Package {spconform}

Type: Package
Title: Conformal Prediction for Spatially and Spatio-Temporally Dependent Data
Version: 0.1.2
Date: 2026-10-10
Description: Provides distribution-free, model-agnostic prediction intervals for spatially and spatio-temporally dependent data using localized conformal calibration. Implements locally weighted split conformal prediction for geostatistical (point-referenced) data based on spatial-distance kernels, and a neighbourhood-weighted conformal procedure for areal (lattice) data based on graph adjacency structures. Relaxes the standard exchangeability assumption using spatial proximity, following the localized conformal framework of Mao, Martin and Reich (2024) <doi:10.1080/01621459.2022.2147531>. Includes comprehensive spatial diagnostic tools to audit empirical coverage, conditional spatial strata, and boundary proximity effects.
License: GPL-3
URL: https://amjed-droid.github.io/spconform/, https://github.com/amjed-droid/spconform
BugReports: https://github.com/amjed-droid/spconform/issues
Encoding: UTF-8
Depends: R (≥ 3.6.0)
Imports: stats
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown, sp
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-10-19 11:28:40 UTC; intel
Author: Ahmed Sattar Jabbar ORCID iD [aut, cre]
Maintainer: Ahmed Sattar Jabbar <ahmed.state.me@gmail.com>
Repository: CRAN
Date/Publication: 2026-10-19 12:00:02 UTC

spconform: Conformal Prediction for Spatially and Spatio-Temporally Dependent Data

Description

Provides distribution-free, model-agnostic prediction intervals for spatially and spatio-temporally dependent data using localized conformal calibration, fast k-NN proximity calculations, and comprehensive spatial diagnostics.

Author(s)

Maintainer: Ahmed Sattar Jabbar ahmed.state.me@gmail.com (ORCID)

Authors:

References

Mao, H., Martin, R., and Reich, B. J. (2024). Valid Conformal Prediction for Dependent Data. Journal of the American Statistical Association, 119(546), 1269–1281. doi:10.1080/01621459.2022.2147531.

Winkler, R. L. (1972). A Decision-Theoretic Approach to Interval Estimation. Journal of the American Statistical Association, 67(337), 187–191.

Susmann, H. P. (2024). Adaptive Conformal Prediction for Spatial Data. Computo, doi:10.57750/edan-5f53.

Shafer, G. and Vovk, V. (2008). A Tutorial on Conformal Prediction. Journal of Machine Learning Research, 9, 371–421.

See Also

Useful links:

Areal (Lattice) Graph Distance Proximity Weights

Description

Computes neighbourhood-based weights for areal (lattice) data based on shortest-path graph distances.

Usage

areal_neighbor_weights(i0, adjacency, decay = 1)

Arguments

i0

Integer index of the target (unobserved / held-out) areal unit.

adjacency

A square 0/1 (or weighted) adjacency matrix describing the neighbourhood structure of the areal units (e.g. a spatial contiguity matrix). Row/column i0 corresponds to the target unit.

decay

Numeric decay rate applied to graph distance (number of hops) from i0; larger values down-weight distant neighbours more aggressively. Defaults to 1.

Value

A numeric vector of length nrow(adjacency) with weights based on graph distance from i0 (self-weight is 0, i.e. the target unit is excluded from its own calibration set).

Coerce an spconform Object to a Data Frame

Description

Converts a conformal prediction output object of class "spconform" into a tidy data.frame containing coordinates (if present), point predictions, lower/upper bounds, and interval widths.

Usage

## S3 method for class 'spconform'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x

An object of class "spconform".

row.names

NULL or a character vector giving row names.

optional

Logical; passed to as.data.frame.

...

Additional arguments (currently unused).

Value

A data.frame with prediction columns pred, lower, upper, width, and any coordinate columns.

Coerce an spconform_diagnose Object to a Data Frame

Description

Extracts conditional spatial strata and diagnostic statistics as a tidy data frame for programmatic inspection.

Usage

## S3 method for class 'spconform_diagnose'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x

An object of class "spconform_diagnose".

row.names

Optional row names.

optional

Logical; passed to as.data.frame.

...

Additional arguments (unused).

Value

A tidy data.frame of spatial diagnostic strata.

