loocv: Perform leave-one-out cross validation

In spmodel: Spatial Statistical Modeling and Prediction

Perform leave-one-out cross validation

Description

Perform leave-one-out cross validation with options for computationally efficient approximations for big data.

Usage

loocv(object, ...)

## S3 method for class 'splm'
loocv(
  object,
  cv_predict = FALSE,
  se.fit = FALSE,
  local,
  interval = c("none", "prediction"),
  level = 0.95,
  ...
)

## S3 method for class 'spautor'
loocv(
  object,
  cv_predict = FALSE,
  se.fit = FALSE,
  local,
  interval = c("none", "prediction"),
  level = 0.95,
  ...
)

## S3 method for class 'spglm'
loocv(
  object,
  cv_predict = FALSE,
  type = c("link", "response"),
  se.fit = FALSE,
  delta = FALSE,
  local,
  ...
)

## S3 method for class 'spgautor'
loocv(
  object,
  cv_predict = FALSE,
  type = c("link", "response"),
  se.fit = FALSE,
  delta = FALSE,
  local,
  ...
)

Arguments

object	A fitted model object from splm(), spautor(), spglm(), or spgautor().
...	Other arguments. Not used (needed for generic consistency).
cv_predict	A logical indicating whether the leave-one-out fitted values should be returned. Defaults to FALSE. If object is from spglm() or spgautor(), the fitted values returned are on the link scale.
se.fit	A logical indicating whether the leave-one-out prediction standard errors should be returned. Defaults to FALSE. If object is from spglm() or spgautor(), the standard errors correspond to the fitted values returned on the link scale.
local	A list or logical. If a list, specific list elements described in predict.spmodel() control the big data approximation behavior. If a logical, TRUE chooses default list elements for the list version of local as specified in predict.spmodel(). Defaults to FALSE, which performs exact computations.
interval	Whether to also report empirical leave-one-out prediction interval coverage in the returned fit statistics. "none" (the default) omits it; "prediction" reports it (see Details). Only available for splm()/spautor() objects.
level	The prediction interval level (e.g. 0.95) used to compute prediction interval coverage when interval = "prediction". Ignored otherwise. The default is 0.95.
type	The scale (response or link) of predictions obtained when cv_predict = TRUE and using spglm() or spgautor objects.
delta	A logical indicating whether to return delta method standard errors on the response scale when se.fit = TRUE and type = "response". The default is FALSE.

Details

Each observation is held-out from the data set and the remaining data are used to make a prediction for the held-out observation. This is compared to the true value of the observation and several fit statistics are (sometimes optionally) computed: bias, mean-squared-prediction error (MSPE), root-mean-squared-prediction error (RMSPE), and the squared correlation (cor2) between the observed data and leave-one-out predictions (regarded as a prediction version of r-squared appropriate for comparing across spatial and nonspatial models), and prediction interval coverage (cover.XX). Generally, bias should be near zero and prediction interval coverage at the intended level for well-fitting models. The lower the MSPE and RMSPE, the better the model fit (according to the leave-out-out criterion). The higher the cor2, the better the model fit (according to the leave-out-out criterion). cor2 and cover.XX are not returned when object was fit using spglm() or spgautor() because we do not observe the underlying latent mean.

Value

If cv_predict = FALSE and se.fit = FALSE, a fit statistics tibble (with bias, MSPE, RMSPE, and cor2; see Details). If cv_predict = TRUE or se.fit = TRUE, a list with elements: stats, a fit statistics tibble (with bias, MSPE, RMSPE, and cor2; see Details); cv_predict, a numeric vector with leave-one-out predictions for each observation (if cv_predict = TRUE); and se.fit, a numeric vector with leave-one-out prediction standard errors for each observation (if se.fit = TRUE). When object is from splm() or spautor() and interval = "prediction", the fit statistics tibble also has a cover.XX column (e.g. cover.95 for level = 0.95; see Details).

Examples

spmod <- splm(z ~ water + tarp,
  data = caribou,
  spcov_type = "exponential", xcoord = x, ycoord = y
)
loocv(spmod)
loocv(spmod, cv_predict = TRUE, se.fit = TRUE)

spmodel documentation built on Sept. 11, 2026, 1:07 a.m.