mvregmed.grid {regmed}

Fit a grid of mvregmed models over a vector of lambda penalty parameters

Description

For each lambda in an input vector of values, fit a penalized mvregmed model

Usage

mvregmed.grid(x, mediator, y, lambda.vec, max.outer = 5000,
max.inner = 2, x.std = TRUE, med.std = TRUE, y.std = TRUE,
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.vec

Vector of values of penalty parameter lambda's

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.

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.

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.grid

Author(s)

Daniel Schaid and Jason Sinnwell

References

Schaid DS, Dikilitas O, Sinnwell JP, Kullo I (2022). Penalized mediation models for multivariate data. Genet Epidemiol 46:32-50.

See Also

mvregmed.fit

Examples

  data(medsim)
  mvfit.grid <- mvregmed.grid(x, med[,1:10], y, lambda.vec=seq(.3, .04, by=-.01))
  summary(mvfit.grid)
  ## plot(mvfit.grid)