multivar {multisensi}

A function to decompose the output data set and reduce its dimension

Description

The function multivar applies a multivariate method to decompose the output variables on a given basis.

Usage

multivar(simuls, dimension = NULL, reduction, centered = TRUE, 
         scale = TRUE, basis.args = list())

Arguments

simuls

a data.frame of size N x T, typically a set of N simulation outputs of length T

dimension

the number of variables to analyse, specified by an integer (for example 3) or by the minimal proportion of inertia (for example 0.95) to keep in the output decomposition

reduction

a function to decompose the multivariate output on a basis of smaller dimension

centered

logical value. If TRUE the output variables are centred.

scale

logical value. If TRUE the output variables are normalized.

basis.args

a list of arguments for the function given in the reduction argument. See the function help for more precision.

Value

A list containing:

H

a data.frame of size N x d, where d is the number of basis vectors. It contains the coefficients of the decomposition for each row of the simuls data.frame.

L

a matrix of size T x d. It contains the vectors of the user-defined basis.

sdev

standard deviations of the columns of H

nbcomp

number of components kept from the decomposition

SStot

total sums of squares of the simulations (after application of centered and scale)

centering

either 0 or the column averages of simuls

scaling

either 1 or sdY, depending on the scale argument

sdY

standard deviations of the columns of simuls

cor

correlation matrix (L*sdev), of size T x nbcomp

scale

kept in case the option scale has been changed in the function

importance

cumulated percentage of SS_H (sdev^2) with respect to SStot

call.info

list with the element reduction storing the name of the argument reduction

See Also

basis.ACP, basis.bsplines, basis.poly, basis.osplines

Examples

data(biomasseY)

res <- multivar(biomasseY, dimension=0.95, reduction=basis.ACP)