predict.glmnet
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 inMatrix
package. This argument is not used fortype=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 ofs
not included in the original fit, these values ofs
are merged withobject$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"
ortype="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,
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)