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 all columns used to fit
the model are present.

Augment will often behavior different depending on whether `data`

or
`newdata`

is specified. 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 some 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. At this time, tibbles do not
support matrix-columns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that `splines::ns()`

, `stats::poly()`

and
`survival::Surv()`

objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.

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.

# S3 method for drc augment(x, data = NULL, newdata = NULL, se_fit = FALSE, conf.int = FALSE, conf.level = 0.95, ...)

x | A |
---|---|

data | A |

newdata | A |

se_fit | Logical indicating whether or not a |

conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |

conf.level | The confidence level to use for the confidence interval
if |

... | Additional arguments. Not used. Needed to match generic
signature only. |

Other drc tidiers: `glance.drc`

,
`tidy.drc`

A `tibble::tibble()`

with columns:

Upper bound on confidence interval for fitted values.

Lower bound on confidence interval for fitted values.

Cooks distance.

Fitted or predicted value.

The difference between fitted and observed values.

Standard errors of fitted values.

library(drc)#>#> #>#>#> #>#> #>#>#>#> #>#>#> #>mod <- drm(dead/total~conc, type, weights = total, data = selenium, fct = LL.2(), type = "binomial") tidy(mod)#> # A tibble: 8 x 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#> # A tibble: 8 x 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 NA NA #> 2 b 2 -0.843 0.139 -6.06 1.35e- 9 NA NA #> 3 b 3 -2.16 0.138 -15.7 1.65e-55 NA NA #> 4 b 4 -1.45 0.169 -8.62 3.41e-18 NA NA #> 5 e 1 252. 13.8 18.2 1.16e-74 NA NA #> 6 e 2 378. 39.4 9.61 3.53e-22 NA NA #> 7 e 3 120. 5.91 20.3 1.14e-91 NA NA #> 8 e 4 88.8 8.62 10.3 3.28e-25 NA NAglance(mod)#> # A tibble: 1 x 4 #> AIC BIC logLik df.residual #> <dbl> <dbl> <dbl> <int> #> 1 768. 778. -376. 17#> # A tibble: 25 x 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 #> # … with 15 more rows