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Moderated DMSA (mDMSA)

Source: R/moderate.R
dmsa_moderate.Rd

Moderated DMSA (mDMSA)

Usage

dmsa_moderate(
  data,
  probes,
  alignment,
  outcome,
  moderator,
  design,
  method = c("expected", "fixed"),
  center = TRUE,
  scale_outcome = TRUE,
  B = 999L,
  engine = c("auto", "lm", "lmer"),
  beta_input = TRUE,
  check = TRUE,
  seed = NULL
)

Arguments

data

data.frame.

probes, alignment

as in dmsa_fit().

outcome

Character. The outcome column (a psychological measure).

moderator

Character. The moderator column.

design

A dmsa_design(). Its focal is ignored and replaced by score * moderator; its fixed, random, exchangeable and forbid are used as declared.

method

"expected" or "fixed" scoring.

center

Center the moderator (default TRUE). Leaving a moderator uncentered makes the product term collinear with its parents and the simple slopes uninterpretable; the returned object records what was done.

scale_outcome

Standardise the outcome (default TRUE).

B

Permutations (Freedman-Lane: residuals of the no-interaction model are permuted within exchangeable blocks, preserving the dependence).

engine

as in dmsa_fit().

beta_input

TRUE if probes hold beta values.

check

Run the design check (default TRUE).

seed

Optional integer.

Value

An object of class dmsa_moderate.

Examples

set.seed(1)
n <- 100; probes <- paste0("cg", 1:6)
dat <- data.frame(sex_c = rep(c(-.5, .5), length.out = n),
                  chip = rep(1:10, each = 10),
                  cID = rep(seq_len(n / 2), each = 2), mod = rnorm(n))
dat[probes] <- plogis(matrix(rnorm(n * 6), n, 6))
al <- dmsa_align(data.frame(cpg = probes, d = rep(c(1, -1), 3),
                            p_plus = rep(c(.9, .1), 3)),
                 genes = rep("OXTR", 6), level = "gene")
dat$out <- 0.6 * dmsa_score(dat, probes, al) * dat$mod + rnorm(n)
des <- dmsa_design("placeholder", "sex_c", random = "chip", exchangeable = "cID")
dmsa_moderate(dat, probes, al, outcome = "out", moderator = "mod",
              design = des, B = 99, engine = "lm", seed = 3)
#> mDMSA: out ~ tone x mod
#>   n = 100   moderator centered (mean 0.109, SD 0.898)
#>   interaction b = 0.4547  se = 0.0939  t = 4.84  p = 4.955e-06
#>   Freedman-Lane permutation p = 0.01 (B = 99, block = cID)
#>   product-term VIF = 1.06
#>   engine lm
#>   simple slopes:
#>  at_moderator moderator_value  slope    se     t        p
#>         -1 SD          -0.789 -0.392 0.141 -2.79 0.006372
#>         +1 SD           1.007  0.424 0.113  3.77 0.000284
#>   Johnson-Neyman: significant outside [-0.44, 0.49]; 63% of the sample
#>     (moderator observed range -2.21 to 2.40)