Domain Boundary Proximity Diagnostics for Conformal Prediction

Description

Evaluates whether prediction intervals degrade near the convex-hull boundary of the spatial prediction domain versus interior locations.

Usage

boundary_diagnostics(object, y_true, s_test)

Arguments

object

An object of class "spconform".

y_true

Numeric vector of true observed responses.

s_test

Numeric matrix of spatial coordinates (m \times 2).

Value

An S3 object of class "spconform_boundary" comparing empirical coverage, interval width, and Winkler scores across edge and interior subdomains.

References

Winkler, R. L. (1972). A Decision-Theoretic Approach to Interval Estimation. Journal of the American Statistical Association, 67(337), 187–191.

Examples

set.seed(42)
n <- 50
s <- matrix(runif(n * 2), n, 2)
y <- sin(3 * s[, 1]) + rnorm(n, sd = 0.2)
pfun <- function(s_tr, y_tr, s_new) rep(mean(y_tr), nrow(s_new))
fit <- scp_geostatistical(s[1:35, ], y[1:35], s[36:50, ], pfun, alpha = 0.1)
b_res <- boundary_diagnostics(fit, y[36:50], s[36:50, ])
print(b_res)

Empirical Coverage and Average Interval Width for an spconform Object

Description

Evaluates empirical coverage, average prediction interval width, and the strictly proper Winkler interval score for an "spconform" object.

Usage

coverage_report(object, y_true, na.rm = TRUE)

Arguments

object

An object of class "spconform".

y_true

Numeric vector of true observed values at the prediction locations, same length/order as object$pred.

na.rm

Logical; if TRUE, missing values in y_true are removed before computing evaluation metrics. Defaults to TRUE.

Value

A named list with:

coverage

Proportion of observations falling within [lower, upper]

.

mean_width

Average prediction interval width

.

winkler_score

Mean Winkler interval score across evaluation points

.

References

Winkler, R. L. (1972). A Decision-Theoretic Approach to Interval Estimation. Journal of the American Statistical Association, 67(337), 187–191.

Examples

set.seed(1)
n <- 20
adj <- matrix(0, n, n)
for (i in 1:(n - 1)) { adj[i, i + 1] <- 1; adj[i + 1, i] <- 1 }
y <- cumsum(rnorm(n))
out <- scp_areal(y, adjacency = adj, alpha = 0.2)
coverage_report(out, y)

Comprehensive Diagnostic Suite for Spatial Conformal Prediction Objects

Description

Produces a comprehensive multi-panel diagnostic report evaluating marginal coverage validity, Winkler Interval Score (WIS) sharpness, conditional coverage across spatial strata, boundary proximity effects, spatial residual autocorrelation (Moran's I), and nonconformity score distributions.

Usage

diagnose(object, y_true, s_test = NULL, n_bins = 4, plot = TRUE, ...)

Arguments

object

An object of class "spconform", typically output from scp_geostatistical or scp_areal.

y_true

Numeric vector of true observed responses at target prediction locations. Must have the same length as object$pred.

s_test

Optional numeric matrix of target prediction coordinates (m \times p). Required for spatial stratification, boundary effects, and spatial autocorrelation diagnostics.

n_bins

Integer; number of spatial bins per dimension for conditional coverage (default 4).

plot

Logical; if TRUE (default), generates a multi-panel diagnostic plot.

...

Additional graphical parameters passed to internal plotting methods.

Details

The diagnostic suite coordinates modular audits across the prediction intervals:

  1. Marginal Validity & Sharpness: Evaluates empirical coverage and the strictly proper Winkler Interval Score (WIS; Winkler 1972).

  2. Conditional Spatial Stratification: Calls strata_diagnostics to evaluate local coverage across quadrants.

  3. Convex Hull Boundary Proximity: Calls boundary_diagnostics to test whether edge-effect extrapolations degrade calibration.

  4. Spatial Autocorrelation (Moran's I): Calls spatial_moran to test for residual spatial dependence.

  5. Score Distribution: Analyzes empirical quantiles and normality/QQ structure.

Value

An S3 object of class "spconform_diagnose" containing:

marginal

List with coverage, mean width, median width, sd width, Winkler score, and sample size.

conditional

Data frame of coverage and widths partitioned by spatial quadrant (class "spconform_strata").

boundary

Object of class "spconform_boundary" comparing edge vs interior units.

spatial_autocorr

Object of class "spconform_moran" with Moran's I statistics.

scores

Summary of nonconformity score moments and quantiles.

raw_scores

Numeric vector of nonconformity scores.

alpha

Miscoverage level of the evaluated object.

