling from the data set.

oob

see represampling_factor_bootstrap

...

additional arguments to be passed to partition_tiles

Draw uniform random (sub)sample at the group level

Description

resample_factor draws a random (sub)sample (with or without replacement) of the groups or clusters identified by the fac argument.

Usage

resample_factor(data, param = list(fac = "class", n = Inf, replace = FALSE))

Arguments

data

a data.frame, rows represent samples

param

a list with the following components: fac is a factor variable of length nrow(data) or the name of a factor variable in data; n is a numeric value specifying the size of the subsample (in terms of groups, not observations); replace determines if resampling of groups is to be done with or without replacement.

Details

If param$replace=FALSE, a subsample of min(param$n,nlevel(data[,fac])) groups will be drawn from data. If param$replace=TRUE, the number of groups to be drawn is param$n.

Value

a data.frame containing a subset of the rows of data.

Draw stratified random sample

Description

resample_strat_uniform draws a stratified random sample (with or without replacement) from the samples in data. Stratification is over the levels of data[, param$response]. The same number of samples is drawn within each level.

Usage

resample_strat_uniform(
  data,
  param = list(strat = "class", nstrat = Inf, replace = FALSE)
)

Arguments

data

a data.frame, rows represent samples

param

a list with the following components: strat is either the name of a factor variable in data that defines the stratification levels, or a vector of type factor and length nrow(data); n is a numeric value specifying the size of the subsample; replace determines if sampling is with or without replacement

Details

If param$replace=FALSE, a subsample of size min(param$n,nrow(data)) will be drawn from data. If param$replace=TRUE, the size of the subsample is param$n.

Value

a data.frame containing a subset of the rows of data.

Examples

data(ecuador) # Muenchow et al. (2012), see ?ecuador
d <- resample_strat_uniform(ecuador,
  param = list(strat = "slides", nstrat = 100)
)
nrow(d) # == 200
sum(d$slides == "TRUE") # == 100

Draw uniform random (sub)sample

Description

resample_uniform draws a random (sub)sample (with or without replacement) from the samples in data.

Usage

resample_uniform(data, param = list(n = Inf, replace = FALSE))

Arguments

data

a data.frame, rows represent samples

param

a list with the following components: n is a numeric value specifying the size of the subsample; replace determines if sampling is with or without replacement

Details

If param$replace=FALSE, a subsample of size min(param$n,nrow(data)) will be drawn from data. If param$replace=TRUE, the size of the subsample is param$n.

Value

a data.frame containing a subset of the rows of data.

Examples

# Muenchow et al. (2012), see ?ecuador
d <- resample_uniform(ecuador, param = list(strat = "slides", n = 200))
# == 200
sum(d$slides == "TRUE")

runfolds

Description

Runs model fitting, error estimation and variable importance on fold level

Usage

runfolds(
  j = NULL,
  current_sample = NULL,
  data = NULL,
  i = NULL,
  formula = NULL,
  model_args = NULL,
  model_fun = NULL,
  pred_fun = NULL,
  imp_variables = NULL,
  imp_permutations = NULL,
  imp_sample_from = "test",
  err_fun = NULL,
  train_fun = NULL,
  importance = NULL,
  current_res = NULL,
  current_impo = NULL,
  pred_args = NULL,
  pooled_obs_train = NULL,
  pooled_obs_test = NULL,
  pooled_pred_train = NULL,
  response = NULL,
  progress = NULL,
  pooled_pred_test = NULL,
  coords = NULL,
  test_fun = NULL,
  imp_one_rep = NULL,
  do_gc = NULL,
  test_param = NULL,
  train_param = NULL
)

runreps

Description

Runs model fitting, error estimation and variable importance on fold level

Usage

runreps(
  current_sample = NULL,
  data = NULL,
  formula = NULL,
  model_args = NULL,
  do_gc = NULL,
  imp_one_rep = NULL,
  model_fun = NULL,
  pred_fun = NULL,
  imp_variables = NULL,
  imp_permutations = NULL,
  imp_sample_from = "test",
  err_fun = NULL,
  importance = NULL,
  current_res = NULL,
  current_impo = NULL,
  pred_args = NULL,
  progress = NULL,
  mode_fold = "sequential",
  pooled_obs_train = NULL,
  pooled_obs_test = NULL,
  pooled_pred_train = NULL,
  response = NULL,
  pooled_pred_test = NULL,
  test_fun = NULL,
  test_param = NULL,
  train_fun = NULL,
  train_param = NULL,
  coords = NULL,
  i = NULL
)

Perform spatial error estimation and variable importance assessment

Description

sperrorest is a flexible interface for multiple types of parallelized spatial and non-spatial cross-validation and bootstrap error estimation and parallelized permutation-based assessment of spatial variable importance.

