regmed.prefilter: Prefilter to reduce the number of mediators for subsequent...

In regmed: Regularized Mediation Analysis

Prefilter to reduce the number of mediators for subsequent analyses

Description

Use sure independence screening (Fan & Lv, 2008)to reduce the number of potential mediators when the number of potential mediators is large. This is based on ranking marginal correlations and then selecting the highest ranked values such that the number of parameters is less than the sample size. Because mediation depends on the two correlations, cor(x,med) and cor(med, y) we rank the absolute values of their products, |cor(x, med) * cor(med, y)|, and choose the highest k ranked values to determine which potential mediators to include in penalized mediation models. If k is not specified, the default value of k is n/2, where n is the sample size, because each mediator results in two parameters alpha and beta.

Usage

regmed.prefilter(x, mediator, y, k = NULL, x.std = TRUE,
med.std = TRUE, y.std=TRUE, max.cor=0.99)

Arguments

x	vector representing "exposure" variable (sometimes called instrumental variable)
mediator	matrix of mediators, rows are observations, columns are different mediators
y	vector representing outcome
k	Number of potential mediators to select. Default is n/2, where n is sample size.
x.std	logical (TRUE/FALSE) whether to standardize x by dividing by standard devation of x. Note that x will be centered on its mean.
med.std	logical (TRUE/FALSE) whether to standardize mediators by dividing each mediator by its standard deviation. Note that mediators will be centered on their means.
y.std	logical (TRUE/FALSE) whether to standardize y by dividing by standard devation of y. Note that y will be centered on its mean.
max.cor	maximum correlation within mediators, so that fitting is more robust

Value

list with x, mediator, and y, after subsetting to no missing values, applying x.std and med.std, and subsetting mediators to k top choices.

Author(s)

Dan Schaid, Jason Sinnwell

References

Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. J. R. Statist. Soc.B, 70, 849-911. Schaid, DJ, Sinnwell JP. (2020) Penalized Models for Analysis of Multiple Mediators. Genet Epidemiol 44:408-424.

See Also

regmed

Examples

data(medsim)
dim(med)
filtered <- regmed.prefilter(x[,1], med, y[,1], k=10)
dim(filtered$med)

regmed documentation built on Jan. 22, 2023, 1:30 a.m.