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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