loocv function

Perform leave-one-out cross validation

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

loocv(object, ...)

## S3 method for class 'splm'
loocv(object, cv_predict = FALSE, se.fit = FALSE, local, ...)

## S3 method for class 'spautor'
loocv(object, cv_predict = FALSE, se.fit = FALSE, local, ...)

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

## S3 method for class 'spgautor'
loocv(
  object,
  cv_predict = FALSE,
  type = c("link", "response"),
  se.fit = 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.
• type: The scale (response or link) of predictions obtained when cv_predict = TRUE and using spglm() or spgautor objects.

Returns

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).

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 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). Generally, bias should be near zero 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 is not returned when object was fit using spglm() or spgautor(), as it is only applicable here for linear models.

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)