mediate_spcma applies sparse principal component mediation
analysis to mediation settings in which the mediators are high-dimensional.
mediate_spcma(
A,
M,
Y,
var_per = 0.8,
n_pc = NULL,
sims = 1000,
boot_ci_type = "bca",
ci_level = 0.95,
fused = FALSE,
gamma = 0,
per_jump = 0.7,
eps = 1e-04,
maxsteps = 2000,
seed = 1
)A list containing:
loadings: a matrix of the sparse PC loadings.
pcs: a matrix of the PCs.
var_explained: the cumulative proportion of variance explained by the PCs.
contributions: a data frame containing the estimates, confidence intervals, and p-values of the mediation contributions.
effects: a data frame containing the estimated direct, global mediation, and total effects
length n numeric vector containing exposure variable
n x p numeric matrix of high-dimensional mediators.
length n numeric vector containing continuous outcome variable.
a numeric variable with the desired proportion of variance explained. Default is 0.8.
optional numeric variable with the desired number of PCs, in which case
var_per is ignored. Default is NULL and the number of PCs is
determined based on the desired proportion of variance explained.
number of Monte Carlo draws for nonparametric bootstrap or
quasi-Bayesian approximation (see mediation::mediate()).
Default is 1000.
character string indicating the type of bootstrap
confidence intervals for when boot = TRUE. If "bca",
bias-corrected and accelerated (BCa) confidence intervals will be estimated.
If "perc", percentile confidence intervals will be estimated
(see mediation::mediate()). Default is "bca".
the designated confidence level. Default 0.95.
logical variable for whether the fused LASSO should be used
instead of the ordinary LASSO. Default is FALSE.
numeric variable >=0 indicating the ratio of the
standard LASSO penalty to the fusion penalty (see genlasso::genlasso()).
Ignored if fused = FALSE. Default is 0, meaning there is no standard
penalty. Larger values result in more shrinkage and sparser PCs.
numeric value used for tuning parameter selection - the
quantile cut-off for total variance change under different lambda values
in the LASSO. Default is 0.7. Larger values result in more shrinkage and
sparser PCs
numeric variable indicating the multiplier for the ridge penalty
in case X is rank deficient (see genlasso::genlasso()).
Default is 1e-4.
an integer specifying the maximum number of steps for the
algorithm before termination (see genlasso::genlasso()). Default
is 2000.
seed used for fitting single-mediator models after PCA
mediate_spcma performs principal component mediation analysis, comparable
to mediate_pcma, with the modification that the PC loadings are sparsified
by a flexible LASSO penalty. This has the potential make the PCs more interpretable,
since, unlike in PCA, they are only linear combinations of a subset of mediators
rather than all of them. The choice of LASSO penalties is determined by
the fused argument - which, when set to TRUE, deploys a fused
LASSO penalty that encourages the model to give consecutive mediators
similar loadings. The default is fused = FALSE, and the standard
LASSO penalty is used instead of the fusion penalty. Once the sparse PCs are
computed, inference proceeds exactly like in PCMA, and the PC-mediators are
evaluated with methods from the mediate package.
Zhao, Y., Lindquist, M. A. & Caffo, B. S. Sparse principal component based high-dimensional mediation analysis. Comput. Stat. Data Anal. 142, 106835 (2020).
A <- med_dat$A
M <- med_dat$M
Y <- med_dat$Y
# Fit SPCMA with the fused LASSO penalty while choosing the number of PCs based
# on the variance they explain. In practice, var_per and sims should be higher.
out <- mediate_spcma(A, M, Y, var_per = 0.25, fused = TRUE, gamma = 2, sims = 10)
out$effects
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