cpp_poisson_gaga {GAGAs}

Fit a poisson model via the GAGA algorithm using cpp

Description

Fit a poisson model the Global Adaptive Generative Adjustment algorithm

Usage

cpp_poisson_gaga(
  X,
  y,
  s_alpha,
  s_itrNum,
  s_thresh,
  s_flag,
  s_lamda_0,
  s_fdiag,
  s_subItrNum
)

Arguments

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. In order to run the program stably, it is recommended that the value of X should not be too large. It is recommended to preprocess all the items in X except the intercept item by means of preprocessing, so that the mean value of each column is 0 and the standard deviation is 1/ colnum(X).

y

Non-negative count response vector.

s_alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

s_itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

s_thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

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. The running result of the algorithm is not sensitive to this value.

s_fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

s_subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector.