predict.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': predict(object, newx, s = object$lambda, type=c("link","response","coefficients","class","nonzero"), exact = FALSE, ...) ## S3 method for class 'glmnet': coef(object,s=object$lambda, exact=FALSE, ...)
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" or "multinomial"
models; for "gaussian" models it gives the fitted
values. Type "response" gives the fitted probabilities for
"binomial" or "multinomial" ; 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 |
By default (exact=FALSE ) the predict function uses linear interpolation
to make predictions for values of s that do not coincide with
those used in the fitting algorithm. Currently exact=TRUE is
not implemented, but prints an error message telling the user how to
achieve the exact predictions. This is done my rerunning the algorithm
with the desired values interspersed (in order) with the values used in
the original fit. This is easily achieved via the R command
lamba=sort(c(object$lambda, new.lambda)) |
... |
Not used. Other arguments to predict. |
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
glmnet
, and print
, and coef
methods.
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)