# predictIWLS: Predictions from ridge fits In multiridge: Fast Cross-Validation for Multi-Penalty Ridge Regression

## Description

Produces predictions from ridge fits for new data.

## Usage

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

## Arguments

 `IWLSfit` List, containing fits from either `IWLSridge` (linear, logistic ridge) or `IWLSCoxridge` `X1new` Matrix. Dimension `nnew x p_0`, representing unpenalized covariates for new data. `Sigmanew` Matrix. Dimensions `nnew x n`. Sample cross-product from penalized variables, usually computed by first applying `createXXblocks` and then `SigmaFromBlocks`.

## Details

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

## Value

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:
 ``` 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) ```