Fit subgroup-specific models — fit_subgroups • idiographic

Fits pooled, subgroup and person-specific models together so all three levels can be compared in one table.

Usage

fit_subgroups(
  data,
  y,
  x,
  id,
  subgroup,
  method = c("lm", "glm", "ml"),
  scope = "all",
  ...
)

Arguments

data: Data frame.
y: Outcome column name.
x: Predictors: names, numeric positions, a:b range, formula, or data frame.
id: Person/unit ID column.
subgroup: Subgroup mapping: an find_subgroups() result, a grouping column in data, or a named vector of labels per person.
method: Which family to fit: "lm", "glm", or "ml".
scope: Defaults to "all" (pooled + subgroup + individual).
...: Passed to the underlying fitter, e.g. model or family.

Value

An idiographic_fit.

Examples

g <- find_subgroups(srl, y = "effort", x = "efficacy:monitoring",
                    id = "name", k = 2, reps = 10)
fit <- fit_subgroups(srl, y = "effort", x = "efficacy:monitoring",
                     id = "name", subgroup = g, time = "day")
metrics(fit, overall = TRUE)
#>        scope model estimator  subject subgroup    n     rmse      mae      bias
#> 1     pooled    lm    native .overall     .all 1150 20.18702 16.28579 0.7323279
#> 2   subgroup    lm    native .overall     .all 1150 19.61460 15.79144 0.8497595
#> 3 individual    lm    native .overall     .all 1150 17.23228 12.86623 0.7977109
#>   r_squared
#> 1 0.4353275
#> 2 0.4668970
#> 3 0.5885302