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The best answers are voted up and rise to the top But tests of interactions can have low power - some people perform them by relaxing the significance level to guard against this (e.g., increase the significance level from 0.05 to 0.10). If you look at the residual sd, it's the same, which indicates (not definitively) that the underlying models are the same, just expressed (to the lm internals) differently.
In contrast, in a regression model including interaction terms centering predictors does have an influence on the main effects. It only takes a minute to sign up.I have a question about which is the best way to specify an interaction in a regression model. Consider a model with two binary regressors $x_1, x_2$ and an interaction. Predicting new data using glm and cv.glmnet in R (including interactions and categorical variables ) Hot Network Questions See the data set.I guess is related the way in the variables are relationated see The only advantage I see to define the interactions as in lm2 is that it is easy to perform multiple comparisons for the interaction term. If this is your coefficient of interest, then you should code it as such. Start here for a quick overview of the site Two equivalent ways to specify the model with interactions are: lm0 <- lm(y ~ r*s, data=d) lm1 <- lm(y ~ r + s + r:s, data=d) My question is if I could specify the interaction considering a new variable (rs) with the same levels of interaction: lm2 <- lm(y ~ r + s + rs, data=d) What advantages/disadvantages have this approach? Discuss the workings and policies of this site Detailed answers to any questions you might have We would like to see how these two variables influence the attack ratesIn this case we have to be carefull, the first coefficient as always is the intercept, the second one is the slope between the attack rates and the number of prey when the temperature is equal to 0, the third one is the slope between the attack rates and the temperature when the number of preys is equal to 0, the fourt one is the change in the slope as on of the two variables increases, for eaxmple if the number of prey items increase by one the slope between the attack rates and the temperature increase by 1.49 in this case. After getting confused by this, I read this nice paper by Afshartous & Preston (2011) on the topic and played around with the examples in R. Perhaps you could show us the code that created rsr1s2?The rs factor was defined manualy (simply paste factors r and s). If the two variables can never reach 0 (ie when measuring length) then the interpretation of the second and third coefficient is useless and the variables should be centered around 0 for them to be safely interpreted. The basic structure of a formula is the tilde symbol (~) and at least one independent (righthand) variable. In marketing, this is known as a synergy effect, and in statistics it is referred to as an interaction effect (James et al. You have four categories, but you can write the model several different ways, e.g., let 1 be a constant term, with variables $(1, x_1, x_2, x_1*x_2)$ or $(x_1, x_2, x_1*x_2, (1-x_1)*(1-x_2)$, or others. Looping thru variables in a dataframe to create interactions. Cross Validated works best with JavaScript enabled The coefficient estimates will be different, but the model is really the same.Therefore, although different, both approaches are correct, aren't it?Right. Here is an example: 2014).

By using our site, you acknowledge that you have read and understand our Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. In most (but not all) situations, a single dependent (lefthand) variable is also needed.Thus we can construct a formula quite simply by just typing:Note: Spaces in formulae are not important.And, like any other object, we can store this as an R variable and see that it is, in fact, a formula:More commonly, we want to express a formula as a relationship between an outc… 2. Otherwise, suppose the minority factor takes the value of 1 if you do not belong to a minority. I need to write a function that takes such a formula and returns a formula with all of the terms in the interaction "spelled out."