public CacheEntry(Key key, QrFactorization qrFactorization)
{
Key = key;
QrFactorization = qrFactorization;
MatrixCrossproduct = ImmutableMatrix.OfMatrix(ComputeMatrixCrossproduct(key.Matrix, qrFactorization.IndependentColumnIndexes));
MatrixCrossproductInverse = ImmutableMatrix.OfMatrix(MatrixCrossproduct.Inverse());
}
public IList <LinearFitResult> Fit(double[] observations)
{
if (observations.Length != DesignMatrix.RowCount)
{
throw new ArgumentException("Wrong number of rows"); // Not L10N
}
var coefficients = QrFactorization.Solve(observations);
var fittedValues = new double[observations.Length];
var residuals = new double[observations.Length];
for (int iRow = 0; iRow < observations.Length; iRow++)
{
var designRow = Enumerable.Range(0, DesignMatrix.ColumnCount).Select(index => DesignMatrix[iRow, index]).ToArray();
fittedValues[iRow] = DotProduct(designRow, coefficients);
residuals[iRow] = observations[iRow] - fittedValues[iRow];
}
double rss = DotProduct(residuals, residuals);
int degreesOfFreedom = observations.Length - QrFactorization.NumberIndependentColumns;
double resVar = rss / degreesOfFreedom;
double sigma = Math.Sqrt(resVar);
var covarianceUnscaled = MatrixCrossproductInverse;
var scaledCovariance = covarianceUnscaled.Multiply(sigma * sigma);
var indepColIndexes = QrFactorization.IndependentColumnIndexes.ToArray();
var result = new List <LinearFitResult>();
foreach (var contrastRow in ContrastValues.EnumerateRows())
{
double standardError = 0;
for (int iRow = 0; iRow < indepColIndexes.Length; iRow++)
{
for (int iCol = 0; iCol < indepColIndexes.Length; iCol++)
{
standardError += contrastRow[indepColIndexes[iRow]] * scaledCovariance[iRow, iCol] * contrastRow[indepColIndexes[iCol]];
}
}
standardError = Math.Sqrt(standardError);
double foldChange = DotProduct(coefficients, contrastRow);
double tValue = foldChange / standardError;
double pValue;
if (0 != degreesOfFreedom)
{
var studentT = new StudentT(0, 1.0, degreesOfFreedom);
pValue = (1 - studentT.CumulativeDistribution(Math.Abs(tValue))) * 2;
}
else
{
pValue = 1;
}
result.Add(new LinearFitResult(foldChange)
.SetDegreesOfFreedom(degreesOfFreedom)
.SetTValue(tValue)
.SetStandardError(standardError)
.SetPValue(pValue));
}
return(result);
}
