| Type: | Package |
| Title: | Optimization via Subsampling (OPTS) |
| Version: | 0.1 |
| Date: | 2022-05-20 |
| Maintainer: | Mihai Giurcanu <giurcanu@uchicago.edu> |
| Author: | Mihai Giurcanu [aut, cre], Marinela Capanu [aut, ctb], Colin Begg [aut], Mithat Gonen [aut] |
| Imports: | MASS, cvTools, changepoint |
| Description: | Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models. |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Packaged: | 2022-05-24 14:16:53 UTC; mgiurcanu |
| Repository: | CRAN |
| Date/Publication: | 2022-05-25 07:50:08 UTC |
Optimization via Subsampling (OPTS)
Description
opts computes the OPTS MLE in low dimensional
case.
Usage
opts(X, Y, m, crit = "aic", prop_split = 0.5, cutoff = 0.75, ...)
Arguments
X |
n x p covariate matrix (without intercept) |
Y |
n x 1 binary response vector |
m |
number of subsamples |
crit |
information criterion to select the variables: (a) aic = minimum AIC and (b) bic = minimum BIC |
prop_split |
proportion of subsample size and sample size, default value = 0.5 |
cutoff |
cutoff used to select the variables using the stability selection criterion, default value = 0.75 |
... |
other arguments passed to the glm function, e.g., family = "binomial" |
Value
opts returns a list:
betahat |
OPTS MLE of regression parameter vector |
Jhat |
estimated set of active predictors (TRUE/FALSE) corresponding to the OPTS MLE |
SE |
standard error of OPTS MLE |
freqs |
relative frequency of selection for all variables |
Examples
require(MASS)
P = 15
N = 100
M = 20
BETA_vector = c(0.5, rep(0.5, 2), rep(0.5, 2), rep(0, P - 5))
MU_vector = numeric(P)
SIGMA_mat = diag(P)
X <- mvrnorm(N, MU_vector, Sigma = SIGMA_mat)
linearPred <- cbind(rep(1, N), X)
Y <- rbinom(N, 1, plogis(linearPred))
# OPTS-AIC MLE
opts(X, Y, 10, family = "binomial")
Threshold OPTimization via Subsampling (OPTS_TH)
Description
opts_th computes the threshold OPTS MLE in low
dimensional case.
Usage
opts_th(X, Y, m, crit = "aic", type = "binseg", prop_split = 0.5,
prop_trim = 0.2, q_tail = 0.5, ...)
Arguments
X |
n x p covariate matrix (without intercept) |
Y |
n x 1 binary response vector |
m |
number of subsamples |
crit |
information criterion to select the variables: (a) aic = minimum AIC and (b) bic = minimum BIC |
type |
method used to minimize the trimmed and averaged information criterion: (a) min = observed minimum subsampling trimmed average information, (b) sd = observed minimum using the 0.25sd rule (corresponding to OPTS-min in the paper), (c) pelt = PELT changepoint algorithm (corresponding to OPTS-PELT in the paper), (d) binseg = binary segmentation changepoint algorithm (corresponding to OPTS-BinSeg in the paper), (e) amoc = AMOC method. |
prop_split |
proportion of subsample size of the sample size; default value is 0.5 |
prop_trim |
proportion that defines the trimmed mean; default value = 0.2 |
q_tail |
quantiles for the minimum and maximum p-values across the subsample cutpoints used to define the range of cutpoints |
... |
other arguments passed to the glm function, e.g., family = "binomial" |
Value
opts_th returns a list:
betahat |
STOPES MLE of regression parameters |
SE |
SE of STOPES MLE |
Jhat |
set of active predictors (TRUE/FALSE) corresponding to STOPES MLE |
cuthat |
estimated cutpoint for variable selection |
pval |
marginal p-values from univariate fit |
cutpoits |
subsample cutpoints |
aic_mean |
mean subsample AIC |
bic_mean |
mean subsample BIC |
Examples
require(MASS)
P = 15
N = 100
M = 20
BETA_vector = c(0.5, rep(0.5, 2), rep(0.5, 2), rep(0, P - 5))
MU_vector = numeric(P)
SIGMA_mat = diag(P)
X <- mvrnorm(N, MU_vector, Sigma = SIGMA_mat)
linearPred <- cbind(rep(1, N), X)
Y <- rbinom(N, 1, plogis(linearPred))
# Threshold OPTS-BinSeg MLE
opts_th(X, Y, M, family = "binomial")