[R-sig-ME] Comparing Model Performance Across Data Sets: report p values?

[Thierry Onkelinx]

Dear Karista,

Much depends on what you want to compare between the models. The parameter
estimates? The predicted values? The goodness of fit? You 'll need to make
that clear.

Best regards,


ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2017-08-02 19:54 GMT+02:00 Karista Hudelson <karistaeh op gmail.com>:

> Hello All,
>
> I am comparing the fit of a mixed model on different time periods of a data
> set.  For the first time period I have 113 observations and only one of the
> fixed effects is significant.  For the second time period I have 322
> observations and all of the fixed effects are significant.  Because n is
> important in the calculation of p, I'm not sure how or even if to interpret
> the differences in p values for the model terms in the two time periods.
> Does anyone have advice on how to compare the fit of the variables in the
> mixed model for the two data sets in a way that is less impacted by the
> difference in the number of observations?  Or is a difference of 209
> observations enough to drive these differences in p values?
>
> Time period 1 output:
> Fixed effects:
>                   Estimate Std. Error         df t value Pr(>|t|)
> (Intercept)      -0.354795   0.811871  82.140000  -0.437    0.663
> Length            0.024371   0.003536 106.650000   6.892 4.01e-10 ***
> Res_Sea_Ice_Dur  -0.002408   0.002623 107.970000  -0.918    0.361
> Sp_MST        0.014259   0.024197 106.310000   0.589    0.557
> Summer_Rain      -0.005015   0.003536 107.970000  -1.418    0.159
>
>
> Time period 2 output:
> Fixed effects:
>                   Estimate Std. Error         df t value Pr(>|t|)
> (Intercept)     -1.183e+00  3.103e-01  6.650e+00  -3.812 0.007281 **
> Length           1.804e-02  1.623e-03  3.151e+02  11.120  < 2e-16 ***
> Res_Sea_Ice_Dur  2.206e-03  5.929e-04  3.153e+02   3.721 0.000235 ***
> Spring_MST       1.022e-02  7.277e-03  3.150e+02   1.404 0.161319
> Summer_Rain     -1.853e-03  5.544e-04  3.150e+02  -3.343 0.000929 ***
>
>
>
>
> Thanks in advance for your time and consideration of this question.
> Karista
>
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