cv.sprinter: Running sprinter with cross-validation
In sprintr: Sparse Reluctant Interaction Modeling

Description Usage Arguments Value See Also Examples

View source: R/sprinter_cv.R

The main cross-validation function to select the best sprinter fit for a path of tuning parameters.

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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)

`x`	An `n` by `p` design matrix of main effects. Each row is an observation of `p` main effects.
`y`	A response vector of size `n`.
`num_keep`	Number of candidate interactions to keep in Step 2. If `num_keep` is not specified (as default), it will be set to `[n / log n]`.
`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 `lambda` path based on `nlam` and `lam_min_ratio`.
`nlam`	The number of `lambda` values. Default value is `100`.
`lam_min_ratio`	The ratio of the smallest and the largest values in `lambda`. The largest value in `lambda` is usually the smallest value for which all coefficients are set to zero. Default to be `1e-2` in the `n` < `p` setting.
`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 `nfold = 3` is used.
`foldid`	A vector of length `n` representing which fold each observation belongs to. Default to be `NULL`, and the program will generate its own randomly.

An object of S3 class "sprinter".

n: The sample size.
p: The number of main effects.
a0: estimate of intercept corresponding to the CV-selected model.
compact: A compact representation of the selected variables. compact has 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.
fit: The whole glmnet fit object in Step 3.
fitted: fitted value of response corresponding to the CV-selected model.
lambda: The sequence of lambda values used.
cvm: The averaged estimated prediction error on the test sets over K folds.
cvsd: The standard error of the estimated prediction error on the test sets over K folds.
foldid: Fold assignment. A vector of length n.
ibest: The index in lambda that is chosen by CV.
call: Function call.

predict.cv.sprinter

n <- 100
p <- 200
x <- 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)