coef.glmnet {glmnet} | R Documentation |

## Extract coefficients from a glmnet object

### Description

Similar to other predict methods, this functions predicts fitted values,
logits, coefficients and more from a fitted `"glmnet"`

object.

### Usage

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

### Arguments

`object` |
Fitted |

`s` |
Value(s) of the penalty parameter |

`exact` |
This argument is relevant only when predictions are made at
values of |

`...` |
This is the mechanism for passing arguments like |

`newx` |
Matrix of new values for |

`type` |
Type of prediction required. Type |

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

`gamma` |
Single value of |

### 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.

### Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani

Maintainer:
Trevor Hastie hastie@stanford.edu

### References

Friedman, J., Hastie, T. and Tibshirani, R. (2008)
*Regularization Paths for Generalized Linear Models via Coordinate
Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22*,
doi:10.18637/jss.v033.i01.

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011)
*Regularization Paths for Cox's Proportional
Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol.
39(5), 1-13*,
doi:10.18637/jss.v039.i05.

Glmnet webpage with four vignettes, https://glmnet.stanford.edu.

### 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)
```

*glmnet*version 4.1-8 Index]