cv.lspath: Compute cross validation error
In sahirbhatnagar/sail: Sparse Additive Interaction Learning

Description Usage Arguments Details Functions References See Also

functions used to calculate cross validation error and used by the cv.sail function

cv.lspath(outlist, lambda, x, y, e, weights, foldid, type.measure, grouped,
  keep = FALSE)

cvcompute(mat, weights, foldid, nlams)

getmin(lambda, cvm, cvsd)

lambda.interp(lambda, s)

`outlist`	list of cross validated fitted models. List is of length equal to `nfolds` argument in `cv.sail` function
`lambda`	a user supplied lambda sequence. Typically, by leaving this option unspecified users can have the program compute its own lambda sequence based on `nlambda` and `lambda.factor`. Supplying a value of lambda overrides this. It is better to supply a decreasing sequence of lambda values than a single (small) value, if not, the program will sort user-defined lambda sequence in decreasing order automatically. Default: `NULL`.
`x`	input matrix of dimension `n x p`, where `n` is the number of subjects and p is number of X variables. Each row is an observation vector. Can be a high-dimensional (n < p) matrix. Can be a user defined design matrix of main effects only (without intercept) if `expand=FALSE`
`y`	response variable. For `family="gaussian"` should be a 1 column matrix or numeric vector. For `family="binomial"`, should be a 1 column matrix or numeric vector with -1 for failure and 1 for success.
`e`	exposure or environment vector. Must be a numeric vector. Factors must be converted to numeric.
`weights`	observation weights. Default is 1 for each observation. Currently NOT IMPLEMENTED.
`foldid`	numeric vector indicating which fold each observation belongs to
`type.measure`	loss to use for cross-validation. Currently only 3 options are implemented. The default is `type.measure="deviance"`, which uses squared-error for gaussian models (and is equivalent to `type.measure="mse"`) there). `type.measure="mae"` (mean absolute error) can also be used which measures the absolute deviation from the fitted mean to the response (\|y-\hat{y}\|).
`grouped`	This is an experimental argument, with default `TRUE`, and can be ignored by most users. This refers to computing `nfolds` separate statistics, and then using their mean and estimated standard error to describe the CV curve. If `grouped=FALSE`, an error matrix is built up at the observation level from the predictions from the `nfold` fits, and then summarized (does not apply to `type.measure="auc"`). Default: TRUE.
`keep`	If `keep=TRUE`, a prevalidated array is returned containing fitted values for each observation and each value of `lambda`. This means these fits are computed with this observation and the rest of its fold omitted. The `folid` vector is also returned. Default: FALSE
`mat`	matrix of predictions
`nlams`	number of lambdas fit
`cvm`	mean cv error
`cvsd`	sd of cv error
`s`	numeric value of lambda

The output of the cv.lspath function only returns values for those tuning parameters that converged. cvcompute, getmin, lambda.interp are taken verbatim from the glmnet package

cvcompute: Computations for crossvalidation error
getmin: get lambda.min and lambda.1se
lambda.interp: Interpolation function.

Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. http://www.jstatsoft.org/v33/i01/.

cv.sail

sahirbhatnagar/sail documentation built on July 17, 2021, 5:10 a.m.