This function builds the HICA tree up to a prespecified height providing the corresponding non-orthogonal bases.

1 | ```
basis_hica(X, maxlev = dim(X)[2] - 1, dim.subset = 512)
``` |

`X` |
Data matrix with nrow(X) observations and ncol(X) variables. |

`maxlev` |
The maximum level of the tree. This must be an integer between 1 and ncol(X)-1. The default value is set to ncol(X)-1. |

`dim.subset` |
The dimension of the subset used for the evaluation of the similarity index (i.e., distance correlation). If this it is greater than nrow(X) all the observations are used, unless a random subsample of |

`X` |
data matrix. |

`basis` |
a list with |

`aggregation` |
a matrix with |

The distance correlation is evaluated through the function `dcor`

of the package "energy". It becomes computationally unfeasible if the number of observations is too large. For this reason it is possibile to choose the dimension of the subsample to be used in the evaluation of the similarity matrix. By default the dimension is set to 512.

Piercesare Secchi, Simone Vantini, and Paolo Zanini.

P. Secchi, S. Vantini, and P. Zanini (2014). Hierarchical Independent Component Analysis: a multi-resolution non-orthogonal data-driven basis. *MOX-report 01/2014*, Politecnico di Milano.

`energy_hica`

, `similarity_hica`

, `extract_hica`

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 | ```
## Not run:
##########################################################
# Example - Independent sources and overlapping loadings #
##########################################################
c1=c(0,0,0,0,1,1)
c2=c(1,1,1,1,0,0)
c3=c(1,1,0,0,0,0)
s1=runif(400,0,20)
s2=runif(400,0,20)
s3=runif(400,0,20)
# Here we generate the simulated dataset
X=s1%*%t(c1)+s2%*%t(c2)+s3%*%t(c3)+rnorm(6*400,0,1)
# Here we perform HICA on the simulated dataset
basis=basis_hica(X,5)
# Here we plot the 3 main components of HICA basis
# (according to the energy criterium) for 4th level
energy=energy_hica(basis,6,5,plot=TRUE)
ex4=extract_hica(energy,3,4)
loa4=ex4$C
par( mfrow = c(3,1))
barplot(loa4[,1], ylim = c(-1, 1),main="HICA transform - Level 4",
ylab="1st component",xlab="Coordinate",names.arg=1:6,col="red",mgp=c(2.5,1,0))
barplot(loa4[,2], ylim = c(-1, 1),ylab="2nd component",
xlab="Coordinate",names.arg=1:6,col="green",mgp=c(2.5,1,0))
barplot(loa4[,3], ylim = c(-1, 1),ylab="3rd component",
xlab="Coordinate",names.arg=1:6,col="blue",mgp=c(2.5,1,0))
## End (Not run)
``` |

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