| 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.