Timeline for interpretation and inclusion of interaction terms in regression model
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 15, 2017 at 11:42 | comment | added | Frank Harrell | It i not good statistical practice to select terms based on significance testing. Pre-specify your model and compute contrasts of interest within that one model. | |
| Oct 15, 2017 at 11:27 | comment | added | alexeymosco | I will put it in different words: your interpretation of B * C have nothing to do with other terms of your formula because you work with a linear combination (sum of weighted terms). Whatever other terms' coefficients are, you interpret B * C as intreacting term where B coefficient depends on C. However when you interprete the model as a whole, you really interprete how output was affected by all the terms in the formula. Here you are dependent on all the terms and their interactions specified. | |
| Oct 15, 2017 at 11:17 | vote | accept | cs0815 | ||
| Oct 15, 2017 at 11:16 | comment | added | alexeymosco | Yes you can (have to). Estimates of all formula's parameters influence the OUTPUT whether they are attributed with low or high p-values. If otherwise is not coded in your function of linear model, which may be language-related question. | |
| Oct 15, 2017 at 11:13 | comment | added | cs0815 | Thanks. The thing is I would like to keep them in as I am after the parameters for BC and B. The question is, if for example the p-value for AB*C is above 0.05, can I still see this parameter as included and thus interpret the value for B (provided B's p value is below 0.05)? | |
| Oct 15, 2017 at 11:02 | history | answered | alexeymosco | CC BY-SA 3.0 |