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~+'''Two Groups (One Factor/Two Levels), No Covariates'''+~

This models the input as two separate offsets/intercepts/means, one
for each group. The two groups can be thought of as two levels of a
single discrete factor. There is not continuous covariate. These
results hold for DODS or DOSS.

<<TableOfContents>>

= FSGD File (g2v0.fsgd) =
{{{
GroupDescriptorFile 1
Title OSGM
Class Group1
Class Group2
Input subject1 Group1
Input subject2 Group2
}}}

Nclasses = 2 <<BR>>
Nvariables = 0 <<BR>>

= Regressors (DODS) =

Nregressors = Nclasses*(Nvariables+1) = 2*(0+1) = 2 <<BR>>
Regressor1: ones for subjects in Group 1, 0 otherwise. Codes intercept/mean for Group 1 <<BR>>
Regressor2: ones for subjects in Group 2, 0 otherwise. Codes intercept/mean for Group 2 <<BR>>

= Contrasts =

The number of columns in each contrast matrix must be the same as the
number of regressors (Nregressors). If there is only one row in the
contrast matrix, then the result will be a t-test and will have a
sign. Reversing the signs in the contrast matrix will only change the
sign of the output, not its magnitude.  If there is more than one row,
the result will be an F-test and will be unsigned.

== Contrast 1 group.diff.mtx ==
Null Hypothesis: is there a difference between the group intercepts?
Is there a difference between groups?

{{{
1 -1
}}}

This is a t-test with Group1>Group2 being positive (red/yellow).

== Contrast 2 group1.mtx ==

Null Hypothesis: is there a main effect of Group1? Does the mean of
Group1 equal 0?

{{{
1 0
}}}

This is a t-test with Group1>0 being positive (red/yellow).

== Contrast 3 group2.mtx ==

Null Hypothesis: is there a main effect of Group2? Does the mean of
Group2 equal 0?

{{{
0 1
}}}

This is a t-test with Group2>0 being positive (red/yellow).

== Contrast 4 g1g2.intercept.mtx ==
Null Hypothesis: does mean of the group means differ from 0?

{{{
0.5 0.5
}}}

This is a t-test with (Group1+Group2)/2 > 0 being positive
(red/yellow). If the mean is < 0, then it will be displayed in
blue/cyan. 

== mri_glmfit command ==

This is an example invocation of mri_glmfit. Depending upon your
application, you may have other options as well.

{{{
mri_glmfit \
  --glmdir g2v0 \
  --y y.mgh \
  --fsgd g2v0.fsgd \
  --C group.diff.mtx \
  --C group1.mtx \
  --C g1g2.intercept.mtx \
}}}