Given x, coefficients and intercept, return linear predictions. Wrapper that works with both regular and sparse x. Only works for single set of coefficients and intercept.

`get_eta(x, beta, a0)`

x

Input matrix, of dimension `nobs x nvars`

; each row is an
observation vector. If it is a sparse matrix, it is assumed to be unstandardized.
It should have attributes `xm`

and `xs`

, where `xm(j)`

and
`xs(j)`

are the centering and scaling factors for variable j respsectively.
If it is not a sparse matrix, it is assumed to be standardized.

beta

Feature coefficients.

a0

Intercept.