Description Usage Arguments Details Value See Also Examples

View source: R/MultiLambdaCVfun.R

Produces predictions from ridge fits for new data.

1 | ```
predictIWLS(IWLSfit, X1new = NULL, Sigmanew)
``` |

`IWLSfit` |
List, containing fits from either |

`X1new` |
Matrix. Dimension |

`Sigmanew` |
Matrix. Dimensions |

Predictions rely purely on the linear predictors, and do not require producing the parameter vector.

Numerical vector of linear predictor for the test samples.

`IWLSridge`

(`IWLSCoxridge`

) for fitting linear and
logistic ridge (Cox ridge). `betasout`

for obtaining parameter
estimates.
`Scoring`

to evaluate the predictions. A full demo and data are available from:

https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
#Example below shows how to create the input argument Sigmanew (for simulated data)
#Simulate
Xbl1 <- matrix(rnorm(1000),nrow=10)
Xbl2 <- matrix(rnorm(2000),nrow=10)
Xbl1new <- matrix(rnorm(200),nrow=2)
Xbl2new <- matrix(rnorm(400),nrow=2)
#check whether dimensions are correct
nrow(Xbl1)==nrow(Xbl1new)
nrow(Xbl2)==nrow(Xbl2new)
ncol(Xbl1)==nrow(Xbl2)
ncol(Xbl1new)==ncol(Xbl2new)
#create cross-product
XXbl <- createXXblocks(list(Xbl1,Xbl2),list(Xbl1new,Xbl2new))
#suppose penalties for two data types equal 5,10, respectively
Sigmanew <- SigmaFromBlocks(XXbl,c(5,10))
#check dimensions (should be nnew x n)
dim(Sigmanew)
``` |

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