predict.SSN2: Model predictions (Kriging)

In SSN2: Spatial Modeling on Stream Networks

Model predictions (Kriging)

Description

Predicted values and intervals based on a fitted model object.

Usage

## S3 method for class 'ssn_lm'
predict(
  object,
  newdata,
  se.fit = FALSE,
  scale = NULL,
  df = Inf,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  type = c("response", "terms"),
  block = FALSE,
  local,
  terms = NULL,
  na.action = na.fail,
  ...
)

## S3 method for class 'ssn_glm'
predict(
  object,
  newdata,
  type = c("link", "response", "terms"),
  se.fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  dispersion = NULL,
  terms = NULL,
  local,
  var_correct = TRUE,
  newdata_size,
  na.action = na.fail,
  ...
)

Arguments

object	A fitted model object from ssn_lm() or ssn_glm().
newdata	A character vector that indicates the name of the prediction data set for which predictions are desired (accessible via object$ssn.object$preds). Note that the prediction data must be in the original SSN object used to fit the model. If newdata is omitted, predictions for all prediction data sets are returned. Note that the name ".missing" indicates the prediction data set that contains the missing observations in the data used to fit the model.
se.fit	A logical indicating if standard errors are returned. The default is FALSE.
scale	A numeric constant by which to scale the regular standard errors and intervals. Similar to but slightly different than scale for stats::predict.lm(), because predictions form a spatial model may have different residual variances for each observation in newdata. The default is NULL, which returns the regular standard errors and intervals.
df	Degrees of freedom to use for confidence or prediction intervals (ignored if scale is not specified). The default is Inf.
interval	Type of interval calculation. The default is "none". Other options are "confidence" (for confidence intervals) and "prediction" (for prediction intervals).
level	Tolerance/confidence level. The default is 0.95.
type	The scale (response or link) of predictions obtained using ssn_glm objects.
block	A logical indicating whether a block prediction over the entire region in newdata should be returned. The default is FALSE, which returns point predictions for each location in newdata. Currently only available for model fit using ssn_lm() or models fit using ssn_glm() where family is "gaussian".
local	A optional logical or list controlling the big data approximation. If omitted, local is set to TRUE or FALSE based on the observed data sample size (i.e., sample size of the fitted model object) – if the sample size exceeds 10,000, local is set to TRUE, otherwise it is set to FALSE. This default behavior occurs because main computational burden of the big data approximation depends almost exclusively on the observed data sample size, not the number of predictions desired (which we feel is not intuitive at first glance). If local is FALSE, no big data approximation is implemented. If a list is provided, the following arguments detail the big data approximation: method: The big data approximation method. If method = "all", all observations are used and size is ignored. If method = "covariance", the size data observations having the average highest covariance with the prediction locations are used. The default is "covariance". Only used with models fit using ssn_lm(). size: The number of data observations to use when method is "distance" or "covariance". The default is 4000. Only used with models fit using ssn_lm(). parallel: If TRUE, parallel processing via the parallel package is automatically used. This can significantly speed up computations even when method = "all" (i.e., no big data approximation is used), as predictions are spread out over multiple cores. The default is FALSE. ncores: If parallel = TRUE, the number of cores to parallelize over. The default is the number of available cores on your machine. When local is a list, at least one list element must be provided to initialize default arguments for the other list elements. If local is TRUE, defaults for local are chosen such that local is transformed into list(size = 4000, method = "covariance", parallel = FALSE).
terms	If type is "terms", the type of terms to be returned, specified via either numeric position or name. The default is all terms are included.
na.action	Missing (NA) values in newdata will return an error and should be removed before proceeding.
...	Other arguments. Not used (needed for generic consistency).
dispersion	The dispersion of assumed when computing the prediction standard errors for ssn_glm() model objects when family is "nbinomial", "beta", "Gamma", or "inverse.gaussian". If omitted, the model object dispersion parameter is used.
var_correct	A logical indicating whether to return the corrected prediction variances when predicting via models fit using ssn_glm. The default is TRUE.
newdata_size	The size value for each observation in newdata used when predicting for the binomial family.

Details

The (empirical) best linear unbiased predictions (i.e., Kriging predictions) at each site are returned when interval is "none" or "prediction" alongside standard errors. Prediction intervals are also returned if interval is "prediction". When interval is "confidence", the estimated mean is returned alongside standard errors and confidence intervals for the mean.

Value

If se.fit is FALSE, predict.ssn() returns a vector of predictions or a matrix of predictions with column names fit, lwr, and upr if interval is "confidence" or "prediction". If se.fit is TRUE, a list with the following components is returned:

• fit: vector or matrix as above

• se.fit: standard error of each fit

Examples

# Copy the mf04p .ssn data to a local directory and read it into R
# When modeling with your .ssn object, you will load it using the relevant
# path to the .ssn data on your machine
copy_lsn_to_temp()
temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
mf04p <- ssn_import(temp_path, predpts = "pred1km", overwrite = TRUE)

ssn_mod <- ssn_lm(
  formula = Summer_mn ~ ELEV_DEM,
  ssn.object = mf04p,
  tailup_type = "exponential",
  additive = "afvArea"
)
predict(ssn_mod, "pred1km")

SSN2 documentation built on Nov. 5, 2025, 7:25 p.m.