augment.SSN2 {SSN2} | R Documentation |
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 class 'ssn_lm'
augment(
x,
drop = TRUE,
newdata = NULL,
se_fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
...
)
## S3 method for class 'ssn_glm'
augment(
x,
drop = TRUE,
newdata = NULL,
type = c("link", "response"),
se_fit = FALSE,
interval = c("none", "confidence", "prediction"),
newdata_size,
level = 0.95,
var_correct = TRUE,
...
)
Arguments
x |
|
drop |
A logical indicating whether to drop extra variables in the
fitted model object |
newdata |
A vector that contains the names of the prediction |
se_fit |
Logical indicating whether or not a |
interval |
Character indicating the type of confidence interval columns to
add to the augmented |
level |
Tolerance/confidence level. The default is |
... |
Additional arguments to |
type |
The scale ( |
newdata_size |
The |
var_correct |
A logical indicating whether to return the corrected prediction
variances when predicting via models fit using |
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.
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.
See Also
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 = "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")