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

In jeffwong/glmnet: Lasso and elastic-net regularized generalized linear models

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 = 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 fitted values. Type "response" gives the fitted probabilities for "binomial" or "multinomial", fitted mean for "poisson" and the fitted relative-risk for "cox"; for "gaussian" type "response" is equivalent to type "link". Type "coefficients" computes the coefficients at the requested values for s. Note that for "binomial" models, results are returned only for the class corresponding to the second level of the factor response. Type "class" applies only to "binomial" or "multinomial" models, and produces the class label corresponding to the maximum probability. Type "nonzero" returns a list of the indices of the nonzero coefficients for each value of s.
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 exact=FALSE (default), then the predict function uses linear interpolation to make predictions for values of s that do not coincide with those used in the fitting algorithm. Note that exact=TRUE is fragile when used inside a nested sequence of function calls. predict.glmnet() needs to update the model, and expects the data used to create it to be around.
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.

Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie <[e-mail]>

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/

See Also

glmnet, and print, and coef methods, and cv.glmnet.

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)

jeffwong/glmnet documentation built on May 19, 2019, 4:02 a.m.