public LinearModelWithSweep(ArrayList<ModelEffect> effects, double[] data) {
int numberOfEffects = effects.size();
this.data = data;
xtxmatrices = new DoubleMatrix2D[numberOfEffects][numberOfEffects];
xtymatrices = new DoubleMatrix2D[numberOfEffects][1];
for (int i = 0; i < numberOfEffects; i++) {
ModelEffect me = effects.get(i);
xtxmatrices[i][i] = me.getXTX();
xtymatrices[i][0] =
DoubleFactory2D.dense.make(me.getXTy(data).toArray(), me.getNumberOfLevels());
for (int j = 0; j < i; j++) {
xtxmatrices[i][j] = ModelEffect.getX1TX2(me, effects.get(j));
xtxmatrices[j][i] = xtxmatrices[i][j].viewDice();
}
}
sweep = new Sweep();
initialSweep();
}
public static DoubleMatrix2D[][] modelEffectsToxtx(ModelEffect[] mes) {
int dim = mes.length;
DoubleMatrix2D[][] xtx = new DoubleMatrix2D[dim][dim];
for (int i = 0; i < dim; i++) {
xtx[i][i] = mes[i].getXTX();
for (int j = i + 1; j < dim; j++) {
xtx[i][j] = ModelEffect.getX1TX2(mes[i], mes[j]);
xtx[j][i] = xtx[i][j].viewDice();
}
}
return xtx;
}
public void test() {
ArrayList<String> A = new ArrayList<String>();
ArrayList<String> B = new ArrayList<String>();
ArrayList<String> C = new ArrayList<String>();
ArrayList<String> D = new ArrayList<String>();
ArrayList<Double> values = new ArrayList<Double>();
try {
BufferedReader br = new BufferedReader(new FileReader("c:/temp/testdata.txt"));
// br.readLine();
String line;
while ((line = br.readLine()) != null) {
String[] splitline = line.split("\t");
A.add(splitline[0]);
B.add(splitline[1]);
C.add(splitline[2]);
D.add(splitline[3]);
values.add(Double.parseDouble(splitline[4]));
}
br.close();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
}
data = new double[values.size()];
Iterator<Double> dit = values.iterator();
int count = 0;
while (dit.hasNext()) {
data[count++] = dit.next().doubleValue();
}
double[] dblC = new double[C.size()];
Iterator<String> sit = C.iterator();
count = 0;
while (sit.hasNext()) {
dblC[count++] = Double.parseDouble(sit.next());
}
double[] dblD = new double[D.size()];
sit = D.iterator();
count = 0;
while (sit.hasNext()) {
dblD[count++] = Double.parseDouble(sit.next());
}
int[] meanLevel = new int[data.length];
ModelEffect memean = new ModelEffect(meanLevel);
ModelEffect meA = new ModelEffect(ModelEffect.getIntegerLevels(A));
ModelEffect meB = new ModelEffect(ModelEffect.getIntegerLevels(B));
ModelEffect meC = new CovariateModelEffect(dblC);
ModelEffect meD = new CovariateModelEffect(dblD);
ModelEffect[] theEffects = new ModelEffect[] {memean, meA, meB, meC, meD};
xtxmatrices = modelEffectsToxtx(theEffects);
xtymatrices = modelEffectsToxty(theEffects, data);
initialSweep();
double cfm = effectSS.get(0);
for (int i = 0; i < effectSS.size(); i++) {
System.out.println(i + ", " + effectSS.get(i) + ", " + effectdf.get(i));
}
System.out.println("model ss = " + (getModelSS() - cfm) + ", model df = " + (getModeldf() - 1));
System.out.println("error ss = " + getErrorSS() + ", error df = " + getErrordf());
double[] result = marginalEffectSSdf(1);
System.out.println("marginal A ss = " + result[0] + ", df = " + result[1]);
result = marginalEffectSSdf(2);
System.out.println("marginal B ss = " + result[0] + ", df = " + result[1]);
result = marginalEffectSSdf(3);
System.out.println("marginal C ss = " + result[0] + ", df = " + result[1]);
result = marginalEffectSSdf(4);
System.out.println("marginal D ss = " + result[0] + ", df = " + result[1]);
System.out.println("finished");
}
