Plot_Partial_RSS
Location: ..\cubatch\Model_PARAFAC\private\Plot_Partial_RSS.m
function
[Axes,MO] = Plot_Partial_RSS (PlotStruct,ModelOut,MainFig);
Description:
It plots the sum of squares of the residuals vs the scalars in
the mode specified in PlotStruct.nummode.
Additional selections can be made so that subsets of variables (or
sample/batches) are used.
If only one sample/batch is selected and the mode is not the first, confidence
limits, computed with Residuals_CL, are
automatically displayed.
If 'leave one out' validation has been calculated it is asked which scores are
to be used: those from the complete models or the "predicted" ones.
When the model has been applied to new data this plot automatically applies to
the new data; the confidence limits are computed on the original
data set.
For the axes specified by
Axes the
'ModelOut'
application-defined data is set, and it contains a copy of ModelOut.(employed
by PARAFACDisplayInfo)
Inputs:
PlotStruct:
PlotStruct structure
ModelOut: ModelOut
structure for the current model
MainFig: main figure handle
Outputs:
Axes: handle of the axes where the
Residuals are plot
MO: ModelOut structure with the updated statistic. Empty if no new statistic was computed.
Called by:
Model_PARAFAC\PARAFACActivatePlots
NB. The
actual
call is made from
PARAFACPlot via feval
and not from
Model_PARAFAC\PARAFACActivatePlots.
Subroutines:
Internal: None
External:
cenwindow,
nshape,
residuals_cl,
selectmodel,
dispinfo
NB DispInfo is set as the
'ButtonDownFcn'
of the line plots when the residuals are displayed versus any mode but the first
one.
Author:
Giorgio Tomasi
Royal Agricultural and Veterinary University
MLI, LMT, Chemometrics group
Rolighedsvej 30
DK-1958 Frederiksberg C
Danmark
Last modified: 15-Oct-2002 11:28:08
Contact: Giorgio Tomasi, gt@kvl.dk
References
[1] - Jackson, J.E., Mudholkar, G.S., "Control Procedures for Residuals associated
with Principal Components Analysis", Technometrics, 21, (1979), 341
[2] - Nomikos P., Mac Gregor J.F., "Monitoring batch process using
multiway principal component analysis", AIChE journal, Vol 40, n°8, 1994,
1361-1373