accTrainTest {hddplot} | R Documentation

## Two subsets of data each take in turn the role of test set

### Description

A division of data is specified, for use of linear discriminant analysis, into a training and test set. Feature selection and model fitting is formed, first with I/II as training/test, then with II/I as training/test

### Usage

    accTrainTest(x = Golub.BM, cl = cancer.BM, traintest = gp.id, nfeatures = NULL, print.acc = FALSE)

### Arguments

`x` | Matrix; rows are features, and columns are observations ('samples')
`cl` | Factor that classifies columns into groups that will classify the data for purposes of discriminant calculations
`traintest` | Values that specify a division of observations into two groups. In the first pass (fold), one to be training and the other test, with the roles then reversed in a second pass or fold.
`nfeatures` | integer: numbers of features for which calculations are required
`print.acc` | logical: should accuracies be printed?

### Details

### Value

`sub1.2` | row numbers of features, by order of values of the group separation measure, for the first subset (I) of the `x`
`acc1.2` | accuracies, with I as training set and II as test
`sub2.1` | row numbers of features, by order of values of the group separation measure, for the second subset (II) of the `x`
`acc2.1` | accuracies, with II as training set and I as test

### Note

### Author(s)

John Maindonald

### References

### See Also

### Examples

    mat <- matrix(rnorm(1000), ncol=20)
    cl <- factor(rep(1:3, c(7,9,4)))
    gp.id <- divideUp(cl, nset=2)
    accTrainTest(x=mat, cl=cl, traintest=gp.id,
                 nfeatures=1:16, print.acc=TRUE)

    ## The function is currently defined as
    function(x=Golub.BM, cl=cancer.BM, traintest=gp.id,
               nfeatures=NULL, print.acc=FALSE){
        traintest <- factor(traintest)
        train <- traintest==levels(traintest)[1]
        testset <- traintest==levels(traintest)[2]
        cl1 <- cl[train]
        cl2 <- cl[testset]
        ng1 <- length(cl1)
        ng2 <- length(cl2)
        maxg <- max(c(ng1-length(unique(cl1))-2,
                      ng2-length(unique(cl2))-2))
        if(is.null(nfeatures)){
          max.features <- maxg
          nfeatures <- 1:max.features
        } else
        {
          if(max(nfeatures)>maxg)nfeatures <- nfeatures[nfeatures<=maxg]
          max.features <- max(nfeatures)
        }
        ord1 <- orderFeatures(x, cl, subset=train)[1:max.features]
        ord2 <- orderFeatures(x, cl, subset=testset)[1:max.features]
        ord <- unique(c(ord1, ord2))
        sub1 <- match(ord1, ord)
        sub2 <- match(ord2, ord)
        df1 <- data.frame(t(x[ord, train]))
        df2 <- data.frame(t(x[ord, testset]))
        acc1 <- acc2 <- numeric(max(nfeatures))
        for(i in nfeatures){
          cat(paste(i, ":", sep=""))
          df1.lda <- lda(df1[, sub1[1:i], drop=FALSE], cl1)
          hat2 <- predict(df1.lda, newdata=df2[, sub1[1:i], drop=FALSE])$class
          tab <- table(hat2, cl2)
          acc1[i] <- sum(tab[row(tab)==col(tab)])/sum(tab)
          df2.lda <- lda(df2[, sub2[1:i], drop=FALSE], cl2)
          hat1 <- predict(df2.lda, newdata=df1[, sub2[1:i], drop=FALSE])$class
          tab <- table(hat1, cl1)
          acc2[i] <- sum(tab[row(tab)==col(tab)])/sum(tab)
        }
        cat("\n")
        if(print.acc){
          print(round(acc1,2))
          print(round(acc2,2))
        }
        maxacc1 <- max(acc1)
        maxacc2 <- max(acc2)
        sub1 <- match(maxacc1, acc1)
        sub2 <- match(maxacc2, acc2)
        nextacc1 <- max(acc1[acc1<1])
        nextacc2 <- max(acc1[acc1<2])
        lower1 <- maxacc1-sqrt(nextacc1*(1-nextacc1)/ng1)
        lower2 <- maxacc2-sqrt(nextacc2*(1-nextacc2)/ng2)
        lsub1 <- min((1:ng1)[acc1>lower1])
        lsub2 <- min((1:ng2)[acc2>lower2])
        lower <- c("Best accuracy, less 1SD  ",
                   paste(paste(round(c(lower1, lower2),2), c(lsub1, lsub2),
                               sep=" ("), " features)   ", sep=""))
        best <- c("Best accuracy",
                  paste(paste(round(c(maxacc1, maxacc2),2), c(sub1, sub2),
                              sep=" ("), " features)", sep=""))
        acc.df <- cbind(lower, best)
        dimnames(acc.df) <- list(c("Training/test split",
                                   "I (training) / II (test)    ",
                                   "II (training) / I (test)    "),c("",""))
        print(acc.df, quote=FALSE)
        invisible(list(sub1.2=ord1, acc1.2=acc1, sub2.1=ord2, acc2.1=acc2))
      }

[Package hddplot version 0.51 Index]