xy_Obj {ffmanova} | 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)
xObj |
object created by x_Obj |
Y |
response matrix |
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).
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 |
Øyvind Langsrud and Bjørn-Helge Mevik
Langsrud, Ø. (2002) 50-50 Multivariate Analysis of Variance for Collinear Responses. The Statistician, 51, 305–317.