References

Winkler, R. L. (1972). A Decision-Theoretic Approach to Interval Estimation. Journal of the American Statistical Association, 67(337), 187–191.

Mao, H., Martin, R., and Reich, B. J. (2024). Valid Conformal Prediction for Dependent Data. Journal of the American Statistical Association, 119(546), 1269–1281. doi:10.1080/01621459.2022.2147531.

Susmann, H. P. (2024). Adaptive Conformal Prediction for Spatial Data. Computo, doi:10.57750/edan-5f53.

See Also

spatial_moran, boundary_diagnostics, strata_diagnostics, scp_geostatistical, scp_areal

Examples

set.seed(42)
n <- 100
s_tr <- matrix(runif(n * 2), n, 2)
y_tr <- sin(3 * s_tr[, 1]) + rnorm(n, sd = 0.2)
s_te <- matrix(runif(30 * 2), 30, 2)
y_te <- sin(3 * s_te[, 1]) + rnorm(30, sd = 0.2)

pfun <- function(s_tr, y_tr, s_new) rep(mean(y_tr), nrow(s_new))
out <- scp_geostatistical(s_tr, y_tr, s_te, pfun, alpha = 0.1)

diag_res <- diagnose(out, y_true = y_te, s_test = s_te, plot = FALSE)
print(diag_res)

# Programmatic access to modular components
as.data.frame(diag_res)

Plot Prediction Intervals for spconform Objects

Description

Plots point predictions and conformal prediction intervals across observation indices, optionally overlaying true target values for visual evaluation.

Usage

## S3 method for class 'spconform'
plot(x, y_true = NULL, ...)

Arguments

x

An object of class "spconform".

y_true

Optional numeric vector of true observed responses at prediction locations.

...

Further graphical parameters passed to plot.

Value

Invisibly returns the input object x.

Plot Method for spconform_boundary Objects

Description

Plot Method for spconform_boundary Objects

Usage

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

Arguments

x

An object of class "spconform_boundary".

...

Further graphical arguments.

Plot Diagnostic Audit for an spconform_diagnose Object

Description

Generates a multi-panel visual audit of marginal coverage, nonconformity score QQ-distribution, conditional spatial strata coverage, and boundary proximity effects.

Usage

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

Arguments

x

An object of class "spconform_diagnose".

...

Additional arguments passed to plotting functions.

Value

Invisibly returns the input object x.

Plot Method for spconform_strata Objects

Description

Plot Method for spconform_strata Objects

Usage

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

Arguments

x

An object of class "spconform_strata".

...

Further graphical arguments.

Extract Predictions and Intervals from an spconform Object

Description

Extracts point predictions or prediction interval bounds from a fitted spconform object.

Usage

## S3 method for class 'spconform'
predict(object, interval = c("none", "prediction"), ...)

Arguments

object

An object of class "spconform".

interval

Character string specifying the type of prediction intervals to extract: "none" (default, point predictions only) or "prediction" (matrix with fit, lwr, upr).

...

Further arguments passed to or from other methods.

Value

If interval = "none", a numeric vector of point predictions. If interval = "prediction", a numeric matrix with columns fit, lwr, and upr.

Print Method for spconform Objects

Description

Displays a concise summary of the conformal prediction object including model type, nominal target coverage, and a preview table of prediction intervals.

Usage

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

Arguments

x

An object of class "spconform".

...

Further arguments passed to or from other methods.

Value

Invisibly returns the input object x.

Print Method for spconform_boundary Objects

Description

Print Method for spconform_boundary Objects

Usage

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

Arguments

x

An object of class "spconform_boundary".

...

Further arguments (unused).

Print Diagnostic Audit for an spconform_diagnose Object

Description

Print Diagnostic Audit for an spconform_diagnose Object

Usage

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

Arguments

x

An object of class "spconform_diagnose".

...

Further arguments (unused).

Print Method for spconform_moran Objects

Description

Print Method for spconform_moran Objects

Usage

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

Arguments

x

An object of class "spconform_moran".

