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, 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/
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)