glmnet (version 1.1-4)

predict.glmnet: make predictions from a "glmnet" object.

Description

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

Usage

## S3 method for class 'glmnet':
predict(object, newx, s = object$lambda,
type=c("link","response","coefficients","class","nonzero"), exact =
FALSE, ...)
## S3 method for class 'glmnet':
coef(object,s=object$lambda, 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" or "multinomial" models; for "gaussian" models it gives the fitted values. Type "response" gi

exact

By default (exact=FALSE) the predict function uses linear interpolation to make predictions for values of s that do not coincide with those used in the fitting algorithm. Currently exact=TRUE is not implemented

...

Not used. Other arguments to predict.

Value

• The object returned depends on type.

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",...)

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent

See Also

glmnet, and print, and coef methods.

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)

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