...

Further arguments (unused).

Print Method for spconform_strata Objects

Description

Print Method for spconform_strata Objects

Usage

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

Arguments

x

An object of class "spconform_strata".

...

Further arguments (unused).

Extract Residuals from an spconform Object

Description

Calculates prediction residuals (observed minus fitted values) or nonconformity scores for an spconform object given true responses.

Usage

## S3 method for class 'spconform'
residuals(object, y_true, type = c("response", "abs"), ...)

Arguments

object

An object of class "spconform".

y_true

Numeric vector of true observed responses at prediction locations.

type

Character string indicating residual type: "response" (raw residuals y - \hat{y}) or "abs" (absolute nonconformity scores |y - \hat{y}|).

...

Further arguments passed to or from other methods.

Value

A numeric vector of residuals.

Neighbourhood-Weighted Conformal Prediction for Areal (Lattice) Data

Description

Constructs distribution-free prediction intervals for areal units (e.g. counties, census tracts, grid cells) using a leave-one-unit-out conformal procedure in which nonconformity scores are weighted by graph (neighbourhood) distance to the held-out unit, via its adjacency structure.

Usage

scp_areal(y, X = NULL, adjacency, pred_fun = NULL, alpha = 0.1, decay = 1)

Arguments

y

Numeric vector of observed responses, one per areal unit.

X

Optional numeric design matrix of covariates (one row per areal unit); passed to pred_fun if supplied. May be NULL for purely spatial (neighbourhood-based) prediction.

adjacency

Square adjacency (contiguity) matrix describing the neighbourhood structure among the length(y) areal units. Non-zero entries are treated as neighbours (coerced to binary).

pred_fun

A function with signature function(y_train, X_train, idx_train, idx_target, adjacency) returning a single numeric point prediction for the areal unit indexed by idx_target, fitted using the training units idx_train. If NULL (default), a simple neighbourhood-mean predictor is used (the mean of y_train over the target's graph neighbours, falling back to the global training mean if the target has no observed neighbours).

alpha

Miscoverage level; intervals target 1-\alpha coverage. Default 0.1.

decay

Decay rate for neighbourhood weights; see areal_neighbor_weights.

Details

Uses a full leave-one-out (jackknife-style) conformal scheme: for each areal unit i, a model is fit on all other units and used to predict unit i; the resulting nonconformity scores across all units are combined into a graph-distance-weighted quantile specific to each target unit, so that the calibration set is dominated by spatially/graph-proximate units rather than treating all units as exchangeable.

If the effective neighbourhood weight for a target unit is vanishingly small (e.g., an isolated unit with no neighbours), the procedure falls back to an unweighted quantile over all other units and issues a warning.

Value

An object of class "spconform" (areal variant) with the same structure as scp_geostatistical, using unit indices in place of coordinates.

References

Mao, H., Martin, R., and Reich, B. J. (2024). Valid Conformal Prediction for Dependent Data. Journal of the American Statistical Association, 119(546), 1269–1281. doi:10.1080/01621459.2022.2147531.

Susmann, H. P. (2024). Adaptive Conformal Prediction for Spatial Data. Computo, doi:10.57750/edan-5f53.

Examples

set.seed(1)
n <- 25
adj <- matrix(0, n, n)
for (i in 1:(n - 1)) { adj[i, i + 1] <- 1; adj[i + 1, i] <- 1 }
y <- cumsum(rnorm(n)) + rnorm(n, sd = 0.2)

out <- scp_areal(y, adjacency = adj, alpha = 0.2)
print(out)

Locally Weighted Split Conformal Prediction for Geostatistical (Point-Referenced) Data

Description

Constructs distribution-free prediction intervals at new spatial (or spatio-temporal) locations by combining split conformal prediction with spatial-distance kernel weights, relaxing the exchangeability assumption that standard conformal prediction relies on.