Usage

sperrorest(
  formula,
  data,
  coords = c("x", "y"),
  model_fun,
  model_args = list(),
  pred_fun = NULL,
  pred_args = list(),
  smp_fun = partition_cv,
  smp_args = list(),
  train_fun = NULL,
  train_param = NULL,
  test_fun = NULL,
  test_param = NULL,
  err_fun = err_default,
  imp_variables = NULL,
  imp_permutations = 1000,
  imp_sample_from = c("test", "train", "all"),
  importance = !is.null(imp_variables),
  distance = FALSE,
  do_gc = 1,
  progress = "all",
  benchmark = FALSE,
  mode_rep = c("future", "sequential", "loop"),
  mode_fold = c("sequential", "future", "loop"),
  verbose = 0
)

Arguments

formula

A formula specifying the variables used by the model. Only simple formulas without interactions or nonlinear terms should be used, e.g. y~x1+x2+x3 but not y~x1*x2+log(x3). Formulas involving interaction and nonlinear terms may possibly work for error estimation but not for variable importance assessment, but should be used with caution. The formula y~... is not supported, but y~1 (i.e. no predictors) is.

data

a data.frame with predictor and response variables. Training and test samples will be drawn from this data set by train_fun and test_fun, respectively.

coords

vector of length 2 defining the variables in data that contain the x and y coordinates of sample locations.

model_fun

Function that fits a predictive model, such as glm or rpart. The function must accept at least two arguments, the first one being a formula and the second a data.frame with the learning sample.

model_args

Arguments to be passed to model_fun (in addition to the formula and data argument, which are provided by sperrorest)

pred_fun

Prediction function for a fitted model object created by model. Must accept at least two arguments: the fitted object and a data.frame newdata with data on which to predict the outcome.

pred_args

(optional) Arguments to pred_fun (in addition to the fitted model object and the newdata argument, which are provided by sperrorest).

smp_fun

A function for sampling training and test sets from data. E.g. partition_kmeans for spatial cross-validation using spatial k-means clustering.

smp_args

(optional) Arguments to be passed to smp_fun.

train_fun

(optional) A function for resampling or subsampling the training sample in order to achieve, e.g., uniform sample sizes on all training sets, or maintaining a certain ratio of positives and negatives in training sets. E.g. resample_uniform or resample_strat_uniform.

train_param

(optional) Arguments to be passed to resample_fun.

test_fun

(optional) Like train_fun but for the test set.

test_param

(optional) Arguments to be passed to test_fun.

err_fun

A function that calculates selected error measures from the known responses in data and the model predictions delivered by pred_fun. E.g. err_default (the default).

imp_variables

(optional; used if importance = TRUE). Variables for which permutation-based variable importance assessment is performed. If importance = TRUE and imp_variables == NULL, all variables in formula will be used.

imp_permutations

(optional; used if importance = TRUE). Number of permutations used for variable importance assessment.

imp_sample_from

(default: "test"): specified if the permuted feature values should be taken from the test set, the training set (a rather unlikely choice), or the entire sample ("all"). The latter is useful in leave-one-out resampling situations where the test set is simply too small to perform any kind of resampling. In any case importances are always estimates on the test set. (Note that resampling with replacement is used if the test set is larger than the set from which the permuted values are to be taken.)

importance

logical (default: FALSE): perform permutation-based variable importance assessment?

distance

logical (default: FALSE): if TRUE, calculate mean nearest-neighbour distances from test samples to training samples using add.distance.represampling.

do_gc

numeric (default: 1): defines frequency of memory garbage collection by calling gc; if ⁠< 1⁠, no garbage collection; if ⁠>= 1⁠, run a gc after each repetition; if ⁠>= 2⁠, after each fold.

progress

character (default: all): Whether to show progress information (if possible). Default shows repetition, fold and (if enabled) variable importance progress. Set to "rep" for repetition information only or FALSE for no progress information.

benchmark

(optional) logical (default: FALSE): if TRUE, perform benchmarking and return sperrorestbenchmark object.

mode_rep, mode_fold

character (default: "future" and "sequential", respectively): specifies whether to parallelize the execution at the repetition level, at the fold level, or not at all. Parallel execution uses future.apply::future_lapply() (see details below). It is only possible to parallelize at the repetition level or at the fold level. The "loop" option uses a for loop instead of an lappy function; this option is for debugging purposes.

verbose

Controls the amount of information printed while processing. Defaults to 0 (no output).

Details

Custom predict functions passed to pred_fun, which consist of multiple child functions, must be defined in one function.

