ostat.c

图像处理的压缩算法

	vector<string> vDatasetNames;
	vDatasetNames.SetSize( nData );

	// Vector containing the number of datapoints in each selected dataset
	vector<int> vNPTS;
	vNPTS.SetSize( nData );

	// Vector containing the mean of each selected dataset
	vector<double> vMEAN;
	vMEAN.SetSize( nData );

	// Vector containing the standard deviation of each selected dataset
	vector<double> vSD;
	vSD.SetSize( nData );

	// Vector containing the standard error of each selected dataset
	vector<double> vSE;
	vSE.SetSize( nData );

	// *** Construct All Test Specific Strings and Vectors needed for Summary Table ***
	// Default value of case 2
	strMainHDR = TT2_PAIRED_SUMMARY_MAIN_HDR;
	switch( iTest )
	{
		case 0:
			// One Sample tTest...get summary statistics for One Sample t-Test
			strMainHDR = TT1_SUMMARY_MAIN_HDR;
			LT_get_str( "%B", szTemp, 40 ); strDatasetName = szTemp;
			vDatasetNames[0] = strDatasetName; 
			LT_evaluate( "sum.n", &dGetVar ); vNPTS[0] = (int) dGetVar;
			LT_evaluate( "sum.mean", &dGetVar ); vMEAN[0] = dGetVar;
			LT_evaluate( "sum.sd", &dGetVar ); vSD[0] = dGetVar;
			LT_evaluate( "sum.sd/sqrt(sum.n)", &dGetVar ); vSE[0] = dGetVar;
			break;
		case 1:
			// Two Sample Independent tTest...continue on through case 2
			strMainHDR = TT2_INDEPENDENT_SUMMARY_MAIN_HDR;
		case 2:
			// Two Sample Paired tTest...code common with case 1
			// Get summary statistics for the first selected dataset of the Two Sample t-Test
			LT_get_str( "%A", szTemp, 40 ); strDatasetName = szTemp;
			vDatasetNames[0] = TT2_SAMPLE1 + strDatasetName; 
			LT_get_var( "n1", &dGetVar ); vNPTS[0] = (int) dGetVar;
			LT_get_var( "ave1", &dGetVar ); vMEAN[0] = dGetVar;
			LT_get_var( "sd1", &dGetVar ); vSD[0] = dGetVar;
			LT_get_var( "se1", &dGetVar ); vSE[0] = dGetVar;

			// Get summary statistics for the second selected dataset of the Two Sample t-Test
			LT_get_str( "%B", szTemp, 40 ); strDatasetName = szTemp;
			vDatasetNames[1] = TT2_SAMPLE2 + strDatasetName; 
			LT_get_var( "n2", &dGetVar ); vNPTS[1] = (int) dGetVar;
			LT_get_var( "ave2", &dGetVar ); vMEAN[1] = dGetVar;
			LT_get_var( "sd2", &dGetVar ); vSD[1] = dGetVar;
			LT_get_var( "se2", &dGetVar ); vSE[1] = dGetVar;
			break;
		case 3:
			// One-Way ANOVA
			strMainHDR = AN1_SUMMARY_MAIN_HDR;
			
			// Get summary statistics for all selected ANOVA1 datasets
			Dataset dsNPTS( "_NPTS" ); vNPTS = dsNPTS; dsNPTS.Detach();
			Dataset dsMEAN( "_MEAN" ); vMEAN = dsMEAN; dsMEAN.Detach();
			Dataset dsSD( "_SD" ); vSD = dsSD; dsSD.Detach();
			double dNpts;
			string strDB;
						
			// Read the names of all selected datasets...
			for( ii = 0; ii < nData; ii++ )
			{
				dNpts = (double) vNPTS[ii];
				vSE[ii] = vSD[ii] / sqrt( dNpts );
			
				strDB.Format( "%%A=ANOVA1Way!SelectedDataLBX.v%d$", ii + 1 );
				LT_execute( strDB );
				LT_get_str( "%A", szTemp, 40 );
				strDatasetName = szTemp;
				vDatasetNames[ii] = strDatasetName;
			}
			
			strDatasetName = AN1_DATASET;
			break;
	}

	int iRow, iCol;
	string strTablePath;
	string strTableName;
	Worksheet wksSummaryTable;
		
	// Create and attach to a temporary Summary Statistics output worksheet
	strTablePath = GetFullPath( "osSummaryStat.OGW", OSTAT_OGW_SUBFOLDER, TRUE );
	if( !strTablePath.IsFile() )
	{
		Type_ErrorMsg1( OSTAT_FILE_NOT_FOUND_ERROR_MSG, strTablePath );
		return OSTAT_ABORT_NO_ERROR_MSG;
	}
	wksSummaryTable.Open( strTablePath, CREATE_TEMP );
	strTableName = wksSummaryTable.GetPage().GetName();

