| Title: | Generalized Fiducial Inference for Ultrahigh-Dimensional Regression |
|---|---|
| Description: | Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>. |
| Authors: | Stéphane Laurent [aut, cre] |
| Maintainer: | Stéphane Laurent <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2024-11-21 03:03:54 UTC |
| Source: | https://github.com/stla/gfiultra |
Fiducial confidence intervals of the selected parameters of a ultrahigh-dimensional regression.
gfiConfInt(gfi, conf = 0.95)gfiConfInt(gfi, conf = 0.95)
gfi |
an output of |
conf |
confidence level |
The confidence intervals in a matrix.
Fiducial estimates of the selected parameters of a ultrahigh-dimensional regression.
gfiEstimates(gfi)gfiEstimates(gfi)
gfi |
an output of |
The estimates in a matrix.
Generates the fiducial simulations of the parameters of a "large p - small n" regression model and returns the selected models with their probability.
gfiUltra(formula, data, nsims = 1000L, verbose = FALSE, gamma = 1, ...)gfiUltra(formula, data, nsims = 1000L, verbose = FALSE, gamma = 1, ...)
formula |
a formula describing the model |
data |
dataframe in which to search the variables of the model |
nsims |
number of fiducial simulations |
verbose |
whether to print the messages generated by the screening procedure |
gamma |
tuning parameter; for expert usage only |
... |
named arguments passed to |
A list with two elements: the fiducial simulations in a matrix
(fidSims) and a vector giving the probabilities of the selected
models (models).
Randy C. S. Lai, Jan Hannig & Thomas C. M. Lee. Generalized Fiducial Inference for Ultrahigh-Dimensional Regression. Journal of the American Statistical Association, Volume 110, 2015 - Issue 510, 760-772. <doi:10.1080/01621459.2014.931237>
# data #### set.seed(666L) n <- 300L p <- 1000L X <- matrix(rnorm(n * p), nrow = n, ncol = p) colnames(X) <- paste0("x", 1L:p) beta <- c(4, 5, 6, 7, 8) y <- X[, 1L:5L] %*% beta + rnorm(n, sd = 0.9) dat <- cbind(y, as.data.frame(X)) # fiducial simulations #### gfi <- gfiUltra(y ~ ., data = dat, nsims = 10000L) # selected models gfi$models # fiducial confidence intervals gfiConfInt(gfi) # fiducial estimates gfiEstimates(gfi)# data #### set.seed(666L) n <- 300L p <- 1000L X <- matrix(rnorm(n * p), nrow = n, ncol = p) colnames(X) <- paste0("x", 1L:p) beta <- c(4, 5, 6, 7, 8) y <- X[, 1L:5L] %*% beta + rnorm(n, sd = 0.9) dat <- cbind(y, as.data.frame(X)) # fiducial simulations #### gfi <- gfiUltra(y ~ ., data = dat, nsims = 10000L) # selected models gfi$models # fiducial confidence intervals gfiConfInt(gfi) # fiducial estimates gfiEstimates(gfi)