Description Usage Arguments Details Value Examples
This function performs CMF on a set of potential mediators, given an input and an output variable. It selects those mediators that are deemed relevant by a default or a userdefined decision function, *conditional* on the other mediators in the model. By doing this cyclically, and with multiple random starts, the algorithm outputs an estimate of the best mediators.
1 2 3 
x 
exogenous variable; numeric vector or data frame with x, y, and at least one M column 
M 
potential mediators; data frame with column names 
y 
outcome variable; numeric vector 
decisionFunction 
either a function with as inputs x, m, y, parameters, and as output a TRUE (include) or FALSE (exclude) statement or a string indicating the builtin decision function to use (see details) 
nStarts 
how many times to start the algorithm 
nCores 
how many threads (cores) to use for parallel processing 
cutoff 
a cutoff value for selection: variables are selected if they
display a selection rate higher than this value. Only relevant when multiple
starts are specified. Can also be specified posthoc using

maxIter 
the maximum number of iterations for each start 
stableLag 
how long does the selection need to be stable before deciding the algorithm has converged 
pb 
Whether to display a progress bar (default TRUE). Only available with builtin decision functions 
... 
parameters passed to decisionFunction 
Available decision functions. These functions are implemented in
C++ to speed up computation. Between brackets the additional parameter that
may be passed to the function in the ...
argument of this function.
(arguments
= defaultvalue
):
 prodcoef
(p.value
= 0.1): Test for the product of
coefficients, Sobel test
 causalsteps
(p.value
= 0.1): Causal steps test min(Ta, Tb)
an object of class cmf
. See cmfmethods
1 2 3 4  # generate some data
dat < generateMed(a = (1:10)/20, b = (1:10)/20)
# Run CMF on this data
cmf(dat)

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