spinyReg: spinyReg
In spinyReg: Sparse Generative Model and Its EM Algorithm

Description Usage Arguments Value See Also Examples

Computethe path of solution of a spinyReg fit.

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spinyreg(X, Y, alpha = 0.1, gamma = 1, z = rep(1, ncol(X)),
  intercept = TRUE, normalize = TRUE, verbose = 1, recovery = TRUE,
  maxit = 1000, eps = 1e-10)

`X`	matrix of features. Do NOT include intercept.
`Y`	matrix of responses.
`alpha`	numeric scalar; prior value for the alpha parameter (see the model's details). Default is 0.1.
`gamma`	numeric scalar; prior value for the gamma parameter (see the model's details). Default is 1.
`z`	numeric vector; prior support of active variable. Default is `rep(1,p)`, meaning all variable activated
`intercept`	logical; indicates if a vector of intercepts should be included in the model. Default is `TRUE`.
`normalize`	logical; indicates if predictor variables should be normalized to have unit L2 norm before fitting. Default is `TRUE`.
`verbose`	integer; activate verbose mode from '0' (nothing) to '2' (detailed output). should be included in the model. Default is `TRUE`.
`recovery`	logical; indicates if the full path of models should be inspected for model selection. Default is `TRUE`.
`maxit`	integer; the maximal number of iteration (i.e. number of alternated optimization between each parameter) in the Expectation/Maximization algorithm.
`eps`	a threshold for convergence. Default is `1e-10`.

an object with class spinyreg, see the documentation page spinyreg for details.

See also spinyreg.

## Not run: 
data <- read.table(file="http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/prostate.data")
x <- data[, 1:8]
y <- data[, 9]
out <- spinyreg(x,y,verbose=2)

## End(Not run)