bootresid.modmed.mlm {multilevelmediation}

Custom function for residual bootstrap for (moderated) multilevel mediation

Description

Custom function for residual bootstrap for (moderated) multilevel mediation

Usage

bootresid.modmed.mlm(
  data,
  L2ID,
  R = 1000,
  X,
  Y,
  M,
  moderator = NULL,
  covars.m = NULL,
  covars.y = NULL,
  ...,
  type = "all",
  modval1 = NULL,
  modval2 = NULL
)

Arguments

data

Data frame in long format.

L2ID

Name of column that contains grouping variable in 'data' (e.g., "SubjectID")

R

Number of resamples

X

(Character) Name of column that contains the X independent variable in data.

Y

(Character) Name of column that contains the Y dependent variable in data.

M

(Character) Name of column that contains the M mediating variable in data.

moderator

Optional Character that contains name of column that contains the moderator variable in data

covars.m

(Character vector) Optional covariates to include in the model for M.

covars.y

(Character vector) Optional covariates to include in the model for Y.

...

Arguments passed to modmed.mlm to define the mediation analysis model.

type

Character that defines what information to extract from the model. Default and options are in extract.modmed.mlm. As examples, "indirect" will compute the indirect effect, "all" will save all random and fixed effects for possible additional computations, "indirect.diff" will compute the difference in the indirect effect at two values of a possible moderating variable.

modval1

(Optional) Numeric. If the model has a moderator, this value will be passed to extract.modmed.mlm to compute the indirect effect or other effects at that value. See extract.modmed.mlm for details.

modval2

(Optional). If the model has a moderator, it is possible to compute the difference in the indirect at two values of the moderator. If given and an appropriate option for such a difference is chosen for type, this value and that of modval1 will be passed to extract.modmed.mlm to compute and save the difference. This is useful for obtaining a CI for the difference in the indirect effect at two different levels of the moderator.

Details

This function restructures data following Bauer, Pearcher, & Gil (2006) and then conducts residual-based bootstrapping in order to later obtain confidence intervals for the indirect effect and other coefficients. The residual-based bootstrap is described in Falk, Vogel, Hammami, & Miočević's manuscript (in press), but generally follows the procedure by Carpenter, Goldstein, & Rashbash (2003; See also Lai, 2021). Currently this function does not support parallel processing. See the newer boot.modmed.mlm.custom version for a re-write that does.

Value

A list with the following elements. Note that t0 and t are intended to trick the boot package into working with some if its functions.

• t0 Parameter estimates based on the dataset.

• t Bootstrap distribution of all parameter estimates.

• model Fitted model to restructured data as one would obtain from modmed.mlm.

• call Call/arguments used when invoking this function. Useful for later extracting things like indirect effect.

References

Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11(2), 142-163. doi:10.1037/1082-989X.11.2.142

Carpenter, J. R., Goldstein, H., & Rasbash, J. (2003). A novel bootstrap procedure for assessing the relationship between class size and achievement. Applied Statistics, 52(4), 431-443.

Falk, C. F., Vogel, T., Hammami, S., & Miočević, M. (in press). Multilevel mediation analysis in R: A comparison of bootstrap and Bayesian approaches. Behavior Research Methods. doi:10.3758/s13428-023-02079-4 Preprint: doi:10.31234/osf.io/ync34

Lai, M. (2021). Bootstrap confidence intervals for multilevel standardized effect size. Multivariate Behavioral Research, 56(4), 558-578. doi:10.1080/00273171.2020.1746902

Examples

# Example data for 1-1-1 w/o moderation
data(BPG06dat)

# Note that R should be set to something MUCH larger, such as 1000 or greater.
# A low number here is chosen only so testing this example code goes relatively
# quickly
bootresid <- bootresid.modmed.mlm(BPG06dat,L2ID="id", X="x", Y="y", M="m",
  R=5, random.a=TRUE, random.b=TRUE, random.cprime=TRUE,
  control=list(opt="nlm")
  )

extract.boot.modmed.mlm(bootresid, type="indirect")