| Type: | Package |
| Title: | Sparse Reluctant Interaction Modeling |
| Version: | 0.9.0 |
| Date: | 2019-08-08 |
| Description: | An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at <doi:10.48550/arXiv.1907.08414>. |
| BugReports: | https://github.com/hugogogo/sprintr/issues |
| License: | GPL-3 |
| Imports: | Rcpp (≥ 0.12.16), glmnet |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 6.0.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2019-08-23 01:16:15 UTC; hugo |
| Author: | Guo Yu [aut, cre] |
| Maintainer: | Guo Yu <gy63@uw.edu> |
| Repository: | CRAN |
| Date/Publication: | 2019-08-24 10:40:02 UTC |
Running sprinter with cross-validation
Description
The main cross-validation function to select the best sprinter fit for a path of tuning parameters.
Usage
cv.sprinter(x, y, num_keep = NULL, square = FALSE, lambda = NULL, nlam = 100, lam_min_ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04), nfold = 5, foldid = NULL)Arguments
x | An |
y | A response vector of size |
num_keep | Number of candidate interactions to keep in Step 2. If |
square | Indicator of whether squared effects should be fitted in Step 1. Default to be FALSE. |
lambda | A user specified list of tuning parameter. Default to be NULL, and the program will compute its own |
nlam | The number of |
lam_min_ratio | The ratio of the smallest and the largest values in |
nfold | Number of folds in cross-validation. Default value is 5. If each fold gets too view observation, a warning is thrown and the minimal |
foldid | A vector of length |
Value
An object of S3 class "sprinter".
nThe sample size.
pThe number of main effects.
a0estimate of intercept corresponding to the CV-selected model.
compactA compact representation of the selected variables.
compacthas three columns, with the first two columns representing the indices of a selected variable (main effects with first index = 0), and the last column representing the estimate of coefficients.fitThe whole
glmnetfit object in Step 3.fittedfitted value of response corresponding to the CV-selected model.
lambdaThe sequence of
lambdavalues used.cvmThe averaged estimated prediction error on the test sets over K folds.
cvsdThe standard error of the estimated prediction error on the test sets over K folds.
foldidFold assignment. A vector of length
n.ibestThe index in
lambdathat is chosen by CV.callFunction call.
See Also
Examples
n <- 100p <- 200x <- matrix(rnorm(n * p), n, p)y <- x[, 1] - 2 * x[, 2] + 3 * x[, 1] * x[, 3] - 4 * x[, 4] * x[, 5] + rnorm(n)mod <- cv.sprinter(x = x, y = y)Calculate prediction from acv.sprinter object.
Description
Calculate prediction from acv.sprinter object.
Usage
## S3 method for class 'cv.sprinter'predict(object, newdata, ...)Arguments
object | a fitted |
newdata | a design matrix of all the |
... | additional argument (not used here, only for S3 generic/method consistency) |
Value
The prediction ofnewdata by the cv.sprinter fitobject.
Examples
n <- 100p <- 200x <- matrix(rnorm(n * p), n, p)y <- x[, 1] + 2 * x[, 2] - 3 * x[, 1] * x[, 2] + rnorm(n)mod <- cv.sprinter(x = x, y = y)fitted <- predict(mod, newdata = x)Sure Independence Screening in Step 2
Description
Sure Independence Screening in Step 2
Usage
screen_cpp(x, y, num_keep, square = FALSE, main_effect = FALSE)Arguments
x | a n-by-p matrix of main effects, with i.i.d rows, and each row represents a vector of observations of p main-effects |
y | a vector of length n. In sprinter, y is the residual from step 1 |
num_keep | the number of candidate interactions in Step 2. Default to be n / [log n] |
square | An indicator of whether squared effects should be considered in Step 1 (NOT Step 2!). square == TRUE if squared effects have been considered in Step 1, i.e., squared effects will NOT be considered in Step 2. |
main_effect | An indicator of whether main effects should also be screened. Default to be false. The functionality of main_effect = true is not used in sprinter, but for SIS_lasso. |
Value
an matrix of 2 columns, representing the index pair of the selected interactions.
Sparse Reluctant Interaction Modeling
Description
This is the main function that fits interaction models with a path of tuning parameters (for Step 3).
Usage
sprinter(x, y, num_keep = NULL, square = FALSE, lambda = NULL, nlam = 100, lam_min_ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04))Arguments
x | An |
y | A response vector of size |
num_keep | Number of candidate interactions to keep in Step 2. If |
square | Indicator of whether squared effects should be fitted in Step 1. Default to be FALSE. |
lambda | A user specified list of tuning parameter. Default to be NULL, and the program will compute its own |
nlam | The number of |
lam_min_ratio | The ratio of the smallest and the largest values in |
Value
An object of S3 class "sprinter".
nThe sample size.
pThe number of main effects.
a0Estimate of intercept.
coefEstimate of regression coefficients.
idxIndices of all main effects and interactions in Step 3.
fittedFitted response value. It is a
n-by-nlammatrix, with each column representing a fitted response vector for a value of lambda.lambdaThe sequence of
lambdavalues used.callFunction call.
See Also
Examples
set.seed(123)n <- 100p <- 200x <- matrix(rnorm(n * p), n, p)y <- x[, 1] - 2 * x[, 2] + 3 * x[, 1] * x[, 3] - 4 * x[, 4] * x[, 5] + rnorm(n)mod <- sprinter(x = x, y = y)