Compare the within-person and between-person effect of each predictor

The point of a within-between model. within is the effect of a person moving away from their own average; between is the effect of one person averaging higher than another. contextual is between - within, with a person-clustered test: if its interval excludes zero, the two processes are genuinely different and pooling them into one coefficient is a modelling error.

Usage

contextual(
  x,
  variable = NULL,
  scope = NULL,
  subject = NULL,
  subgroup = NULL,
  sort_by = NULL,
  decreasing = FALSE,
  n = NULL,
  ...
)

Arguments

x: A fit_within_between() result.
variable, scope, subject, subgroup: Optional filters.
sort_by: Optional column to sort by.
decreasing: Sort order when sort_by is supplied.
n: Optional number of rows.
...: Ignored.

Value

A data frame with one row per predictor per unit.

Details

model = "within" and model = "between" fit only one component, so their contextual effect is NA – there is nothing to compare it against.

Examples

fit <- fit_within_between(srl, y = "effort", x = c("efficacy", "planning"),
                          id = "name", time = "day")
contextual(fit)
#>   variable   within   between   contextual     S.E.            95% CI       p
#> -----------------------------------------------------------------------------
#>   efficacy   0.2681    0.4602       0.1921   0.1361   [-0.084, 0.468]   0.167
#>   planning   0.3319    0.2257      -0.1062   0.1496   [-0.410, 0.197]   0.483
#> 
#> scope = pooled,  model = within_between,  estimator = ols,  subject = .all,  subgroup = .all