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rstatisticsoutputlme4mixed-models

Interpretation of an lmer output

I'm new here, I've tried to run a lmer model:

lmer = lmer(RI ~ SET + LOG_VP + (1|API) + (1|ODOUR), data = a)

Could someone help me interpret the output?

  Linear mixed model fit by REML ['lmerMod'] 
Formula: RI ~ SET + LOG_VP + (1 | API) + (1 | ODOUR)
Data: a
REML criterion at convergence: -349.9
Scaled residuals: 
Min      1Q  Median      3Q     Max 
-2.6167 -0.4719 -0.0357  0.5053  8.4850 
Random effects:
Groups   Name        Variance Std.Dev.
API      (Intercept) 0.01431  0.11964 
ODOUR    (Intercept) 0.00415  0.06442 
Residual             0.00778  0.08820 
Number of obs: 238, groups:  API, 34; ODOUR, 14
Fixed effects:
             Estimate Std. Error t value
(Intercept)  0.15716    0.08792   1.787
SET          0.08180    0.05490   1.490
LOG_VP       0.03527    0.01968   1.792
Correlation of Fixed Effects:
        (Intr) SET   
SET    -0.950       
LOG_VP  0.083 -0.049

Thank you!

Solution

  • It depends on what your research question is, but

    • the response when both fixed effects are zero is is 0.15716

    • a 1 unit change in SET is associated with a 0.08180 change in RI

    • a 1 unit change in LOG_VP is associated with a 0.03527 change in RI

    • Variance at the API level is 0.01431

    • Variance at the ODOUR level is 0.00415

    • Residual (unit level) variance is 0.00778