predict.grridge {GRridge} | R Documentation

## Predictions for new samples

### Description

Returns predictions for new samples from a `grridge` object

### Usage

    ## S3 method for class 'grridge'
    predict(object, datanew, printpred = FALSE, dataunpennew=NULL,
            responsetest=NULL, recalibrate=FALSE, ...)

### Arguments

`object` | A model object resulted from the grridge function.
`datanew` | Vector or data frame. Contains the new data. For a data frame: columns are samples, rows are variables (features).
`printpred` | Boolean. Should the predictions be printed on the screen?
`dataunpennew` | Vector or data frame. Optional new data for unpenalized variables. NOTE: columns are covariates, rows are samples.
`responsetest` | Factor, numeric, binary or survival. Response values of test samples. The number of response values should equal `ncol(datanew)`. Only relevant if `recalibrate=TRUE`.
`recalibrate` | Boolean. Should the prediction model be recalibrated on the test samples? Only implemented for logistic and linear regression with only penalized covariates.
`...` | There is no further argument is used.

### Details

This function returns predictions of the response using the `grridge` output. It should be applied to samples NOT used for fitting of the models. About `recalibrate`: we noticed that recalibration of the linear predictor using a simple regression (with intercept and slope) can improve predictive performance, e.g. in terms of brier score or mean square error (not in terms of AUC, which is rank-based). We recommend to use it for large enough test sets (say >= 25 samples), in particular when one suspects that the test set could have somewhat different properties than the training set.

### Value

A matrix containing the predictions from all models available in `grridge`.

### Author(s)

Mark A. van de Wiel

### References

Mark van de Wiel, Tonje Lien, Wina Verlaat, Wessel van Wieringen, Saskia Wilting. (2016). Better prediction by use of co-data: adaptive group-regularized ridge regression. Statistics in Medicine, 35(3), 368-81.

### See Also

Cross-validated predictions: `grridgeCV`. Examples: `grridge`.

### Examples

    #data(dataFarkas)

    #firstPartition <- CreatePartition(CpGannFarkas)

    #sdsF <- apply(datcenFarkas,1,sd)
    #secondPartition <- CreatePartition(sdsF,decreasing=FALSE, uniform=TRUE, grsize=5000)

    ## Concatenate two partitions
    #partitionsFarkas <- list(cpg=firstPartition, sds=secondPartition)

    ## A list of monotone functions from the corresponding partition
    #monotoneFarkas <- c(FALSE,TRUE)

    #Hold-out first two samples
    #testset <- datcenFarkas[,1:2]
    #resptest <- respFarkas[1:2]

    #trainingset <- datcenFarkas[,-(1:2)]
    #resptraining <- respFarkas[-(1:2)]

    #grFarkas <- grridge(trainingset,resptraining,optl=5.680087,
    #                    partitionsFarkas,monotone=monotoneFarkas)

    ## Prediction of the grridge model to the test samples

    #Standardize variables in testset, becasuse by default standardizeX = TRUE in grridge function.
    #Here, test set is small so we use the summaries of the training set. If the test set
    #is large one may opt to standardize wrt to the test set itself.

    #sds <- apply(trainingset,1,sd)
    #sds2 <- sapply(sds,function(x) max(x,10^{-5}))
    #teststd <- (testset-apply(trainingset,1,mean))/sds2

    #yhat <- predict.grridge(grFarkas,teststd)

[Package GRridge version 1.6.0 Index]