statmod: forward – R documentation

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forward

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))

`y`	numeric response vector.
`x`	numeric matrix of covariates, candidates to be added to the regression.
`xkept`	numeric matrix of covariates to be included in the starting regression.
`intercept`	logical, should an intercept be added to `xkept`?
`nvar`	integer, number of covariates from `x` to add to the regression.

This function has the advantage that x can have many more columns than the length of y.

Integer vector of length nvar, giving the order in which columns of x are added to the regression.

Gordon Smyth

y <- rnorm(10)
x <- matrix(rnorm(10*5),10,5)
forward(y,x)

Statistical Modeling

v1.4.36

GPL-2 | GPL-3

Authors

Gordon Smyth [cre, aut], Yifang Hu [ctb], Peter Dunn [ctb], Belinda Phipson [ctb], Yunshun Chen [ctb]

Initial release

2021-05-10