Value

A list (object of class sperrorest) with (up to) six components:

error_rep: sperrorestreperror containing predictive performances at the repetition level
error_fold: sperroresterror object containing predictive performances at the fold level
represampling: represampling object
importance: sperrorestimportance object containing permutation-based variable importances at the fold level
benchmark: sperrorestbenchmark object containing information on the system the code is running on, starting and finishing times, number of available CPU cores and runtime performance
package_version: sperrorestpackageversion object containing information about the sperrorest package version

Parallelization

Running in parallel is supported via package future. Have a look at vignette("future-1-overview", package = "future"). In short: Choose a backend and specify the number of workers, then call sperrorest() as usual. Example:

future::plan(future.callr::callr, workers = 2)
sperrorest()

Parallelization at the repetition is recommended when using repeated cross-validation. If the 'granularity' of parallelized function calls is too fine, the overall runtime will be very poor since the overhead for passing arguments and handling environments becomes too large. Use fold-level parallelization only when the processing time of individual folds is very large and the number of repetitions is small or equals 1.

Note that nested calls to future are not possible. Therefore a sequential sperrorest call should be used for hyperparameter tuning in a nested cross-validation.

References

Brenning, A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6), 853-862. doi:10.5194/nhess-5-853-2005

Brenning, A., S. Long & P. Fieguth. 2012. Detecting rock glacier flow structures using Gabor filters and IKONOS imagery. Remote Sensing of Environment, 125, 227-237. doi:10.1016/j.rse.2012.07.005

Examples


## ------------------------------------------------------------
## Classification tree example using non-spatial partitioning
## ------------------------------------------------------------

# Muenchow et al. (2012), see ?ecuador
fo <- slides ~ dem + slope + hcurv + vcurv + log.carea + cslope

library(rpart)
mypred_part <- function(object, newdata) predict(object, newdata)[, 2]
ctrl <- rpart.control(cp = 0.005) # show the effects of overfitting
# show the effects of overfitting
fit <- rpart(fo, data = ecuador, control = ctrl)

### Non-spatial cross-validation:
mypred_part <- function(object, newdata) predict(object, newdata)[, 2]
nsp_res <- sperrorest(
  data = ecuador, formula = fo,
  model_fun = rpart,
  model_args = list(control = ctrl),
  pred_fun = mypred_part,
  progress = TRUE,
  smp_fun = partition_cv,
  smp_args = list(repetition = 1:2, nfold = 3)
)
summary(nsp_res$error_rep)
summary(nsp_res$error_fold)
summary(nsp_res$represampling)
# plot(nsp_res$represampling, ecuador)

### Spatial cross-validation:
sp_res <- sperrorest(
  data = ecuador, formula = fo,
  model_fun = rpart,
  model_args = list(control = ctrl),
  pred_fun = mypred_part,
  progress = TRUE,
  smp_fun = partition_kmeans,
  smp_args = list(repetition = 1:2, nfold = 3)
)
summary(sp_res$error_rep)
summary(sp_res$error_fold)
summary(sp_res$represampling)
# plot(sp_res$represampling, ecuador)

smry <- data.frame(
  nonspat_training = unlist(summary(nsp_res$error_rep,
    level = 1
  )$train_auroc),
  nonspat_test = unlist(summary(nsp_res$error_rep,
    level = 1
  )$test_auroc),
  spatial_training = unlist(summary(sp_res$error_rep,
    level = 1
  )$train_auroc),
  spatial_test = unlist(summary(sp_res$error_rep,
    level = 1
  )$test_auroc)
)
boxplot(smry,
  col = c("red", "red", "red", "green"),
  main = "Training vs. test, nonspatial vs. spatial",
  ylab = "Area under the ROC curve"
)

title Summary statistics for a resampling objects

Description

Calculates sample sizes of training and test sets within repetitions and folds of a resampling or represampling object.

Usage

## S3 method for class 'represampling'
summary(object, ...)

## S3 method for class 'resampling'
summary(object, ...)

Arguments

object

A resampling or represampling object.

...

currently ignored.

Value

A list of data.frames summarizing the sample sizes of training and test sets in each fold of each repetition.

Summarize error statistics obtained by sperrorest

Description

summary.sperroresterror calculates mean, standard deviation, median etc. of the calculated error measures at the specified level (overall, repetition, or fold). summary.sperrorestreperror does the same with the pooled error, at the overall or repetition level.

Usage

## S3 method for class 'sperroresterror'
summary(object, level = 0, pooled = TRUE, na.rm = TRUE, ...)

Arguments

object

sperroresterror resp. sperrorestcombinederror error object calculated by sperrorest

level

Level at which errors are summarized: 0: overall (i.e. across all repetitions); 1: repetition; 2: fold

pooled

If TRUE (default), mean and standard deviation etc are calculated between fold-level error estimates. If FALSE, apply first a weighted.mean among folds before calculating mean, standard deviation etc among repetitions. See also Details.

na.rm

Remove NA values? See mean etc.

