predict.glmnet

From glmnet v1.9-3 by Trevor Hastie

make predictions from a "glmnet" object.

Similar to other predict methods, this functions predicts fitted values, logits, coefficients and more from a fitted "glmnet" object.

Keywords: models, regression

Usage

## S3 method for class 'glmnet':
predict(object, newx, s = NULL,
type=c("link","response","coefficients","nonzero","class"), exact = FALSE, offset, ...)
## S3 method for class 'glmnet':
coef(object,s=NULL, exact=FALSE, ...)

Arguments

object: Fitted "glmnet" model object.
newx: Matrix of new values for x at which predictions are to be made. Must be a matrix; can be sparse as in Matrix package. This argument is not used for type=c("coefficients","nonzero")
s: Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type: Type of prediction required. Type "link" gives the linear predictors for "binomial", "multinomial", "poisson" or "cox" models; for "gaussian" models it gives the f
exact: If exact=TRUE, and predictions are to made at values of s not included in the original fit, these values of s are merged with object$lambda, and the model is refit before predictions are made. If
offset: If an offset is used in the fit, then one must be supplied for making predictions (except for type="coefficients" or type="nonzero")
...: Not used. Other arguments to predict.

Details

The shape of the objects returned are different for "multinomial" objects. This function actually calls NextMethod(), and the appropriate predict method is invoked for each of the three model types. coef(...) is equivalent to predict(type="coefficients",...)

Value

The object returned depends on type.

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, http://www.stanford.edu/~hastie/Papers/glmnet.pdf Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010 http://www.jstatsoft.org/v33/i01/ Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13 http://www.jstatsoft.org/v39/i05/

Examples

x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
g2=sample(1:2,100,replace=TRUE)
g4=sample(1:4,100,replace=TRUE)
fit1=glmnet(x,y)
predict(fit1,newx=x[1:5,],s=c(0.01,0.005))
predict(fit1,type="coef")
fit2=glmnet(x,g2,family="binomial")
predict(fit2,type="response",newx=x[2:5,])
predict(fit2,type="nonzero")
fit3=glmnet(x,g4,family="multinomial")
predict(fit3,newx=x[1:3,],type="response",s=0.01)

Documentation reproduced from package glmnet, version 1.9-3, License: GPL-2

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