On Thu, Jun 10, 2010 at 2:04 PM, Gabor Grothendieck
wrote:
> On Thu, Jun 10, 2010 at 5:33 AM, Joris Meys wrote:
>> This is only valid in case your X matrix is exactly the same, thus
>
> The poster did not give a full explanation so the best that can be
> done without getting into an extended ques
On Thu, Jun 10, 2010 at 5:33 AM, Joris Meys wrote:
> This is only valid in case your X matrix is exactly the same, thus
The poster did not give a full explanation so the best that can be
done without getting into an extended question and answer is to make
some assumptions, show the result and hop
Thank you very much for your answers.
The setup is not as you described - all participants have 4 stimuli - 2 for
punishment and 2 for reward. all participants see them in mixed blocks.
I try to measure the correlation between some personality traits (measured
with personality questionnaires) and t
As I explained, you cannot just test two models with different
dependent variables. You can model the difference you calculated, if
you think you can give a sensible interpretation to it. But be aware
of the fact that your model will tell you something about the relation
between your predictors and
I'll try to add some more information regarding my experiment - maybe that
would help clear things out.
Instead of actually measuring the learning curve (i.e. number of correct
responses per block) I created a variable that substract the number of
correct answers from the last block with that of th
This is only valid in case your X matrix is exactly the same, thus
when you have an experiment with multiple response variables (i.e.
paired response data). When the data for both models come from a
different experiment, it ends here.
You also assume that y1 and y2 are measured in the same scale,
We need to define what it means for these models to be the same or
different. With the usual lm assumptions suppose for i=1, 2 (the two
models) that:
y1 = a1 + X b1 + error1
y2 = a2 + X b2 + error2
which implies the following which also satisfies the usual lm assumptions:
y1-y2 = (a1-a2) + X(b1
Ok.
Thank you very much for setting me straight :)
On Wed, Jun 9, 2010 at 7:22 PM, Joris Meys wrote:
> On Wed, Jun 9, 2010 at 5:19 PM, Or Duek wrote:
> > Hi,
> > I would like to compare to regression models - each model has a different
> > dependent variable.
> > The first model uses a number
On Wed, Jun 9, 2010 at 5:19 PM, Or Duek wrote:
> Hi,
> I would like to compare to regression models - each model has a different
> dependent variable.
> The first model uses a number that represents the learning curve for reward.
> The second model uses a number that represents the learning curve
Hi,
I would like to compare to regression models - each model has a different
dependent variable.
The first model uses a number that represents the learning curve for reward.
The second model uses a number that represents the learning curve from
punishment stimuli.
The first model is significant an
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