# 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 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.

##### 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)
```

*Documentation reproduced from package glmnet, version 1.1-5, License: GPL-2*

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