pamr.adaptthresh {pamr} | R Documentation

## A function to adaptive choose threshold scales, for use in pamr.train

### Description

A function to adaptive choose threshold scales, for use in pamr.train

### Usage

    pamr.adaptthresh(object, ntries = 10, reduction.factor = 0.9, full.out = F)

### Arguments

`object` | The result of a call to pamr.train
`ntries` | Number of iterations to use in algorithm
`reduction.factor` | Amount by which a scaling is reduced in one step of the algorithm
`full.out` | Provide more detailed output

### Details

`pamr.adaptthresh` Adaptively searches for set of good threshold scales. The baseline (default) scale is 1 for each class. The idea is that for easy to classify classes, the threshold scale can be increased without increasing the error rate for that class, and resulting in fewer genes needed for the classification rule. The scalings from pamr.adaptthresh are then used in pamr.train, and pamr.cv. The results may be better than those obtained with the default values of threshold.scale.

### Author(s)

Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu

### References

Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu. "Diagnosis of multiple cancer types by shrunken centroids of gene expression" PNAS 2002 99:6567-6572 (May 14).

Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu (2002). Class prediction by nearest shrunken centroids,with applications to DNA microarrays. Stanford tech report.

### Examples

    set.seed(120)
    x <- matrix(rnorm(1000*20),ncol=20)
    y <- sample(c(1:4),size=20,replace=TRUE)
    mydata <- list(x=x,y=y)
    mytrain <-   pamr.train(mydata)
    new.scales <- pamr.adaptthresh(mytrain)

    mytrain2 <- pamr.train(mydata, threshold.scale=new.scales)

    myresults2 <- pamr.cv(mytrain2, mydata)

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