# 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 glmnet
predict(object, newx, s = NULL,
type=c("link","response","coefficients","nonzero","class"), exact = FALSE, offset, ...)
# S3 method for 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
This argument is relevant only when predictions are made at values of

`s`

(lambda)*different*from those used in the fitting of the original model. If`exact=FALSE`

(default), then the predict function uses linear interpolation to make predictions for values of`s`

(lambda) that do not coincide with those used in the fitting algorithm. While this is often a good approximation, it can sometimes be a bit coarse. With`exact=TRUE`

, these different values of`s`

are merged (and sorted) with`object$lambda`

, and the model is refit before predictions are made. In this case, it is strongly advisable to supply the original data`x=`

and`y=`

as additional named arguments to`predict()`

or`coef()`

. The workhorse`predict.glmnet()`

needs to`update`

the model, and expects the data used to create it to be around. Although it will often work fine without supplying these additional arguments, it will likely break when used inside a nested sequence of function calls. If additional arguments such as`weights`

and`offset`

were used, these should be included by name as well.- offset
If an offset is used in the fit, then one must be supplied for making predictions (except for

`type="coefficients"`

or`type="nonzero"`

)- …
This is the mechanism for passing arguments like

`x=`

when`exact=TRUE`

; see`exact`

argument.

##### 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*, 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

```
# NOT RUN {
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 2.0-10, License: GPL-2*