coef.glmnet {glmnet} | R Documentation |
Similar to other predict methods, this functions predicts fitted values,
logits, coefficients and more from a fitted "glmnet"
object.
## 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,
...
)
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 |
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",...)
The object returned depends on type.
Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer:
Trevor Hastie hastie@stanford.edu
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.
glmnet
, and print
, and coef
methods, and
cv.glmnet
.
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)