augment.SSN2: Augment data with information from fitted model objects

Description

Augment accepts a fitted model object and a data set and adds information about each observation in the data set. New columns always begin with a . prefix to avoid overwriting columns in the original data set.

Augment behaves differently depending on whether the original data or new data requires augmenting. Typically, when augmenting the original data, only the fitted model object is specified, and when augmenting new data, the fitted model object and newdata are specified. When augmenting the original data, diagnostic statistics are augmented to each row in the data set. When augmenting new data, predictions and optional intervals (confidence or prediction) or standard errors are augmented to each row in the new data set.

Usage

# S3 method for ssn_lm
augment(
  x,
  drop = TRUE,
  newdata = NULL,
  se_fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  local,
  ...
)
# S3 method for ssn_glm
augment(
  x,
  drop = TRUE,
  newdata = NULL,
  type.predict = c("link", "response"),
  type.residuals = c("deviance", "pearson", "response"),
  se_fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  newdata_size,
  level = 0.95,
  local = local,
  var_correct = TRUE,
  ...
)

Value

When augmenting the original data set, a tibble with additional columns

.fitted: Fitted value
.resid: Response residual (the difference between observed and fitted values)
.hat: Leverage (diagonal of the hat matrix)
.cooksd: Cook's distance
.std.resid: Standardized residuals
.se.fit: Standard error of the fitted value.

When augmenting a new data set, a tibble with additional columns

.fitted: Predicted (or fitted) value
.lower: Lower bound on interval
.upper: Upper bound on interval
.se.fit: Standard error of the predicted (or fitted) value

When predictions for all prediction objects are desired, the output is a list where each element has a name that matches the prediction objects and values that are the predictions.

Arguments

x: A fitted model object from ssn_lm() or ssn_glm().
drop: A logical indicating whether to drop extra variables in the fitted model object x when augmenting. The default for drop is TRUE. drop is ignored if augmenting newdata.
newdata: A vector that contains the names of the prediction sf objects from the original ssn.object requiring prediction. All of the original explanatory variables used to create the fitted model object x must be present in each prediction sf object represented by newdata. Defaults to NULL, which indicates that nothing has been passed to newdata and augmenting occurs for the original data. The value "ssn" is shorthand for specifying all prediction sf objects.
se_fit: Logical indicating whether or not a .se.fit column should be added to augmented output. Passed to predict() and defaults to FALSE.
interval: Character indicating the type of confidence interval columns to add to the augmented newdata output. Passed to predict() and defaults to "none".
level: Tolerance/confidence level. The default is 0.95.
local: A list or logical. If a list, specific list elements described in predict.ssn_lm() or predict.ssn_glm() control the big data approximation behavior. If a logical, TRUE chooses default list elements for the list version of local as specified in predict.ssn_lm() or predict.ssn_glm(). Defaults to FALSE, which performs exact computations.
...: Additional arguments to predict() when augmenting newdata.
type.predict: The scale (response or link) of fitted values and predictions obtained using ssn_glm() objects.
type.residuals: The residual type (deviance, pearson, or response) of fitted models from ssn_glm() objects. Ignored if newdata is specified.
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

augment() returns a tibble as an sf object.

Missing response values from the original data can be augmented as if they were a newdata object by providing ".missing" to the newdata argument.

Examples

Run this code

# 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 = "CapeHorn", overwrite = TRUE)

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