Hi to everyone.
I wish to discuss the following points:
1.) NEED HELP to compile
2.) ANOVA
2.) ANOVA
=========
I will start with point 2. Should I open a new thread for this, by the way?
I have appended a draft implementation of the *ANOVA function*, please
see the code. There are still some unfinished issues, BUT I need help
for those.
- the ANOVA code proper is finished
- depending on how to implement the variable arrays, it might become
pertinent to keep track of array length
-- IF implementing as vectors, NO such need
- some issues at initializing the function: like retrieving how many
parameters and the like;
- I will discuss in a later post my ideas on how to implement the output
ANOVA is probably one of the last functions that could be implemented
directly in Calc (Fisher exact test would be another one). All other
advanced statistical functions should be best implemented through
external software (e.g. R).
1.) COMPILING
============
Niklas Nebel wrote:
OOo can now be built on Windows using only free tools. Noel just
posted something at
http://noelpower.blogs.ie/2006/11/10/hurray-now-you-can-build-with-free-compiler-on-windows/.
I haven't tried it myself, though.
I have NO idea how this goes. I need help. As I previously mentioned, I
am NOT an IT professional. It just happens that I know some C++.
If anybody can compile this, I would be very thankful. It really
corrects a bug inside Calc.
Kind regards,
Leonard Mada
void ScInterpreter::ScANOVA()
{
// WE GET EITHER A SINGLE MATRIX WHERE EVERY COLUMN IS A SEPARATE
VARIABLE
// DISADVANTAGE: ONLY ONE COLUMN PER VARIABLE
// OR MULTIPLE MATRICES, EACH MATRIX IS ONE VARIABLE
// DISADVANTAGE:
// CALC FUNCTIONS ACCEPT ONLY 30 PARAMS
// SO THERE ARE AT MOST 30 VARIABLES
SCSIZE iVarNr = /* NUMBER OF PARAMETERS */; // NUMBER OF VARIABLES
if ( iVarNr == 0 /* NO PARAMETERS */)
return; // EXIT
if ( iVarNr == 1 /* ONLY ONE PARAMETER */ )
ScMatrixRef pMat = GetMatrix();
if (!pMat) {
// NO DATA MATRIX - INVALID PARAMETERS
SetIllegalParameter();
return; }
SCSIZE nC, nR;
// WE HAVE ONLY ONE MATRIX
// WE CONSIDER EVERY COLUMN AS A SEPARATE DATA SET
pMat->GetDimensions(nC, nR);
iVarNr = nC;
nC = 1; // NOT REALLY NEEDED
ScMatrixRef pMat[iVarNr];
for(size_t i=0; i<iVarNr; i++) {
ScMatrixRef pMat[i] = /* COLUMN i FROM THE ORIGINAL
MATRIX pMat */
}
else {
ScMatrixRef pMat[iVarNr];
for(size_t i=0; i<iVarNr; i++) {
ScMatrixRef pMat[i] = GetMatrix();
if (!pMat[i]) {
// NO DATA MATRIX - INVALID PARAMETERS
SetIllegalParameter();
return; }
}
}
if( iVarNr == 1 ) {
SetNoValue();
return; // ONLY ONE VARIABLE - ANOVA NOT POSSIBLE
}
SCSIZE nC, nR; // DIMENSIONS OF ACTIVE MATRIX
SCSIZE dfB = 0; // DEGREES OF FREEDOM
SCSIZE dfE = 0; // DEGREES OF FREEDOM
SCSIZE N = 0; // TOTAL NUMBER OF DATA VALUES
double fMSB = 0.0; // THIS IS INTER-GROUP VARIANCE
double fMSE = 0.0; // THIS IS INTRA-GROUP VARIANCE (DUE TO ERROR)
SCSIZE iCount = 0; // NUMBER OF THE CURRENT VARIABLE
SCSIZE jCount = 0; // NUMBER OF VALUES FOR EACH VARIABLE
double fSumM = 0.0; // THIS IS THE GRAND MEAN
double fSumX[iVarNr] ; // THIS STORES THE MEANS FOR THE INDIVIDUAL
VARIABLES
double fValX[iVarNr] [ /* WE DO NOT KNOW THIS */ ]; // THE VALUES
for (; iCount < iVarNr; iCount++) {
pMat[iCount]->GetDimensions(nC, nR);
fSumX[iCount] = 0.0; // INITIALIZE THE SUM
for (SCSIZE i = 0; i < nC; i++)
for (SCSIZE j = 0; j < nR; j++)
{
if (!pMat[iCount]->IsString(i,j))
{
fValX[iCount][jCount] =
pMat[iCount]->GetDouble(i,j);
fSumX[iCount] +=
fValX[iCount][jCount];
jCount++;
}
}
fSumM += fSumX[iCount];
fSumX[iCount] = fSumX[iCount] / jCount; // THIS IS THE MEAN
N += jCount;
jCount = 0; // RESET jCount FOR NEXT VARIABLE
} // END OUTER FOR LOOP
if (iCount < 2)
SetNoValue();
else {
dfB = iCount -1;
dfE = N - iCount;
fSumM = fSumM / N; // THIS IS THE GRAND MEAN
for(SCSIZE i = 0; i < iCount; i++) {
for(SCSIZE j = 0; j < /* INDIVIDUAL GROUP SIZE */; j++)
{
// GROUPS MAY HAVE DIFFERENT SIZES
fMSE += (fValX[iCount][jCount] - fSumX[iCount])
* (fValX[iCount][jCount] - fSumX[iCount]);
// fMSB += (fSumM - fSumX[iCount]) * (fSumM -
fSumX[iCount]);
// TO AVOID MORE COMPUTATIONS WE CAN CALCULATE
fMSB OUTSIDE THIS LOOP
}
fMSB += /* INDIVIDUAL GROUP SIZE */ * (fSumM -
fSumX[iCount]) * (fSumM - fSumX[iCount]);
}
fMSB = fMSB / dfB;
fMSE = fMSE / dfE;
PushDouble( fMSB/fMSE );
// WE STILL NEED TO INTERPRET fMSB/fMSE USING THE F STATISTICS
}
}
---------------------------------------------------------------------
To unsubscribe, e-mail: [EMAIL PROTECTED]
For additional commands, e-mail: [EMAIL PROTECTED]
