R: Fit a linear model via the GAGA algorithm using cpp.

rcpp_lm_gaga {GAGAs}

R Documentation

Fit a linear model via the GAGA algorithm using cpp.

Fit a linear model via the GAGA algorithm using cpp.

rcpp_lm_gaga(
  X,
  y,
  s_alpha,
  s_itrNum,
  s_thresh,
  s_QR_flag,
  s_flag,
  s_lamda_0,
  s_fix_sigma,
  s_sigm2_0,
  s_fdiag,
  s_frp
)

`X`	Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of `X` must be all 1s.
`y`	Quantitative response N*1 matrix.
`s_alpha`	Hyperparameter. The suggested value for alpha is 2 or 3.
`s_itrNum`	The number of iteration steps. In general, 20 steps are enough.
`s_thresh`	Convergence threshold for beta Change, if `max(abs(beta-beta_old))<threshold`, return.
`s_QR_flag`	It identifies whether to use QR decomposition to speed up the algorithm.
`s_flag`	It identifies whether to make model selection. The default is `TRUE`.
`s_lamda_0`	The initial value of the regularization parameter for ridge regression.
`s_fix_sigma`	It identifies whether to update the variance estimate of the Gaussian noise or not.
`s_sigm2_0`	The initial variance of the Gaussian noise.
`s_fdiag`	It identifies whether to use diag Approximation to speed up the algorithm.
`s_frp`	Pre-processing by OMP method to reduce the number of parameters

Coefficient vector

[Package GAGAs version 0.6.2 Index]