Augment data with information from a(n) rma object

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 'rma'
augment(x, interval = c("prediction", "confidence"), ...)

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().

interval

For rma.mv models, should prediction intervals ("prediction", default) or confidence intervals ("confidence") intervals be returned? For rma.uni models, prediction intervals are always returned. For rma.mh and rma.peto models, confidence intervals are always returned.

...

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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Value

A tibble::tibble() with columns:

.fitted: Fitted or predicted value.
.lower: Lower bound on interval for fitted values.
.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 observed and fitted values.
.se.fit: Standard errors of fitted values.
.upper: Upper bound on interval for fitted values.
.observed: The observed values for the individual studies

Examples


# load modeling library
library(metafor)
#> Loading required package: metadat
#> Loading required package: numDeriv
#> 
#> Loading the 'metafor' package (version 4.6-0). For an
#> 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:mclust’:
#> 
#>     hc

# generate data and fit
df <-
  escalc(
    measure = "RR",
    ai = tpos,
    bi = tneg,
    ci = cpos,
    di = cneg,
    data = dat.bcg
  )

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

# summarize model fit with tidiers
augment(meta_analysis)
#> # A tibble: 13 × 6
#>    .observed  .fitted .se.fit .lower   .upper  .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