StratifiedMedicine (version 1.0.5)

filter_train: filter_train: Identify variables of interest

Description

Wrapper function to train a filter model to determine variables associated with the outcome and/or treatment.. Options include elastic net (glmnet) and random forest based variable importance (ranger). Used directly in PRISM.

Usage

filter_train(
  Y,
  A,
  X,
  family = "gaussian",
  filter = "glmnet",
  hyper = NULL,
  ...
)

Arguments

Y

The outcome variable. Must be numeric or survival (ex; Surv(time,cens) )

A

Treatment variable. (Default supports binary treatment, either numeric or factor). "ple_train" accomodates >2 along with binary treatments.

X

Covariate space.

family

Outcome type. Options include "gaussion" (default), "binomial", and "survival".

filter

Filter model to determine variables that are likely associated with the outcome and/or treatment. Outputs a potential reduce list of varia where X.star has potentially less variables than X. Default is "glmnet" (elastic net). Other options include "ranger" (random forest based variable importance with p-values). See filter_train for more details. "None" uses no filter.

hyper

Hyper-parameters for the filter model (must be list). Default is NULL. See details below.

...

Any additional parameters, not currently passed through.

Value

Trained filter model and vector of variable names that pass the filter.

• mod - trained model

• filter.vars - Variables that remain after filtering (could be all)

Details

filter_train currently fits elastic net or random forest to find a reduced set of variables which are likely associated with the outcome (Y) and/or treatment (A). Current options include:

1. glmnet: Wrapper function for the function "glmnet" from the glmnet package. Here, variables with estimated elastic net coefficients of 0 are filtered. Uses LM/GLM/cox elastic net for family="gaussian","binomial", "survival" respectively. Default is to regress Y~ENET(X) with hyper-parameters:

hyper = list(lambda="lambda.min", family="gaussian",interaction=FALSE))

If interaction=TRUE, then Y~ENET(X,X*A), and variables with estimated coefficients of zero in both the main effects (X) and treatment-interactions (X*A) are filtered. This aims to find variables that are prognostic and/or predictive.

2. ranger: Wrapper function for the function "ranger" (ranger R package) to calculate random forest based variable importance (VI) p-values. Here, for the test of VI>0, variables are filtered if their one-sided p-value>=0.10. P-values are obtained through subsampling based T-statistics (T=VI_j/SE(VE_j)) for feature j through the delete-d jackknife), as described in Ishwaran and Lu 2017. Used for continuous, binary, or survival outcomes. Default hyper-parameters are:

hyper=list(b=0.66, K=200, DF2=FALSE, FDR=FALSE, pval.thres=0.10)

where b=(% of total data to sample; default=66%), K=# of subsamples, FDR (FDR based multiplicity correction for p-values), pval.thres=0.10 (adjust to change filtering threshold). DF2 fits Y~ranger(X, XA) and calculates the VI_2DF = VI_X+VI_XA, which is the variable importance of the main effect + the interaction effect (joint test). Var(VI_2DF) = Var(VI_X)+Var(VI_AX)+2cov(VI_X, VI_AX) where each component is calculated using the subsampling approach described above.

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, https://web.stanford.edu/~hastie/Papers/glmnet.pdf Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010 Vol. 33(1), 1-22 Feb 2010.

Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. 10.18637/jss.v077.i01.

Ishwaran, H. Lu, M. (2017). Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival. Statistics in Medicine 2017.

See Also

PRISM

Examples

# NOT RUN {
library(StratifiedMedicine)
## Continuous ##
dat_ctns = generate_subgrp_data(family="gaussian")
Y = dat_ctns$Y
X = dat_ctns$X
A = dat_ctns$A

# Fit ple_ranger directly (treatment-specific ranger models) #
mod1 = filter_train(Y, A, X, filter="filter_glmnet")
mod1$filter.vars

mod2 = filter_train(Y, A, X, filter="filter_glmnet", hyper=list(interaction=TRUE))
mod2$filter.vars

mod3 = filter_train(Y, A, X, filter="filter_ranger")
mod3$filter.vars

# }
# NOT RUN {

# }