fitted.SSN2 {SSN2}

Extract model fitted values

Description

Extract fitted values from fitted model objects. fitted.values is an alias.

Usage

## S3 method for class 'ssn_lm'
fitted(object, type = "response", ...)

## S3 method for class 'ssn_lm'
fitted.values(object, type = "response", ...)

## S3 method for class 'ssn_glm'
fitted(object, type = "response", ...)

## S3 method for class 'ssn_glm'
fitted.values(object, type = "response", ...)

Arguments

object

A fitted model object from ssn_lm() or ssn_glm().

type

"response" for fitted values of the response, "tailup" for fitted values of the tailup random errors, "taildown" for fitted values of the taildown random errors, "euclid" for fitted values of the Euclidean random errors, "nugget" for fitted values of the nugget random errors, or "randcov" for fitted values of the random effects. If from ssn_glm(), "link" for fitted values on the link scale. The default is "response".

...

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

Details

When type is "response", the fitted values for each observation are the standard fitted values X \hat{\beta}. When type is "tailup", "taildown", "euclid", or "nugget" the fitted values for each observation are (generally) the best linear unbiased predictors of the respective random error. When type is "randcov", the fitted values for each level of each random effect are (generally) the best linear unbiased predictors of the corresponding random effect. The fitted values for type "tailup", "taildown", "euclid", "nugget", and "randcov" can generally be used to check assumptions for each component of the fitted model object (e.g., check a Gaussian assumption).

If from ssn_glm(), when type is "response", the fitted values for each observation are the standard fitted values on the inverse link scale: g^{-1}(X \hat{\beta} + \nu), where g(.) is a link function, \beta are the fixed effects, and \nu are the spatial and random effects.

Value

The fitted values according to type.

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, overwrite = TRUE)

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