The original suggestion for displaying an interaction via box-plot does not quite make sense in this instance, since both of your variables that define the interaction are continuous. You could dichotomize either G or P, but you do not have much data to work with. Because of this, I would suggest coplots (a description of what they are can be found in;
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.
Although your description makes it sound like this is a fishing expedition, we may entertain the possibility that an interaction between these two variables exist. The coplot seems to show that for lower values of G the effect of P is positive, and for higher values of G the effect of P is negative. After assessing marginal histograms and bivariate scatterplots of VP, P, G and the interaction between P and G, it seemed to me that 1932 was likely a high leverage value for the interaction effect.
Below are four scatterplots, showing the marginal relationships between VP and the mean centered V, G and the interaction of V and G (what I named int_gpcent). I have highlighted 1932 as a red dot. The last plot on the lower right is the residuals of the linear model lm(VP ~ g_cent + p_cent, data = election2012) against int_gpcent.
Below I provide code that shows when removing 1932 from the linear model lm(VP ~ g_cent + p_cent + int_gpcent, data = election2012) the interaction of G and P fail to reach statistical significance. Of course this is all just exploratory (one would also want to assess if any temporal correlation occurs in the series, but hopefully this is a good start. Save ggplot for when you have a better idea of what you exactly want to plot!
#data and directory stuff
mydir <- "C:\\Documents and Settings\\andrew.wheeler\\Desktop\\R_interaction"
setwd(mydir)
election2012 <- read.table("election2012.txt", header=T,
quote="\"")
#making interaction variable
election2012<span class="math-container">$g_cent <- election2012$</span>G - mean(election2012<span class="math-container">$G)
election2012$p_cent <- election2012$P - mean(election2012$P)
election2012$int_gpcent <- election2012$g_cent *
election2012$p_cent
summary(election2012)
View(election2012)
par(mfrow= c(2, 2))
hist(election2012<span class="math-container">$VP)
hist(election2012$G)
hist(election2012$P)
hist(election2012$int_gpcent)
#scatterplot & correlation matrix
cor(election2012[c("VP", "g_cent", "p_cent", "int_gpcent")])
pairs(election2012[c("VP", "g_cent", "p_cent",
"int_gpcent")])
#lets just check out a coplot for interactions
#coplot(VP ~ G | P, data = election2012)
coplot(VP ~ P | G, data = election2012)
#example of coplot - http://stackoverflow.com/questions/5857726/how-to-delete-the-given-in-a-coplot-using-r
#onto models
model1 <- lm(VP ~ g_cent + p_cent, data = election2012)
summary(model1)
election2012$resid_m1 <- residuals(model1)
election2012<span class="math-container">$color <- "black"
election2012$color[14] <- "red"
attach(election2012)
par(mfrow = c(2,2))
plot(x = g_cent,y = VP, col = color, pch = 16)
plot(x = p_cent,y = VP, col = color, pch = 16)
plot(x = int_gpcent,y = VP, col = color, pch = 16)
plot(x = int_gpcent,y = resid_m1, col = color, pch = 16)
#what does the same model look like with 1932 removed
model1_int <- lm(VP ~ g_cent + p_cent + int_gpcent,
data = election2012)
summary(model1_int)
model2_int <- lm(VP ~ g_cent + p_cent + int_gpcent,
data = election2012[-14,])
summary(model2)
VP ~ G*P. – Andy W Nov 21 '12 at 20:33effectspackage? See http://stats.stackexchange.com/questions/18814/presenting-marginal-effects-of-logit-with-fixed-effects/137385#137385 – landroni Feb 12 '15 at 10:22