xy_Obj {ffmanova}R Documentation

Creation of a design-with-responses object

Description

The function takes an object created by x_Obj as input and add response values. Further initial computations for prediction and testing is made.

Usage

xy_Obj(xObj, Y)

Arguments

xObj object created by x_Obj
Y response matrix

Details

Traditionally, sums of squares and cross-products (SSC) is the multivariate generalisation of sums of squares. When there is a large number of responses this representation is inefficient and therefore linear combinations of observations (Langsrud, 2002) is stored instead, such as errorObs. The corresponding SSC matrix can be obtained by t(errorObs)%*%errorObs. When there is a large number of observations the errorObs representation is also inefficient, but it these cases it is possible to chose a representation with several zero rows. Then, errorObs is stored as a two-component list: A matrix containing the nonzero rows of errorObs and an integer representing the degrees of freedom for error (number of rows in the full errorObs matrix).

Value

A list with components

xObj same as input
Y same as input
ssTotFull equals sum(Y^2)
ssTot equals sum((center(Y))^2). That is, the total sum of squares summed over all responses.
ss Sums of squares summed over all responses.
Beta Output from linregEst where xObj$D_om is the regressor matrix.
Yhat fitted values
YhatStd standard deviations of fitted values
msError mean square error of each response
errorObs Error observations that can be used in multivariate testing
hypObs Hypothesis observations that can be used in multivariate testing

Author(s)

Øyvind Langsrud and Bjørn-Helge Mevik

References

Langsrud, Ø. (2002) 50-50 Multivariate Analysis of Variance for Collinear Responses. The Statistician, 51, 305–317.

See Also

x_Obj.


[Package ffmanova version 0.1-1.1 Index]