Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the .fitted column, residuals in the
.resid column, and standard errors for the fitted values in a .se.fit
column. New columns always begin with a . prefix to avoid overwriting
columns in the original dataset.
Users may pass data to augment via either the data argument or the
newdata argument. If the user passes data to the data argument,
it must be exactly the data that was used to fit the model
object. Pass datasets to newdata to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in newdata, then no
.resid column will be included in the output.
Augment will often behave differently depending on whether data or
newdata is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.
For convenience, many augment methods provide default data arguments,
so that augment(fit) will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.
The augmented dataset is always returned as a tibble::tibble with the
same number of rows as the passed dataset. This means that the passed
data must be coercible to a tibble. If a predictor enters the model as part
of a matrix of covariates, such as when the model formula uses
splines::ns(), stats::poly(), or survival::Surv(), it is represented
as a matrix column.
We are in the process of defining behaviors for models fit with various
na.action arguments, but make no guarantees about behavior when data is
missing at this time.
Usage
# S3 method for class 'drc'
augment(
x,
data = NULL,
newdata = NULL,
se_fit = FALSE,
conf.int = FALSE,
conf.level = 0.95,
...
)Arguments
- x
A
drcobject produced by a call todrc::drm().- data
A base::data.frame or
tibble::tibble()containing the original data that was used to produce the objectx. Defaults tostats::model.frame(x)so thataugment(my_fit)returns the augmented original data. Do not pass new data to thedataargument. Augment will report information such as influence and cooks distance for data passed to thedataargument. These measures are only defined for the original training data.- newdata
A
base::data.frame()ortibble::tibble()containing all the original predictors used to createx. Defaults toNULL, indicating that nothing has been passed tonewdata. Ifnewdatais specified, thedataargument will be ignored.- se_fit
Logical indicating whether or not a
.se.fitcolumn should be added to the augmented output. For some models, this calculation can be somewhat time-consuming. Defaults toFALSE.- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
FALSE.- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
Other drc tidiers:
glance.drc(),
tidy.drc()
Value
A tibble::tibble() with columns:
- .cooksd
Cooks distance.
- .fitted
Fitted or predicted value.
- .lower
Lower bound on interval for fitted values.
- .resid
The difference between observed and fitted values.
- .se.fit
Standard errors of fitted values.
- .upper
Upper bound on interval for fitted values.
Examples
# load libraries for models and data
library(drc)
#>
#> 'drc' has been loaded.
#> Please cite R and 'drc' if used for a publication,
#> for references type 'citation()' and 'citation('drc')'.
#>
#> Attaching package: ‘drc’
#> The following objects are masked from ‘package:stats’:
#>
#> gaussian, getInitial
# fit model
mod <- drm(dead / total ~ conc, type,
weights = total, data = selenium, fct = LL.2(), type = "binomial"
)
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 8 × 6
#> term curve estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 b 1 -1.50 0.155 -9.67 2.01e-22
#> 2 b 2 -0.843 0.139 -6.06 1.35e- 9
#> 3 b 3 -2.16 0.138 -15.7 1.65e-55
#> 4 b 4 -1.45 0.169 -8.62 3.41e-18
#> 5 e 1 252. 13.8 18.2 1.16e-74
#> 6 e 2 378. 39.4 9.61 3.53e-22
#> 7 e 3 120. 5.91 20.3 1.14e-91
#> 8 e 4 88.8 8.62 10.3 3.28e-25
tidy(mod, conf.int = TRUE)
#> # A tibble: 8 × 8
#> term curve estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 b 1 -1.50 0.155 -9.67 2.01e-22 -1.81 -1.20
#> 2 b 2 -0.843 0.139 -6.06 1.35e- 9 -1.12 -0.571
#> 3 b 3 -2.16 0.138 -15.7 1.65e-55 -2.43 -1.89
#> 4 b 4 -1.45 0.169 -8.62 3.41e-18 -1.78 -1.12
#> 5 e 1 252. 13.8 18.2 1.16e-74 225. 279.
#> 6 e 2 378. 39.4 9.61 3.53e-22 301. 456.
#> 7 e 3 120. 5.91 20.3 1.14e-91 108. 131.
#> 8 e 4 88.8 8.62 10.3 3.28e-25 71.9 106.
glance(mod)
#> # A tibble: 1 × 4
#> AIC BIC logLik df.residual
#> <dbl> <dbl> <logLik> <int>
#> 1 768. 778. -376.2099 17
augment(mod, selenium)
#> # A tibble: 25 × 7
#> type conc total dead .fitted .resid .cooksd
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 151 3 0 0.0199 0
#> 2 1 100 146 40 0.199 0.0748 0.0000909
#> 3 1 200 116 31 0.414 -0.146 0.000104
#> 4 1 300 159 85 0.565 -0.0302 0.00000516
#> 5 1 400 150 102 0.667 0.0133 0.00000220
#> 6 1 500 140 112 0.737 0.0633 0.0000720
#> 7 2 0 141 2 0 0.0142 0
#> 8 2 100 153 30 0.246 -0.0495 0.000168
#> 9 2 200 142 59 0.369 0.0468 0.0000347
#> 10 2 300 139 82 0.451 0.139 0.0000430
#> # ℹ 15 more rows