Unexpected behaviour when testing for independence with multiple factors

Hi, I'm a new user of R. My background is Electrical Engineering, so
please bear with me if this is a silly question.

I'm trying to assess whether the results of an experiment satisfy the
hypothesis of homoscedasticity (my ultimate goal is to use ANOVA).

The result of the experiment is mean delay (dT), which depends on
three factors, topology, drift, and lambda. The first two factors are
categorical (with 4 levels each) and the last one is numerical, with
two levels.

A sample of my data is as follows:

dT      Topology        Drift   lambda
258.789 Tree    b1      .43
244.195 Tree    b1      .43
115.961 Tree    b2      .3
115.183 Tree    b2      .3

I would like to separate dT in the 32 samples (4x4x2), and test if the
variance of each sample is equal to the other 31 samples.
I tried using fligner.test and bartlett.test, but either test seems to
only work for one factor:

fligner.test( dT ~ Topology + Drift + lambda)

        Fligner-Killeen test of homogeneity of variances

data:  dT by Topology by Drift by lambda
Fligner-Killeen:med chi-squared = 15.4343, df = 2, p-value = 0.0004451

fligner.test( dT ~ Topology )

        Fligner-Killeen test of homogeneity of variances

data:  dT by Topology
Fligner-Killeen:med chi-squared = 15.4343, df = 2, p-value = 0.0004451

As I see from the previous two outputs, fligner.test only takes into
account the first factor. Similar results are obtained for
bartlett.test.

At this point I don't know if I'm using the test incorrectly or
something else. I would really appreciate any help. I'm using R
version 2.7.2 (2008-08-25) in Windows XP.

Many thanks in advance
Javier

----------------------------------------------------
Javier Acuna
Electrical Engineering Grad Student
Universidad de Chile
javier.acuna.o at gmail.com