Fitted "glmnetfit" object.

Matrix of new values for x at which predictions are to be made. Must be a matrix. This argument is not used for type = c("coefficients","nonzero").

Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.

Type of prediction required. Type "link" gives the linear predictors (eta scale); Type "response" gives the fitted values (mu scale). Type "coefficients" computes the coefficients at the requested values for s. Type "nonzero" returns a list of the indices of the nonzero coefficients for each value of s.

This argument is relevant only when predictions are made at values of s (lambda) different from those used in the fitting of the original model. If exact=FALSE (default), then the predict function uses linear interpolation to make predictions for values of s (lambda) that do not coincide with those used in the fitting algorithm. While this is often a good approximation, it can sometimes be a bit coarse. With exact=TRUE, these different values of s are merged (and sorted) with object$lambda, and the model is refit before predictions are made. In this case, it is required to supply the original data x= and y= as additional named arguments to predict() or coef(). The workhorse predict.glmnet() needs to update the model, and so needs the data used to create it. The same is true of weights, offset, penalty.factor, lower.limits, upper.limits if these were used in the original call. Failure to do so will result in an error.

If an offset is used in the fit, then one must be supplied for making predictions (except for type="coefficients" or type="nonzero").

This is the mechanism for passing arguments like x= when exact=TRUE; see exact argument.