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Below is a coplot of the election2012 data generated by the code coplot(VP ~ P | G, data = election2012). So this is assessing the effect of P on VP conditional on varying values of G. … for interactions #coplot(VP ~ G | P, data = election2012) coplot(VP ~ P | G, data = election2012) #example of coplot - http://stackoverflow.com/questions/5857726/how-to-delete-the-given-in-a-coplot-using-r …

Make use of the coplot to avoid this problem in the future coplot(y ~ x1 | x2, data=l, panel=panel.smooth) The same recommendations apply that have been described elsewhere for handling such an issue …

The first coplot suggests that it is reasonable to assume that x1 has a linear effect on y when controlling for x2 and that this effect does not depend on x2. … The second coplot suggests that it is reasonable to assume that x2 has a quadratic effect on y when controlling for x1 and that this effect does not depend on x1. …

You could investigate your data visually with a conditioning plot, in R this is named coplot. … There is an example in Best method to visualize large interaction between two factors or run example(coplot) within R. …

A coplot is conditional (the top part of my answer here contrasts conditional vs. marginal). Literally, 'coplot' is a blended word from 'conditional plot'. … In a coplot, you are taking slices (or subsets) of the data on the other dimensions and plotting the data in those subsets in a series of scatterplots. …

A more powerful tool to inspect these relations is a coplot. w <- abs(seq(-3, 3, by=0.1)) y2 <- rbinom(length(x), 1, plogis(-0.3 + 1 * x - 0.2*w)) mypanel <- function(x, y, ...) panel.smooth(x=x, y=y, … span=1/2, ...) coplot(y ~ x | w, panel=mypanel) Notice what a terrible smoother LOESS is compared to a smoothing spline for binary response - this is because LOESS excludes outliers. …

R has a function coplot which is convenient for this, and can be used to condition on one or two variables. …

In R you could play with the function coplot and you could make scatterplot matrices, replacing what would be one number in each of the two functions above (autocorrelation, crosscorrelation) with a scatterplot …

Another more visual way to explore interactions is to use the coplot function in R, which makes a series of plots for the response versus one x-variable while varying the level of another x-variable. …

In R, the coplot function is very useful for visualizing such relations. …

I would start with some visualizations, in R there is a useful function coplot for conditioning plots, some examples in following posts: Investigate correlation conditional on a threshold, Can I analyze …

In this case a conditioning plot may be informative: (made in R with function coplot) The order of the panels are left to right, bottom up. …

The R command coplot can do this well. If the shape of the sigmoid curve changes direction or shifts drastically, we can conclude that the interaction has a strong magnitude of effect. …

One useful way of plotting the data is an conditioning plot (conditioning on the ind(ex) number), using coplot function in R: If you remove some few high y values, there is no correlation to be seen. …

To play with that example, you can access it from R by data(stork, package="TeachingDemos") I will leave the fun for the readers, but one interesting plot is this coplot: …