Description Usage Arguments Details Value Note Author(s) References See Also Examples
brainGraph_mediate
performs simple mediation analyses in which a given
graph or vertexlevel 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
diseasespecific metric (e.g., recovery time).
bg_to_mediate
converts the results into an object of class
mediate
. In brainGraph
, it is only used for
the summary.mediate
method, but you can similarly
use its output for the plot.mediate
method.
1 2 3 4 5 6 7 8 9 10  brainGraph_mediate(g.list, covars, mediator, treat, outcome, covar.names,
level = c("graph", "vertex"), control.value = 0, treat.value = 1,
int = FALSE, boot = TRUE, boot.ci.type = c("perc", "bca"),
N = 1000, conf.level = 0.95, long = FALSE, ...)
## S3 method for class 'bg_mediate'
summary(object, mediate = FALSE, region = NULL,
digits = max(3L, getOption("digits")  2L), ...)
bg_to_mediate(x, region = NULL)

g.list 
A 
covars 
A data table containing covariates of interest. It must include
columns for 
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 
covar.names 
Character vector of the column name(s) in 
level 
Character string; either 
control.value 
Value of 
treat.value 
Value of 
int 
Logical indicating whether or not to include an interaction of the
mediator and treatment. Default: 
boot 
Logical indicating whether or not to perform bootstrapping. This
should always be done. Default: 
boot.ci.type 
Character string; which type of CI's to calculate.
Default: 
N 
Integer; the number of bootstrap samples to run. Default:

conf.level 
Numeric between 0 and 1; the level of the CI's to
calculate. Default: 
long 
Logical indicating whether or not to return all bootstrap
samples. Default: 
... 
Other arguments passed to 
object 
A 
mediate 
Logical indicating whether or not to use the 
region 
Character string specifying which region's results to
summarize; only relevant if 
digits 
Integer specifying the number of digits to display for Pvalues 
x 
Object output from 
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). If you use this function, please cite
their work.
An object of class bg_mediate
with elements:
level 
Either 
removed.subs 
A character vector of Study.ID's removed due to incomplete data 
X.m, X.y 
Design matrix and numeric array for the model with the
mediator as the outcome variable ( 
y.m, y.y 
Outcome variables for the associated design matrices above.

res.obs 
A 
res.ci 
A 
res.p 
A 
boot 
Logical, the 
boot.ci.type 
Character string indicating which type of bootstrap confidence intervals were calculated. 
res.boot 
A 
treat 
Character string of the treatment variable. 
mediator 
Character string of the mediator variable. 
outcome 
Character string of the outcome variable. 
covariates 
Returns 
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 pretreatment covariate names. 
bg_to_mediate
returns an object of class mediate
As of brainGraph v2.0.0
, this function has been tested only for
a treatment (independent) variable X being a factor (e.g.,
disease group, old vs. young, etc.). If your treatment variable has more
than 2 levels, then you must explicitly specify the levels you would like to
compare; otherwise, the baseline and first levels are taken to be the
control and treatment values, respectively. Be aware that these are 0
indexed; that is, if you have 3 groups and you would like the treatment
group to be the 3rd, you should specify as either the group's character
string or as treat.value=2
.
Allowing for treatmentmediator 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.
Christopher G. Watson, cgwatson@bu.edu
Tingley, D. and Yamamoto, T. and Hirose, K. and Keele, L. and Imai, K. (2014) mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38. https://dx.doi.org/10.18637/jss.v059.i05
Imai, K. and Keele, L. and Yamamoto, T. (2010) Identification inference, and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. https://dx.doi.org/10.1214/10STS321
Imai, K. and Keele, L. and Tingley, D. (2010) A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334. https://dx.doi.org/10.1037/a0020761
Imai, K. and Keele, L. and Tingley, D. and 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. https://dx.doi.org/10.1017/S0003055411000414
Imai, K. and Yamamoto, T. (2013) Identification and sensitivity analysis for multiple causal mechanisms: revisiting evidence from framing experiments. Political Analysis, 21(2), 141–171. https://dx.doi.org/10.1093/pan/mps040
Other Group analysis functions: Bootstrapping
,
GLM
, NBS
,
brainGraph_permute
, mtpc
1 2 3 4 5 6 7  ## Not run:
med.EglobWt.FSIQ < brainGraph_mediate(g[[5]], covars.med, 'E.global.wt',
'Group', 'FSIQ', covar.names=c('age', 'gender'), N=1e4)
med.strength.FSIQ < brainGraph_mediate(g[[5]], covars.med, 'strength',
'Group', 'FSIQ', covar.names=c('age', 'gender'), level='vertex')
## End(Not run)

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