gps | R Documentation |
A pre-specified number (i.e., maxvar) of covariates will be selected for generalized linear models by constructing maxcmp components with generalized POCRE. Each component will be constructed by selecting maxvar/macmp covariates which are most relevant to the response variable(s). Similar to pocrescreen, gps selects covariates for their top relevance to the response variable(s) without penalization.
gps(y, x, family="binomial", bc.method="optimal", x.include=NULL, weights=NULL, maxcmp=10, maxvar=NULL, tol = 1e-6, maxit = 100)
y |
n*q matrix, values of q response variables (allow for multiple response variables). |
x |
n*p matrix, values of p predicting variables (excluding the intercept). |
family |
Family objects as |
bc.method |
Bias correction method. |
x.include |
a vector of indices indicating covariates which should always be included in the model (so not counted into selected maxvar covariates). |
weights |
A vector, including a prespecified weight for each observation (set as 1/n by default). |
maxcmp |
maximum number of components to be constructed. |
maxvar |
maximum number of selected variables. |
tol |
tolerance of precision in iterations. |
maxit |
maximum number of iterations to be allowed. |
a vector of indices of selected covariates (excluding those in x.include).
Dabao Zhang, Zhongli Jiang, Yu-ting Chen, Department of Statistics, Purdue University
Zhang D, Lin Y, and Zhang M (2009). Penalized orthogonal-components regression for large p small n data. Electronic Journal of Statistics, 3: 781-796.
pocrescreen
.
# Binomial Data data(simbin) gps(simbin[,1], simbin[,-1], maxcmp=3, maxvar=9) gps(simbin[,1], simbin[,-1], x.include=103:104, maxcmp=3, maxvar=9) # Count Data data(simpoi) gps(simpoi[,1], simpoi[,-1], family='poisson',maxcmp=5,maxvar=10)
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