A function to perform prediction, using an x matrix and the output of the "scout" function.

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`object` |
The results of a call to the "scout" function. The coefficients that are part of this object will be used for making predictions. |

`newx` |
The new x at which predictions should be made. Can be a vector of length ncol(x), where x is the data on which scout.obj was created, or a matrix with ncol(x) columns. |

`...` |
Additional arguments to predict |

`yhat` |
If newx was a vector, then a matrix will be returned, with dimension length(lam1s)xlength(lam2s) (where lam1s and lam2s are attributes of scout.obj). The (i,j) element of this matrix will correspond to tuning parameter values (lam1s[i], lam2s[j]). If newx is a matrix, then an array of dimension nrow(newx)xlength(lam1s)xlength(lam2s) will be returned. |

Daniela M. Witten and Robert Tibshirani

Witten, DM and Tibshirani, R (2008) Covariance-regularized regression and classification for high-dimensional problems. Journal of the Royal Statistical Society, Series B 71(3): 615-636. <http://www-stat.stanford.edu/~dwitten>

scout, cv.scout

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
library(lars)
data(diabetes)
attach(diabetes)
# Split data into training and test set
training <- sample(nrow(x2),floor(nrow(x2)/2))
xtrain <- x2[training,]
ytrain <- y[training]
xtest <- x2[-training,]
ytest <- y[-training]
# Done splitting data into training and test set
# Do cross-validation to determine best tuning parameter values for Scout(1,1)
## Not run: cv.out <- cv.scout(xtrain,ytrain,p1=1,p2=1, lam1s=seq(0.001,.15,len=5),K=4)
## Not run: print(cv.out)
# Done cross-validation
## Fit Model
#scout.object <- scout(xtrain,ytrain,p1=1,p2=1,lam1s=cv.out$bestlam1,lam2s=cv.out$bestlam2)
#print(scout.object)
## Done Fitting Model
## Predict on test data, and report MSE
#yhats <- predict(scout.object,xtest)
#print(mean((yhats-ytest)^2))
detach(diabetes)
``` |

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