sparc: Training function of Semiparametric Generalized Linear Models
In sparc: Semiparametric Generalized Linear Models

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/sparc.R

Pathwise Iterative Soft Thresholding Algorithm

1 2	sparc(X, y, lambda = NULL, lambda.min.ratio=NULL, nlambda = NULL, thol = 1e-4, max.ite = 1e4, alpha = sqrt(1/2))

`X`	The `n` by `d` design matrix of the training set, where `n` is sample size and `d` is dimension.
`y`	The `n`-dimensional response vector of the training set, where `n` is sample size.
`lambda`	A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care. Do not supply a single value for lambda. Supply instead a decreasing sequence of lambda values. sparc relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.
`nlambda`	The number of lambda values. The default value is 30.
`lambda.min.ratio`	Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero). The default is 0.1.
`thol`	Stopping precision. The default value is 1e-4.
`max.ite`	The number of maximum iterations. The default value is 1e4.
`alpha`	The backtracking linea search parameter. The default value is sqrt(1/2).

We adopt pathwise Iterative Soft Thresholding Algorithm.

`w`	The `d` by `nlambda` Regularization Path. The j-th column is the estimation coefficient corresponding to the j-th regularization parameter.

Tuo Zhao and Han Liu
Maintainers: Tuo Zhao<tourzhao@gmail.com>

Y. Ning, Y. Chen, and H. Liu. "High Dimensional Semiparametric Generalized Linear Models", Technical Report, 2013.

sparc-package

## generating training data
n = 100
d = 200
set.seed(3)
X = matrix(rnorm(n*d),n,d)
y = 3*X[,1]+2*X[,2] + 1.5*X[,4] + rnorm(n)

## estimating models
out = sparc(X,y)