predict for a cv.glmnet returns an error

Hi:

Here's what I got when I ran your code:

library('glmnet')

x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
cv.fit=cv.glmnet(x,y)
predict(cv.fit,newx=x[1:5,])

1
[1,] 0.1213114
[2,] 0.1213114
[3,] 0.1213114
[4,] 0.1213114
[5,] 0.1213114

coef(cv.fit)

21 x 1 sparse Matrix of class "dgCMatrix"
                    1
(Intercept) 0.1213114
V1          0.0000000
V2          0.0000000
V3          0.0000000
V4          0.0000000
V5          0.0000000
V6          0.0000000
V7          0.0000000
V8          0.0000000
V9          0.0000000
V10         0.0000000
V11         0.0000000
V12         0.0000000
V13         0.0000000
V14         0.0000000
V15         0.0000000
V16         0.0000000
V17         0.0000000
V18         0.0000000
V19         0.0000000
V20         0.0000000

### Check against the versions of the packages listed below:

sessionInfo()

R version 2.14.0 (2011-10-31)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
[1] glmnet_1.7.1   Matrix_1.0-1   lattice_0.20-0 ggplot2_0.8.9  proto_0.3-9.2
[6] reshape_0.8.4  plyr_1.6

loaded via a namespace (and not attached):
[1] tools_2.14.0


Dennis

Hi there,

I am trying to use predict() with an object returned by cv.glmnet(), and get
the following error:
no applicable method for 'predict' applied to an object of class "cv.glmnet"

What's wrong?

my code:

x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
cv.fit=cv.glmnet(x,y)
predict(cv.fit,newx=x[1:5,])
coef(cv.fit)

Thanks so much,

Asaf

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