Usage

scp_geostatistical(
  s_train,
  y_train,
  s0,
  pred_fun,
  alpha = 0.1,
  split = 0.5,
  bandwidth = NULL,
  t_train = NULL,
  t0 = NULL,
  temporal_bandwidth = NULL,
  k_neighbors = NULL,
  seed = NULL
)

Arguments

s_train

Numeric matrix of training coordinates, one row per observation (e.g. longitude/latitude, or projected easting/northing).

y_train

Numeric vector of training responses, same length as nrow(s_train).

s0

Numeric matrix of coordinates at which prediction intervals are desired, one row per target location.

pred_fun

A function with signature function(s_train, y_train, s_new) that returns a numeric vector of point predictions for the locations in s_new, fitted using s_train/y_train. This lets the user plug in any spatial predictor (kriging, random forest, GAM, etc.); spconform handles only the conformal calibration layer.

alpha

Miscoverage level; prediction intervals target 1 - \alpha coverage. Default 0.1 (90 percent intervals).

split

Proportion of the training data used for model fitting; the remainder is used as the calibration set for conformal scores. Default 0.5.

bandwidth

Optional spatial kernel bandwidth passed to spatial_kernel_weights; if NULL, chosen automatically per target location.

t_train, t0, temporal_bandwidth

Optional time indices (and temporal bandwidth) for spatio-temporal weighting; see spatial_kernel_weights.

k_neighbors

Optional integer; number of nearest calibration neighbours to retain for localized calibration. Defaults to NULL (all units).

seed

Optional integer seed for the train/calibration split, for reproducibility.

Details

This implements a localized split-conformal procedure in the spirit of Mao, Martin and Reich (2020, "Valid model-free spatial prediction") and subsequent spatio-temporal extensions: nonconformity scores from a held-out calibration set are combined into a weighted empirical quantile where weights decay with distance (in space, and optionally time) from the target location, so nearby, more relevant observations dominate the calibration of the interval while validity is retained under a local exchangeability argument.

Value

An object of class "spconform", a list with components:

pred

Point predictions at s0.

lower, upper

Lower/upper prediction interval bounds at s0.

alpha

The requested miscoverage level.

s0

The target coordinates.

call

The matched call.

References

Mao, H., Martin, R., and Reich, B. J. (2024). Valid Conformal Prediction for Dependent Data. Journal of the American Statistical Association, 119(546), 1269–1281. doi:10.1080/01621459.2022.2147531.

Shafer, G. and Vovk, V. (2008). A Tutorial on Conformal Prediction. Journal of Machine Learning Research, 9, 371–421.

Examples

set.seed(42)
n <- 200
s <- matrix(runif(2 * n), ncol = 2)
f <- function(s) sin(4 * s[, 1]) + cos(3 * s[, 2])
y <- f(s) + rnorm(n, sd = 0.3)

idx <- sample(n, 150)
s_train <- s[idx, ]; y_train <- y[idx]
s0 <- s[-idx, ][1:10, ]

pred_fun <- function(s_train, y_train, s_new) {
  fit <- lm(y_train ~ s_train[, 1] + s_train[, 2] +
              I(s_train[, 1]^2) + I(s_train[, 2]^2))
  nd <- data.frame(s_train = I(s_new))
  as.numeric(cbind(1, s_new[, 1], s_new[, 2], s_new[, 1]^2, s_new[, 2]^2) %*%
               coef(fit))
}

out <- scp_geostatistical(s_train, y_train, s0, pred_fun, alpha = 0.1, seed = 1)
print(out)

Spatial Gaussian Kernel Proximity Weights

Description

Computes Gaussian-kernel spatial weights between a target location and a set of reference locations.

Usage

spatial_kernel_weights(
  s0,
  s,
  bandwidth = NULL,
  t0 = NULL,
  t = NULL,
  temporal_bandwidth = NULL
)

Arguments

s0

A numeric vector (or 1-row matrix) giving the coordinates of the target (prediction) location.

s

A numeric matrix of coordinates (one row per observation) for the reference (calibration) set.

bandwidth

Positive numeric bandwidth of the Gaussian kernel. If NULL (default), the bandwidth is chosen automatically as the median pairwise distance among s (Silverman-type rule).

t0

Optional numeric scalar: time index of the target observation, for spatio-temporal weighting. If supplied, t must be supplied too.

t

Optional numeric vector: time indices for the reference set, same length as nrow(s).

temporal_bandwidth

Positive numeric bandwidth for the temporal kernel; only used if t0/t are supplied. Defaults to the spatial bandwidth logic applied to t.

