cpp_poisson_gaga {GAGAs} | R Documentation |
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 |
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 |
s_thresh |
Convergence threshold for beta Change, if |
s_flag |
It identifies whether to make model selection. The default is |
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.