brainGraph_mediate: Mediation analysis with brain graph measures as mediator...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/brainGraph_mediate.R

Description

brainGraph_mediate performs simple mediation analyses in which a given graph- or vertex-level measure (e.g., weighted global efficiency) is the mediator M. The outcome (or dependent/response) variable Y can be a neuropsychological measure (e.g., IQ) or can be a disease-specific metric (e.g., recovery time). The treatment variable should be a factor.

Usage

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brainGraph_mediate(g.list, covars, mediator, treat, outcome, covar.names,
  level = c("graph", "vertex"), boot = TRUE, boot.ci.type = c("perc",
  "bca"), N = 1000, conf.level = 0.95, control.value = 0,
  treat.value = 1, long = TRUE, int = FALSE, ...)

Arguments

g.list

A list of igraph graph objects for all subjects

covars

A data table containing covariates of interest. It must include columns for Study.ID, the treatment variable, covar.names, and the outcome variable.

mediator

Character string; the name of the graph measure acting as the mediating variable

treat

Character string; the treatment variable (e.g., Group)

outcome

Character string; the name of the outcome variable of interest (e.g., full-scale IQ, memory, etc.)

covar.names

Character vector of the column names in covars to include in the models as pre-treatment covariates.

level

Character string; either vertex (default) or graph

boot

Logical indicating whether or not to perform bootstrapping (default: TRUE)

boot.ci.type

Character string; which type of CI's to calculate (default: perc)

N

Integer; the number of bootstrap samples to run (default: 1e3)

conf.level

Numeric; the level of the CI's to calculate (default: 0.95 for the 2.5 and 97.5 percentiles)

control.value

Value of treat to be used as the control condition (default: 0)

treat.value

Value of treat to be used as the treatment condition (default: 1)

long

Logical indicating whether or not to return all bootstrap samples (default: TRUE)

int

Logical indicating whether or not to include an interaction of the mediator and treatment (default: FALSE)

...

Other arguments passed to brainGraph_GLM_design (e.g., binarize)

Details

This code was adapted closely from mediate in the mediation package, and the procedure is exactly the same as theirs (see the references listed below). So, if you use this function, please cite their work.

As of brainGraph v2.0.0, this function has been tested only for a treatment (independent) variable X being a 2-level factor (e.g., disease group, old vs. young, etc.).

Allowing for treatment-mediator interaction (setting int=TRUE) currently will only work properly if the mediator is a continuous variable; since the mediator is always a graph metric, this should always be the case.

Value

An object of class bg_mediate with elements:

level

Either graph or vertex.

removed

A character vector of Study.ID's removed due to incomplete data

X.m, X.y

Design matrices for the model with the mediator as the outcome variable (X.m) and for the model with the mediator as an additional predictor (X.y)

y.m, y.y

Outomce variables for the associated design matrices above. y.m will be a matrix of size # subj. X # regions

res.obs

A data.table of the observed values of the point estimates.

res.ci

A data.table of the confidence intervals for the effect estimates.

res.p

A data.table of the two-sided p-values for the effect estimates

boot

Logical, the boot argument.

boot.ci.type

Character string indicating which type of bootstrap confidence intervals were calculated.

res.boot

A data.table with N rows of the bootstrap results for all effects.

treat

Character string of the treatment variable.

mediator

Character string of the mediator variable.

outcome

Character string of the outcome variable.

covariates

Returns NULL; not used in this package.

INT

Logical indicating whether the models included an interaction between treatment and mediator.

conf.level

The confidence level.

control.value

The value of the treatment variable used as the control condition.

treat.value

The value of the treatment variable used as the treatment condition.

nobs

Integer; the number of observations in the models.

sims

Integer; the number of bootstrap replications.

covar.names

The pre-treatment covariate names.

Author(s)

Christopher G. Watson, [email protected]

References

Tingley D, Yamamoto T, Hirose K, Keele L, Imai K (2014). mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5):1-38.

Imai K, Keele L, Yamamoto T (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science, 25(1):51-71.

Imai K, Keele L, Tingley D (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4):309-334.

Imai K, Keele L, Tingley D, Yamamoto T (2011). Unpacking the black box of causality: learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105(4):765-789.

Imai K, Yamamoto T (2013). Identification and sensitivity analysis for multiple causal mechanisms: revisiting evidence from framing experiments. Political Analysis, 21(2):141-171.

See Also

Other Group analysis functions: IndividualContributions, NBS, brainGraph_GLM, brainGraph_boot, brainGraph_permute, mtpc

Examples

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## Not run: 
med.EglobWt.FSIQ <- brainGraph_mediate(dt.G[threshold == thresholds[5]],
  covars.med, 'E.global.wt', 'Group', 'FSIQ', covar.names=c('age', 'gender'),
  boot=TRUE, N=1e4)
med.strength.FSIQ <-
  brainGraph_mediate(dt.V[threshold == thresholds[5] & region == 'lcACC'],
                     covars.med, 'strength', 'Group', 'FSIQ',
                     covar.names=c('age', 'gender'), N=1e3)

## End(Not run)

brainGraph documentation built on May 29, 2018, 9:03 a.m.