Forward Selection of Covariates for Multiple Regression
Fit a multi-group negative-binomial model to SAGE data, with Pearson estimation of the common overdispersion parameter.
forward(y, x, xkept=NULL, intercept=TRUE, nvar=ncol(x))
- numeric response vector.
- numeric matrix of covariates, candidates to be added to the regression.
- numeric matrix of covariates to be included in the starting regression.
- logical, should an intercept be added to
- integer, number of covariates from
xto add to the regression.
This function has the advantage that
x can have many more columns than the length of
- Integer vector of length
nvar, giving the order in which columns of
xare added to the regression.
y <- rnorm(10) x <- matrix(rnorm(10*5),10,5) forward(y,x)
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