	// All row and column numbers are indexed from 0
	
	// *** Change Main Header ***
	// Change main header in row 1 and column 1
	iRow = 0;
	iCol = 0;
	wksSummaryTable.SetCell( iRow, iCol, strMainHDR );
	
	if( iTest==3 ) // If One-Way ANOVA...
	{
		// Change "Sample" to "Dataset" in row 5 column 2
		iRow = 4;
		iCol = 1;
		wksSummaryTable.SetCell( iRow, iCol, strDatasetName );
	}
	
	// ***  Add nData Summary Rows and Output Summary Statistics *** 
	// Insert nData blank lines in summary table at row 7
	iRow = 6;
	Type_Insert_Blank_Lines_in_Wks( wksSummaryTable, iRow, nData );
		
	// Loop on samples/selected datasets starting in row 7
	for( ii = 0; ii < nData; iRow++, ii++ )
	{
		// Output sample or selected dataset name in column 2
		iCol = 1;
		strDatasetName = vDatasetNames[ii];
		wksSummaryTable.SetCell( iRow, iCol, strDatasetName );
		
		// Output number of data points in column 3
		iCol++;
		wksSummaryTable.SetCell( iRow, iCol, vNPTS[ii] );
	
		// Output mean in column 4
		iCol++;
		wksSummaryTable.SetCell( iRow, iCol, vMEAN[ii] );

		// Output standard deviation in column 5
		iCol++;
		wksSummaryTable.SetCell( iRow, iCol, vSD[ii] );
	
		// Output standard error in column 6
		iCol++;
		wksSummaryTable.SetCell( iRow, iCol, vSE[ii] );
	}
	
	if( mod( iTest, 3 ) != 0 ) // If Two Sample t-Test (iTest=1 or 2)...
	{
		// Add blank row after separator table
		iRow++;
		Type_Insert_Blank_Lines_in_Wks( wksSummaryTable, iRow );
		
		// Output difference of sample means after separator table
		iCol = 1;
		wksSummaryTable.SetCell( iRow, iCol, TT2_DIF_OF_MEANS );
		iCol = 3;
		wksSummaryTable.SetCell( iRow, iCol, vMEAN[0] - vMEAN[1] );
	}

	// GJL 11/14/02 v7.0434 USE_OC_WKS_TYPE_NOT_LT
	// Send output Summary Statistics worksheet to Results Log
	wksSummaryTable.Type( TYPETARGET_OUTPUTLOG, NULL, 7 );

	return OSTAT_NO_ERROR;	
}

/**
		Function to output to the Results Log Actual and Hypothetical Power values for the
		One and Two Sample t-Test and the One-Way ANOVA dialog boxes.
	Example:
		See the [DisplayPower] section in TTestMean1Sample.OGS, TTestMean2Sample.OGS, and
		ANOVA1Way.OGS for sample calls. 
	Parameters:
		iPower=Bitwise flag indicating whether no power (0), only actual power (1), or actual and
			hypothetical powers (3) are to be output
		dAlpha=Alpha used in power computations
		iSSType=Flag for sample size type:0 for "Total" or 1 for "Individual"
		iActualSS=Actual sample size
		dActualPower=Actual power value
		strHypotSSDataName=Name of dataset containing hypothetical sample sizes
		strHypotPowerDataName=Name of dataset containing hypothetical powers
	Return:
		Outputs Actual and Hypothetical Power values to Results Log for the One and
		Two Sample t-Test and the One-Way ANOVA dialog boxes. Returns OSTAT_NO_ERROR
		on successful exit.
*/
int osPower_Output_to_ResultsLog( int iPower, double dAlpha, int iSSType, int iActualSS,
	 double dActualPower, string strHypotSSDataName, string strHypotPowerDataName )
{
	int ii, iSize;
	int iRow, iCol;
	string strTablePath;
	string strTableName;
	Worksheet wksPowerTable;

	// Create and attach to a temporary Power output worksheet
	strTablePath = GetFullPath( "osPowerAnalysis.OGW", OSTAT_OGW_SUBFOLDER, TRUE );
	if( !strTablePath.IsFile() )
	{
		Type_ErrorMsg1( OSTAT_FILE_NOT_FOUND_ERROR_MSG, strTablePath );
		return OSTAT_ABORT_NO_ERROR_MSG;
	}
	wksPowerTable.Open( strTablePath, CREATE_TEMP );
	strTableName = wksPowerTable.GetPage().GetName();
	
	// All row and column numbers are indexed from 0
	
	// *** Finish Power Header ***
	iRow = 2; // Sample size type in row 3
	switch( iSSType )
	{
		case -1:
			// If One Sample t-Test then no sample size type row...delete row in *.OGW
			wksPowerTable.DeleteRow( iRow );
			iRow--;
			break;

		case 0:
			// If Two Sample Independent t-Test then sample size type is Total (most common)
			// so leave alone...Total is in *.OGW
			break;

		case 1:
			// If Two Sample Paired t-Test then sample size type is Individual so overwrite
			// Total with Individual
			iCol = 2; // Output "Individual" in column 3
			wksPowerTable.SetCell( iRow, iCol, TT2_INDIVIDUAL );
			break;
	}

