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Multilateral interaction: an easy way to get estimates of numerical coefficients?

Let's say there is a 4-way interaction with a 2x2x2 factorial design plus a continuous variable. Factors have standard contrast coding ( contr.treatment). Here is an example:

set.seed(1)

cat1 <- as.factor(sample(letters[1:2], 1000, replace = TRUE))
cat2 <- as.factor(sample(letters[3:4], 1000, replace = TRUE))
cat3 <- as.factor(sample(letters[5:6], 1000, replace = TRUE))
cont1 <- rnorm(1000)
resp <- rnorm(1000)
df <- data.frame(cat1, cat2, cat3, cont1, resp)

mod <- lm(resp ~ cont1 * cat1 * cat2 * cat3, data = df)

Looking at the output coef(mod), we get something like:

        (Intercept)                   cont1                   cat1b 
        0.019822407             0.011990238             0.207604677 
              cat2d                   cat3f             cont1:cat1b 
       -0.010132897             0.105397591            -0.001153867 
        cont1:cat2d             cat1b:cat2d             cont1:cat3f 
        0.023358901            -0.194991402             0.060960695 
        cat1b:cat3f             cat2d:cat3f       cont1:cat1b:cat2d 
       -0.240624582            -0.117278931            -0.069880751 
  cont1:cat1b:cat3f       cont1:cat2d:cat3f       cat1b:cat2d:cat3f 
       -0.120446848            -0.141688864             0.136945262 
cont1:cat1b:cat2d:cat3f 
        0.201792298

And in order to get an estimated intercept for cat1b(for example), we add our implicit member (Intercept)and cat1b, i.e. coef(mod)[1] + coef(mod)[3]. To get the slope change for the same category, we will use coef(mod)[2] + coef(mod)[6], a la this post is r-bloggers . It’s tedious enough to write everything, and it methods(class="lm")doesn’t look like it has any functions that make it right out of the gate.

- ?

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1

lsmeans. :

lstrends(mod, specs = c('cat1', 'cat2', 'cat3'), var = 'cont1')

cat1 cat2 cat3 cont1.trend         SE  df    lower.CL  upper.CL
 a    c    e     0.01199024 0.08441129 984 -0.15365660 0.1776371
 b    c    e     0.01083637 0.08374605 984 -0.15350502 0.1751778
 a    d    e     0.03534914 0.09077290 984 -0.14278157 0.2134799
 b    d    e    -0.03568548 0.09644117 984 -0.22493948 0.1535685
 a    c    f     0.07295093 0.08405090 984 -0.09198868 0.2378905
 b    c    f    -0.04864978 0.09458902 984 -0.23426916 0.1369696
 a    d    f    -0.04537903 0.09363128 984 -0.22911897 0.1383609
 b    d    f    -0.03506820 0.08905581 984 -0.20982934 0.1396929
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