...

additional arguments (currently ignored)

Details

Let's use an example to explain the error_rep argument. E.g., assume we are using 100-repeated 10-fold cross-validation. If error_rep = TRUE (default), the mean and standard deviation calculated when summarizing at level = 0 are calculated across the error estimates obtained for each of the 100*10 = 1000 folds. If error_rep = FALSE, mean and standard deviation are calculated across the 100 repetitions, using the weighted average of the fold-level errors to calculate an error value for the entire sample. This will essentially not affect the mean value but of course the standard deviation of the error.

error_rep = FALSE is not recommended, it is mainly for testing purposes; when the test sets are small (as in leave-one-out cross-validation, in the extreme case), consider running sperrorest with error_rep = TRUE and examine only the error_rep component of its result.

Value

Depending on the level of aggregation, a list or data.frame with mean, and at level 0 also standard deviation, median and IQR of the error measures.

Summarize variable importance statistics obtained by sperrorest

Description

summary.sperrorestimportance calculated mean, standard deviation, median etc. of the calculated error measures at the specified level (overall, repetition, or fold).

Usage

## S3 method for class 'sperrorestimportance'
summary(object, level = 0, na.rm = TRUE, which = NULL, ...)

Arguments

object

sperrorestimportance object calculated by sperrorest called with argument importance = TRUE

level

Level at which errors are summarized: 0: overall (i.e. across all repetitions); 1: repetition; 2: fold

na.rm

Remove NA values? See mean etc.

which

optional character vector specifying selected variables for which the importances should be summarized

...

additional arguments (currently ignored)

Value

a list or data.frame, depending on the level of aggregation

Summary and print methods for sperrorest results

Description

Summary methods provide varying level of detail while print methods provide full details.

Usage

## S3 method for class 'sperrorestreperror'
summary(object, level = 0, na.rm = TRUE, ...)

## S3 method for class 'sperrorest'
summary(object, ...)

## S3 method for class 'sperrorestimportance'
print(x, ...)

## S3 method for class 'sperroresterror'
print(x, ...)

## S3 method for class 'sperrorestreperror'
print(x, ...)

## S3 method for class 'sperrorest'
print(x, ...)

## S3 method for class 'sperrorestbenchmarks'
print(x, ...)

## S3 method for class 'sperrorestpackageversion'
print(x, ...)

Arguments

object

a sperrorest object

level

Level at which errors are summarized: 0: overall (i.e. across all repetitions); 1: repetition; 2: fold

na.rm

Remove NA values? See mean etc.

...

additional arguments for summary.sperroresterror or summary.sperrorestimportance

x

Depending on method, a sperrorest, sperroresterror or sperrorestimportance object

Determine the names of neighbouring tiles in a rectangular pattern

Description

This based on 'counting' up and down based on the tile name.

Usage

tile_neighbors(nm, tileset, iterate = 0, diagonal = FALSE)

Arguments

nm

Character string or factor: name of a tile, e.g., 'X4:Y6'

tileset

Admissible tile names; if missing and nm is a factor variable, then levels(nm) is used as a default for tileset.

iterate

internal - do not change default: to control behaviour in an interactive call to this function.

diagonal

if TRUE, diagonal neighbours are also considered neighbours.

Value

Character string.

transfer_parallel_output

Description

transfers output of parallel calls to runreps

Usage

transfer_parallel_output(
  my_res = NULL,
  res = NULL,
  impo = NULL,
  pooled_error = NULL
)

Spatial Error Estimation and Variable Importance

Description

References

Add distance information to resampling objects

Description

Usage

Arguments

Details

Value

See Also

Examples

Resampling objects with repetition, i.e. sets of partitionings or bootstrap samples

Description

Usage

Arguments

Details

Value

See Also

Examples

Resampling objects such as partitionings or bootstrap samples

Description

Usage

Arguments

Details

Value

See Also

Examples

Alphanumeric tile names

Description

Usage

Arguments

Value

See Also

Examples

Calculate mean nearest-neighbour distance between point datasets

Description

Usage

Arguments

Details

Value

See Also

Examples

J. Muenchow's Ecuador landslide data set

Description

Format

References

Examples

Default error function

Description

Usage

Arguments

Value

Note

See Also

Examples

Identify small partitions that need to be fixed.

Description

Usage

Arguments

Value

See Also

Examples

Fruit-tree crop classification: the Maipo dataset

Description

Format

References

Partition the data for a (non-spatial) cross-validation

Description

Usage

Arguments

Details

Value

See Also

Examples

Partition the data for a stratified (non-spatial) cross-validation

Description

Usage

Arguments

Value

See Also