Timeline for How to interpret the intercept term in a GLM?
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| when toggle format | what | by | license | comment | |
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| Jan 21, 2013 at 15:34 | comment | added | Samuel Waldron | Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.19504 0.90446 3.533 .000412 *** Treatmentshiny_non-shiny 0.02617 1.26964 0.021 .983558 Trial -1.53880 0.36705 -4.192 2.76e-05 *** Treatment:Trial 0.16909 0.49501 0.342 .732655 --- Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) Trtm_- Trial Trtmntshn_- -0.712 Trial -0.895 0.638 Trtmnts_-:T 0.664 -0.896 -0.742 | |
| Jan 21, 2013 at 15:33 | comment | added | Samuel Waldron | Generalized linear mixed model fit by the Laplace approximation Formula: Attacked ~ Treatment + Trial + Treatment * Trial + (1 | Bird) Data: data AIC BIC logLik deviance 139.6 153.8 -64.78 129.6 Random effects: Groups Name Variance Std.Dev. Bird (Intercept) 0.87795 0.93699 Number of obs: 128, groups: Bird, 32 | |
| Jan 21, 2013 at 15:26 | comment | added | Samuel Waldron | Hi there guys, again thank you for the comments. The data points are almost identical. I am reporting it in a report and have to highlight it nonetheless. This is why the results look odd. With this data (GLM) and other data sets in my reports (GLMM) I am deffinately running (#TEAM2x2x2x2) before I can walk. I think my main problem is knowing what I need to report, do I menton the stats for the intercept or for the IV? Below is my (hopefully more standard) GLMM again with binomial link. | |
| Jan 21, 2013 at 15:06 | history | answered | Peter Flom | CC BY-SA 3.0 |