And what's the difference between these two models?


M0 <- lmer (y ~ 1 + (1 | county))

M1 <- lmer (y ~ -1 + (1 | county))


On Thu, May 31, 2012 at 9:52 AM, Michael <comtech.usa@gmail.com> wrote:

> What are the key differences between the following two models?
>
>
>
> lmefit = lmer(MathAch ~ SES + (1 |School) , MathScores)
>
> lmfit = lm(MathAch ~ SES + School -1 , MathScores)
>
> To me, they seem to be the same, except that lmefit takes less parameters
> (because it used Normal distribution to model the levels at the group
> level...)
>
> Am I right?
>  On Wed, May 30, 2012 at 7:52 PM, Michael <comtech.usa@gmail.com> wrote:
>
>> Hi all,
>>
>> The regular linear regression model is easy to present:
>>
>> We are minimize the sum of the least square errors:  sum{( y_i -
>> yhat_i)^2, i from 1 to n}
>>
>> How do we present the mixed model?
>>
>> More specifically, we are wondering about how to present the following
>> simple model:
>>
>>
>> lmefit = lmer(MathAch
>> ~ SES + (1 |School) , MathScores)
>>
>> We need to explain/present the model to some one who are not of
>> statistical background.
>>
>> Our questions are:
>>
>> 1. What objective function are we minimizing here?
>>
>> We don't want to show that
>>
>>   y = X * beta + Z * b + e,
>>
>> Because this is not very intuitive, especially the Z*b part...
>>
>> Is there a way that we could intuitively see what we are doing via the
>> mixed-model approach?
>>
>> 2. Is there a presentation-friendly way of comparing the regular linear
>> regression vs. the mixed model above?
>>
>> Hopefully we can visualize them intuitively.
>>
>> 3. What sorts of model-checking and post-fit-diagnosis could we show for
>> mixed-model?
>>
>> What are the typical goodness-of-fit checking procedures?
>>
>> Could anybody please shed some lights to us?
>>
>> Thanks a lot!
>>
>> [c.p. on Cross-Validated in case no one sees our questions... Thank you!]
>>
>>
>
>

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