predict.SSN2 {SSN2}

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,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  block = FALSE,
  ...
)

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

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.

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.

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".

...

Other arguments. Not used (needed for generic consistency).

type

The scale (response or link) of predictions obtained using ssn_glm objects.

newdata_size

The size value for each observation in newdata used when predicting for the binomial family.

var_correct

A logical indicating whether to return the corrected prediction variances when predicting via models fit using ssn_glm. The default is TRUE.

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")