filter_train {StratifiedMedicine}

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.

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.

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)

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. doi: 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

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