	// *** Output Actual Power Row ***	
	// Insert one blank line in power table for actual power
	iRow += 3; // Jump to row for actual power
	Type_Insert_Blank_Lines_in_Wks( wksPowerTable, iRow );

	// Output actual alpha in column 2
	iCol = 1;
	wksPowerTable.SetCell( iRow, iCol, dAlpha );

	// Output actual sample size in column 3
	iCol++;
	wksPowerTable.SetCell( iRow, iCol, iActualSS );
		
	// Output actual power in column 4
	iCol++;
	wksPowerTable.SetCell( iRow, iCol, dActualPower );
	
	// Output "(actual)" in column 5
	iCol++;
	wksPowerTable.SetCell( iRow, iCol, OSTAT_POWER_ACTUAL );

	// *** Output Hypothetical Power Rows ***	
	if( iPower & 2 ) // If hypothetical powers were computed...
	{
		// Get hypothetical power dataset
		Dataset dsSSPOW( strHypotPowerDataName );
		iSize = dsSSPOW.GetSize();
		vector<double> vSSPOW;
		vSSPOW.SetSize( iSize );
		vSSPOW = dsSSPOW;
		dsSSPOW.Detach();
		
		// Get hypothetical sample size dataset
		Dataset dsSS( strHypotSSDataName );
		iSize = dsSS.GetSize(); // Get number of hypothetical sample sizes
		vector<int> vSS;
		vSS.SetSize( iSize );
		vSS = dsSS;
		dsSS.Detach();	
		
		// Insert one blank line in power table for each hypothetical power
		iRow++;
		Type_Insert_Blank_Lines_in_Wks( wksPowerTable, iRow, iSize );
			
		// Loop on hypothetical sample sizes starting in row 7
		for( ii = 0; ii < iSize; iRow++, ii++ )
		{
			// Output alpha in column 2
			iCol = 1;
			wksPowerTable.SetCell( iRow, iCol, dAlpha );

			// Output hypothetical sample size in column 3
			iCol++;
			wksPowerTable.SetCell( iRow, iCol, vSS[ii] );
		
			// Output hypothetical power in column 4
			iCol++;
			wksPowerTable.SetCell( iRow, iCol, vSSPOW[ii] );
		}
	}

	// GJL 11/14/02 v7.0434 USE_OC_WKS_TYPE_NOT_LT
	// Send output Power worksheet to Results Log
	wksPowerTable.Type( TYPETARGET_OUTPUTLOG, NULL, 5 );	

	return OSTAT_NO_ERROR;	
}

/**
		Computes the power of an ANOVA experiment.  The computational engine is the
		NAG function nag_prob_non_central_f_dist.
	Example:
		See the [ComputeActualPower] section of ANOVA1Way.OGS and the osANOVA2Way_Compute_Power
		function for sample calls.
	Parameters:
		dFval=Deviate from the F-distribution for given values of alpha, df1, and df2
		ddf1=Degrees of freedom for the numerator variance
		ddf2=Degrees of freedom for the denominator variance
		dnc=Non-centality factor
	Return:
		Returns the power of an ANOVA experiment on successful exit or NANUM on error.
*/
double osANOVA_Power( double dFval, double ddf1, double ddf2, double dnc )
{
	double dtol = AN_POWER_TOLERANCE;		// Required accuracy of the solution
	int imax_iter = AN_POWER_ITERATIONS;	// Maximum number of iterations to be performed
	NagError neErr;							// NAG error structure
	NagError *pneFail = &neErr;
	
	double dPower;							// Computed power
	
	// From <NAG\OCN_g01.h>: g01gdc nag_prob_non_central_f_dist	
	// Computes probabilities for the non-central F-distribution
	dPower = 1 - nag_prob_non_central_f_dist( dFval, ddf1, ddf2, dnc, dtol, imax_iter, pneFail );
	if( pneFail->code != NE_NOERROR )
	{											// If NAG error = NE_REAL_ARG_CONS (non-centrality
		if( pneFail->code == NE_REAL_ARG_CONS ) //  factor is too large causing nag_prob_non_central_f_dist
			dPower = 1;							//  to effectively return 0) then return 1. Otherwise,
		else									//  return NANUM on NAG error.
			dPower = NANUM;
	}
	
	return dPower;
}

/**
		Computes the standard errors of the differences of the means for the osANOVA1Way_Means_Comparison
		and osANOVA2Way_Means_Comparison functions. The returned vector vC is used in place of the C
		matrix required by the NAG function nag_anova_confid_interval and consequently is indexed as
		if it were a matrix (using row major order).
	Example:
		See functions osANOVA1Way_Means_Comparison and osANOVA2Way_Means_Comparison.
	Parameters:
		inData=Number of datasets for which the standard errors of the differences of the means is
			computed
		dmse=Mean Square Error of ANOVA computation
		vNPTS=A vector containing the number of datapoints in each of the inData datasets
		vC=The returned vector vC contains standard errors of the differences of the means for an
			ANOVA computation
	Return: