contr.deviation: Deviation Contrast Matrix

In datawizard: Easy Data Wrangling and Statistical Transformations

Deviation Contrast Matrix

Description

Build a deviation contrast matrix, a type of effects contrast matrix.

Usage

contr.deviation(n, base = 1, contrasts = TRUE, sparse = FALSE)

Arguments

n	a vector of levels for a factor, or the number of levels.
base	an integer specifying which group is considered the baseline group. Ignored if contrasts is FALSE.
contrasts	a logical indicating whether contrasts should be computed.
sparse	logical indicating if the result should be sparse (of class dgCMatrix), using package Matrix.

Details

In effects coding, unlike treatment/dummy coding (stats::contr.treatment()), each contrast sums to 0. In regressions models, this results in an intercept that represents the (unweighted) average of the group means. In ANOVA settings, this also guarantees that lower order effects represent main effects (and not simple or conditional effects, as is the case when using R's default stats::contr.treatment()).

Deviation coding (contr.deviation) is a type of effects coding. With deviation coding, the coefficients for factor variables are interpreted as the difference of each factor level from the base level (this is the same interpretation as with treatment/dummy coding). For example, for a factor group with levels "A", "B", and "C", with contr.devation, the intercept represents the overall mean (average of the group means for the 3 groups), and the coefficients groupB and groupC represent the differences between the A group mean and the B and C group means, respectively.

Sum coding (stats::contr.sum()) is another type of effects coding. With sum coding, the coefficients for factor variables are interpreted as the difference of each factor level from the grand (across-groups) mean. For example, for a factor group with levels "A", "B", and "C", with contr.sum, the intercept represents the overall mean (average of the group means for the 3 groups), and the coefficients group1 and group2 represent the differences the A and B group means from the overall mean, respectively.

See Also

stats::contr.sum()

Examples

if (FALSE) {
  data("mtcars")

  mtcars <- data_modify(mtcars, cyl = factor(cyl))

  c.treatment <- cbind(Intercept = 1, contrasts(mtcars$cyl))
  solve(c.treatment)
  #>            4 6 8
  #> Intercept  1 0 0  # mean of the 1st level
  #> 6         -1 1 0  # 2nd level - 1st level
  #> 8         -1 0 1  # 3rd level - 1st level

  contrasts(mtcars$cyl) <- contr.sum
  c.sum <- cbind(Intercept = 1, contrasts(mtcars$cyl))
  solve(c.sum)
  #>                4      6      8
  #> Intercept  0.333  0.333  0.333   # overall mean
  #>            0.667 -0.333 -0.333   # deviation of 1st from overall mean
  #>           -0.333  0.667 -0.333   # deviation of 2nd from overall mean


  contrasts(mtcars$cyl) <- contr.deviation
  c.deviation <- cbind(Intercept = 1, contrasts(mtcars$cyl))
  solve(c.deviation)
  #>                4     6     8
  #> Intercept  0.333 0.333 0.333   # overall mean
  #> 6         -1.000 1.000 0.000   # 2nd level - 1st level
  #> 8         -1.000 0.000 1.000   # 3rd level - 1st level

  ## With Interactions -----------------------------------------
  mtcars <- data_modify(mtcars, am = C(am, contr = contr.deviation))
  mtcars <- data_arrange(mtcars, select = c("cyl", "am"))

  mm <- unique(model.matrix(~ cyl * am, data = mtcars))
  rownames(mm) <- c(
    "cyl4.am0", "cyl4.am1", "cyl6.am0",
    "cyl6.am1", "cyl8.am0", "cyl8.am1"
  )

  solve(mm)
  #>             cyl4.am0 cyl4.am1 cyl6.am0 cyl6.am1 cyl8.am0 cyl8.am1
  #> (Intercept)    0.167    0.167    0.167    0.167    0.167    0.167  # overall mean
  #> cyl6          -0.500   -0.500    0.500    0.500    0.000    0.000  # cyl MAIN eff: 2nd - 1st
  #> cyl8          -0.500   -0.500    0.000    0.000    0.500    0.500  # cyl MAIN eff: 2nd - 1st
  #> am1           -0.333    0.333   -0.333    0.333   -0.333    0.333  # am MAIN eff
  #> cyl6:am1       1.000   -1.000   -1.000    1.000    0.000    0.000
  #> cyl8:am1       1.000   -1.000    0.000    0.000   -1.000    1.000
}

datawizard documentation built on Sept. 15, 2023, 9:06 a.m.