# coef.glmnet

##### Extract coefficients 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
coef(object, s = NULL, exact = FALSE, ...)
```# S3 method for glmnet
predict(object, newx, s = NULL, type = c("link",
"response", "coefficients", "nonzero", "class"), exact = FALSE,
newoffset, ...)

# S3 method for relaxed
predict(object, newx, s = NULL, gamma = 1,
type = c("link", "response", "coefficients", "nonzero", "class"),
exact = FALSE, newoffset, ...)

##### Arguments

- object
Fitted

`"glmnet"`

model object or a`"relaxed"`

model (which inherits from class "glmnet").- s
Value(s) of the penalty parameter

`lambda`

at which predictions are required. Default is the entire sequence used to create the model.- 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. Not available for`"relaxed"`

objects. 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 required 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 so needs the data used to create it. The same is true of`weights`

,`offset`

,`penalty.factor`

,`lower.limits`

,`upper.limits`

if these were used in the original call. Failure to do so will result in an error.- …
This is the mechanism for passing arguments like

`x=`

when`exact=TRUE`

; see`exact`

argument.- 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")`

- 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`

.- newoffset
If an offset is used in the fit, then one must be supplied for making predictions (except for

`type="coefficients"`

or`type="nonzero"`

)- gamma
Single value of

`gamma`

at which predictions are required, for "relaxed" objects.

##### 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*, https://web.stanford.edu/~hastie/Papers/glmnet.pdf
*Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010*
https://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* https://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 3.0-2, License: GPL-2*