Description Usage Arguments Details Value Author(s) References See Also Examples
Similar to other predict methods, this functions predicts fitted values from a HDtweedie
object.
1 2 3 |
object |
fitted |
newx |
matrix of new values for |
s |
value(s) of the penalty parameter |
type |
type of prediction required:
|
... |
Not used. Other arguments to predict. |
s
is the new vector at which predictions are requested. If s
is not in the lambda sequence used for fitting the model, the predict
function will use linear interpolation to make predictions. The new values are interpolated using a fraction of predicted values from both left and right lambda
indices.
The object returned depends on type.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <weiqian@stat.umn.edu>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2013), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” submitted to Journal of Computational and Graphical Statistics.
coef
method
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # load HDtweedie library
library(HDtweedie)
# load auto data set
data(auto)
# fit the lasso
m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5)
# predicted mean response at x[10,]
print(predict(m0,type="response",newx=auto$x[10,]))
# define group index
group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21)
# fit the grouped lasso
m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5)
# predicted the log mean response at x[1:5,]
print(predict(m1,type="link",newx=auto$x[1:5,]))
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