Fit pooled, subgroup and person-specific generalized linear models

Usage

fit_glm(
  data,
  y,
  x,
  id,
  family = "binomial",
  time = NULL,
  scope = "both",
  subgroup = NULL,
  weights = NULL,
  test_prop = 0.2,
  min_train = 10L,
  min_test = 1L,
  ...
)

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.
family: GLM family name or family object. Native support covers "gaussian", "binomial", and "poisson". "negbin" fits a negative binomial via MASS::glm.nb(), which is what overdispersed counts need – Poisson would report standard errors that are too small.
time: Optional ordering column.
scope: "both" (pooled + individual), "pooled", "individual", "subgroup", or "all" (pooled + subgroup + individual).
subgroup: Optional subgroup mapping: an find_subgroups() result, a grouping column in data, or a named vector of labels per person.
weights: Optional column name in data holding case weights.
test_prop: Proportion of each person's ordered rows held out.
min_train: Minimum complete training rows per person.
min_test: Minimum complete held-out rows per person.
...: Passed to the underlying fitter.

Value

An idiographic_fit.

Examples

# `srl` carries some missing values, so na.rm is needed to derive an outcome.
srl$high <- ifelse(srl$effort > median(srl$effort, na.rm = TRUE),
                   "yes", "no")
fit <- fit_glm(srl, y = "high", x = c("efficacy", "planning"), id = "name",
               family = "binomial", time = "day")
metrics(fit, overall = TRUE)
#>        scope    model estimator  subject subgroup    n  accuracy     brier
#> 1     pooled binomial    native .overall     .all 1150 0.7165217 0.1869180
#> 2 individual binomial    native .overall     .all 1150 0.7765217 0.1485573
#>    log_loss
#> 1 0.5541527
#> 2 0.4468249