# linear_DAGDNE: Double-Adjacency Graphs-based Discriminant Neighborhood... In Rdimtools: Dimension Reduction and Estimation Methods

 do.dagdne R Documentation

## Double-Adjacency Graphs-based Discriminant Neighborhood Embedding

### Description

Doublue Adjacency Graphs-based Discriminant Neighborhood Embedding (DAG-DNE) is a variant of DNE. As its name suggests, it introduces two adjacency graphs for homogeneous and heterogeneous samples accordaing to their labels.

### Usage

do.dagdne(
X,
label,
ndim = 2,
numk = max(ceiling(nrow(X)/10), 2),
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)


### Arguments

 X an (n\times p) matrix or data frame whose rows are observations. label a length-n vector of data class labels. ndim an integer-valued target dimension. numk the number of neighboring points for k-nn graph construction. preprocess an additional option for preprocessing the data. Default is "center". See also aux.preprocess for more details.

### Value

a named list containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

trfinfo

a list containing information for out-of-sample prediction.

projection

a (p\times ndim) whose columns are basis for projection.

Kisung You

### References

\insertRef

ding_double_2015Rdimtools

do.dne

### Examples

## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## try different numbers for neighborhood size
out1 = do.dagdne(X, label, numk=5)
out2 = do.dagdne(X, label, numk=10)
out3 = do.dagdne(X, label, numk=20)

## visualize
plot(out1$Y, main="nbd size=5", col=label, pch=19) plot(out2$Y, main="nbd size=10",col=label, pch=19)