# Plot predictions for both a I/II train/test split, and the reverse

### 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. Two graphs are plotted – for the I (training) /II (test) scores, and for the II/I scores.

### Usage

1 2 3 | ```
plotTrainTest(x, nfeatures, cl, traintest,
titles = c("A: I/II (train with I, scores are for II)",
"B: II/I (train with II, scores are for I)"))
``` |

### Arguments

`x` |
Matrix; rows are features, and columns are observations ('samples') |

`nfeatures` |
integer: numbers of features for which calculations are required |

`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. |

`titles` |
A character vector of length 2 giving titles for the two graphs |

### Value

Two graphs are plotted.

### Author(s)

John Maindonald

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
mat <- matrix(rnorm(1000), ncol=20)
cl <- factor(rep(1:3, c(7,9,4)))
gp.id <- divideUp(cl, nset=2)
plotTrainTest(x=mat, cl=cl, traintest=gp.id, nfeatures=c(2,3))
## The function is currently defined as
function(x, nfeatures, cl, traintest,
titles=c("A: I/II (train with I, scores are for II)",
"B: II/I (train with II, scores are for I)")){
oldpar <- par(mfrow=c(1,2), pty="s")
on.exit(par(oldpar))
if(length(nfeatures)==1)nfeatures <- rep(nfeatures,2)
traintest <- factor(traintest)
train <- traintest==levels(traintest)[1]
testset <- traintest==levels(traintest)[2]
cl1 <- cl[train]
cl2 <- cl[testset]
nf1 <- nfeatures[1]
ord1 <- orderFeatures(x, cl, subset=train)
df1 <- data.frame(t(x[ord1[1:nf1], train]))
df2 <- data.frame(t(x[ord1[1:nf1], testset]))
df1.lda <- lda(df1, cl1)
scores <- predict(df1.lda, newdata=df2)$x
scoreplot(scorelist=list(scores=scores, cl=cl2,
nfeatures=nfeatures[1], other=NULL, cl.other=NULL),
prefix.title="")
mtext(side=3, line=2, titles[1], adj=0)
nf2 <- nfeatures[2]
ord2 <- orderFeatures(x, cl, subset=testset)
df2 <- data.frame(t(x[ord2[1:nf2], testset]))
df1 <- data.frame(t(x[ord2[1:nf2], train]))
df2.lda <- lda(df2, cl2)
scores <- predict(df2.lda, newdata=df1)$x
scoreplot(scorelist=list(scores=scores, cl=cl1,
nfeatures=nfeatures[2], other=NULL, cl.other=NULL),
prefix.title="")
mtext(side=3, line=2, titles[2], adj=0)
}
``` |