xy_Obj  R Documentation 
The function takes an object created by x_Obj
as input and add
response values. Further initial computations for prediction and testing is
made.
xy_Obj(xObj, Y)
ffModelObj(
xObj,
Y,
modelMatrix,
modelTerms,
model,
xlev,
scaleY,
scaleX,
centerX,
isIntercept,
returnY = FALSE,
returnYhat = FALSE,
returnYhatStd = FALSE
)
xObj 
object created by 
Y 
response matrix 
modelMatrix 
Model matrix (output from 
modelTerms 
Model terms (model frame attribute) to be included in output. 
scaleY 
Values used to scale Y (see 
scaleX 
Values used to scale the model matrix (see 
centerX 
Values used to center the model matrix (see 
isIntercept 
A logical (whether model has intercept) to be included in output. 
returnY 
Matrix 
returnYhat 
Matrix 
returnYhatStd 
Standard errors, 
Traditionally, sums of squares and crossproducts (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 twocomponent 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).
A list with components
xObj 
same as input 
Y 
same as input 
ssTotFull 
equals 
ssTot 
equals

ss 
Sums of squares summed over all responses. 
Beta 
Output from 
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 
ffModelObj
is a rewrite of xy_Obj
with additional elements in output corresponding
to the additional parameters in input. Furthermore, Y
and YhatStd
is by default not included in output.
Øyvind Langsrud and BjørnHelge Mevik
Langsrud, Ø. (2002) 5050 Multivariate Analysis of Variance for Collinear Responses. The Statistician, 51, 305–317.
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