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 rma
augment(x, ...)

## Arguments

x An rma object such as those created by metafor::rma(), metafor::rma.uni(), metafor::rma.glmm(), metafor::rma.mh(), metafor::rma.mv(), or metafor::rma.peto(). 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 pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

## Value

A tibble::tibble() with columns:

.conf.high

Upper bound on confidence interval for fitted values.

.conf.low

Lower bound on confidence interval for fitted values.

.cred.high

Upper bound on credible interval for fitted values.

.cred.low

Lower bound on credible interval for fitted values.

.fitted

Fitted or predicted value.

.moderator

In meta-analysis, the moderators used to calculate the predicted values.

.moderator.level

In meta-analysis, the level of the moderators used to calculate the predicted values.

.resid

The difference between fitted and observed values.

.se.fit

Standard errors of fitted values.

.observed

The observed values for the individual studies

## Examples


#> and introduction to the package please type: help(metafor).#>
#> Attaching package: ‘metafor’#> The following object is masked from ‘package:car’:
#>
#>     vif#> The following object is masked from ‘package:ordinal’:
#>
#>     ranef#> The following object is masked from ‘package:mclust’:
#>
#>     hc
df <-
escalc(
measure = "RR",
ai = tpos,
bi = tneg,
ci = cpos,
di = cneg,
data = dat.bcg
)

meta_analysis <- rma(yi, vi, data = df, method = "EB")

augment(meta_analysis)#> # A tibble: 13 x 6
#>    .observed  .fitted .se.fit .conf.low .conf.high  .resid
#>        <dbl>    <dbl>   <dbl>     <dbl>      <dbl>   <dbl>
#>  1   -0.889  -0.801    0.411     -1.61     0.00524 -0.174
#>  2   -1.59   -1.26     0.354     -1.95    -0.561   -0.870
#>  3   -1.35   -0.990    0.437     -1.85    -0.134   -0.633
#>  4   -1.44   -1.40     0.138     -1.67    -1.13    -0.727
#>  5   -0.218  -0.287    0.212     -0.701    0.128    0.497
#>  6   -0.786  -0.785    0.0823    -0.946   -0.623   -0.0711
#>  7   -1.62   -1.25     0.370     -1.97    -0.523   -0.906
#>  8    0.0120  0.00301  0.0626    -0.120    0.126    0.727
#>  9   -0.469  -0.506    0.221     -0.939   -0.0740   0.246
#> 10   -1.37   -1.25     0.246     -1.73    -0.767   -0.656
#> 11   -0.339  -0.353    0.110     -0.568   -0.139    0.376
#> 12    0.446  -0.281    0.460     -1.18     0.621    1.16
#> 13   -0.0173 -0.145    0.244     -0.623    0.333    0.698