Details

Weights are computed as

w_i = \exp(-\|s_0 - s_i\|^2 / (2 h^2))

for the spatial-only case, and multiplied by an analogous temporal kernel term when t0/t are provided. This is the core device used to relax the exchangeability assumption required by standard conformal prediction: observations located near the point to be predicted contribute more to the calibration of the prediction interval than distant ones (Mao, Martin and Reich, 2024).

Value

A numeric vector of weights (not necessarily summing to one), one per row of s, giving higher weight to reference points that are spatially (and, if requested, temporally) closer to s0.

References

Mao, H., Martin, R., and Reich, B. J. (2024). Valid Conformal Prediction for Dependent Data. Journal of the American Statistical Association, 119(546), 1269–1281. doi:10.1080/01621459.2022.2147531.

Examples

set.seed(1)
s <- matrix(runif(20), ncol = 2)
s0 <- c(0.5, 0.5)
w <- spatial_kernel_weights(s0, s)
round(w, 3)

Spatial Autocorrelation Diagnostics (Moran's I) for Conformal Residuals

Description

Evaluates spatial autocorrelation and clustering among prediction residuals and binary miscoverage indicators using Moran's I statistic.

Usage

spatial_moran(object, y_true, s_test)

Arguments

object

An object of class "spconform" or numeric vector of predictions.

y_true

Numeric vector of true observed responses.

s_test

Numeric matrix of spatial coordinates (m \times p).

Value

An S3 object of class "spconform_moran" containing:

moran_i

Observed Moran's I statistic for prediction residuals.

expected

Theoretical expectation -1/(n-1) under spatial independence.

z_score

Standardized normal z-score under the randomization assumption.

p_value

Two-sided p-value against the null hypothesis of spatial randomness.

is_random

Logical; TRUE if p-value >= 0.05.

moran_i_miscoverage

Moran's I on binary hit/miss indicators if available.

References

Cliff, A. D. and Ord, J. K. (1981). Spatial Processes: Models & Applications. Pion, London.

Mao, H., Martin, R., and Reich, B. J. (2024). Valid Conformal Prediction for Dependent Data. Journal of the American Statistical Association, 119(546), 1269–1281. doi:10.1080/01621459.2022.2147531.

Examples

set.seed(42)
n <- 50
s <- matrix(runif(n * 2), n, 2)
y <- sin(3 * s[, 1]) + rnorm(n, sd = 0.2)
pfun <- function(s_tr, y_tr, s_new) rep(mean(y_tr), nrow(s_new))
fit <- scp_geostatistical(s[1:35, ], y[1:35], s[36:50, ], pfun, alpha = 0.1)
m_res <- spatial_moran(fit, y[36:50], s[36:50, ])
print(m_res)

Conditional Spatial Strata Diagnostics for Conformal Prediction

Description

Partitions the spatial domain into coordinate grid strata/quadrants and audits local empirical coverage, interval widths, and Winkler scores.

Usage

strata_diagnostics(object, y_true, s_test, n_bins = 4)

Arguments

object

An object of class "spconform".

y_true

Numeric vector of true observed responses.

s_test

Numeric matrix of spatial coordinates (m \times 2).

n_bins

Integer; number of spatial bins per coordinate axis (default 4).

Value

An S3 object of class "spconform_strata" containing a data frame of strata evaluations and summary metrics.

References

Winkler, R. L. (1972). A Decision-Theoretic Approach to Interval Estimation. Journal of the American Statistical Association, 67(337), 187–191.

Examples

set.seed(42)
n <- 50
s <- matrix(runif(n * 2), n, 2)
y <- sin(3 * s[, 1]) + rnorm(n, sd = 0.2)
pfun <- function(s_tr, y_tr, s_new) rep(mean(y_tr), nrow(s_new))
fit <- scp_geostatistical(s[1:35, ], y[1:35], s[36:50, ], pfun, alpha = 0.1)
s_res <- strata_diagnostics(fit, y[36:50], s[36:50, ], n_bins = 2)
print(s_res)

Summary Method for spconform Objects

Description

Computes and displays summary statistics of the prediction interval widths generated by an spconform object.

Usage

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

Arguments

object

An object of class "spconform".

...

Further arguments passed to or from other methods.

Value

Invisibly returns the input object object.