mvregmed.fit {regmed}

Multivariate regularized mediation model

Description

Fit regularized mediation model for a specified lambda penalty value. Structural equation models for analysis of multiple exposures (x), multiple mediators, and multiple outcome variables (y) are fit with a lasso (L1) penalaty on the model parameters. The model is x-[alpha] -> mediator-[beta] -> outcome, where alpha and beta are the parameters for the indirect effect of x on y, through the mediator. The model also allows a direct effect of x on y: x-[delta]->y.

Usage

mvregmed.fit(x, mediator, y, lambda, x.std = TRUE, med.std = TRUE,
y.std = TRUE, max.outer = 5000, max.inner = 2, step.multiplier = 0.5,
print.iter = FALSE, max.cor=0.99)

Arguments

x

matrix with columns representing "exposure" variable (sometimes called instrumental variable)

mediator

matrix with columns representing mediator variables

y

matrix with columns representing outcome variables

lambda

lambda penalty parameter

x.std

logical (TRUE/FALSE) whether to standardize x by dividing by standard devation of x. Note that each column of x will be centered on its mean.

med.std

logical (TRUE/FALSE) whether to standardize mediator by dividing by standard devation of mediator. Note that each column of mediator will be centered on its mean.

y.std

logical (TRUE/FALSE) whether to standardize y by dividing by standard devation of y. Note that each column of y will be centered on its mean.

max.outer

maximum number of outer loop iterations. The outer loop cycles over several inner loops.

max.inner

maximum number of iterations for each inner loop. There is an inner loop for each paramemeter in the matrices alpha, beta, delta, and vary.

step.multiplier

In inner loop, the step size is shrunk by the step.multiplier to assure that step size is not too large. Generally, the default of 0.5 works well.

print.iter

print iteration number during fitting routine

max.cor

maximum correlation within y, x, or mediators, so fitting is more robust

Value

An object of class mvregmed

Author(s)

Daniel Schaid and Jason Sinnwell

References

Schaid DJ, Dikilitas O, Sinnwell JP, Kullo I (2022). Penalized Mediation Models for Multivariate Data. Genet Epidemiol 46:32-50.

See Also

mvregmed.grid

Examples

  data(medsim)
  mvfit <- mvregmed.fit(x, med[,1:10], y, lambda=.1)
  summary(mvfit)