predict.glmnet
From glmnet v1.1-5
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 = 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 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"
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 ofs
that do not coincide with those used in the fitting algorithm. Currentlyexact=TRUE
is not implemented - ...
- 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.
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)
Community examples
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