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Fit person-specific machine-learning models

Source: R/idiographic_ml.R, R/stats_compat.R
fit_ml.Rd

fit_ml() supports both historical idiographic calls using outcome/predictors and the consolidated panel-model API using y/x. Calls that name y or x use the consolidated result contract; existing positional and outcome/predictors calls retain their original behavior.

Usage

fit_idiographic_ml(
  data,
  outcome,
  predictors,
  id,
  day = NULL,
  beep = NULL,
  task = c("auto", "regression", "classification"),
  model = NULL,
  estimator = NULL,
  compare = c("both", "individual", "pooled"),
  test_prop = 0.2,
  min_train = 10L,
  min_test = 1L,
  lambda = 1,
  alpha = 0.5,
  k = 5L,
  n_components = NULL,
  max_iter = 100L,
  tol = 1e-06,
  standardize = TRUE,
  keep_fits = FALSE,
  ...
)

fit_individualized_ml(
  data,
  outcome,
  predictors,
  id,
  day = NULL,
  beep = NULL,
  task = c("auto", "regression", "classification"),
  model = NULL,
  estimator = NULL,
  compare = c("both", "individual", "pooled"),
  test_prop = 0.2,
  min_train = 10L,
  min_test = 1L,
  lambda = 1,
  alpha = 0.5,
  k = 5L,
  n_components = NULL,
  max_iter = 100L,
  tol = 1e-06,
  standardize = TRUE,
  keep_fits = FALSE,
  ...
)

fit_ml(
  data,
  outcome,
  predictors,
  id,
  day = NULL,
  beep = NULL,
  task = c("auto", "regression", "classification"),
  model = NULL,
  estimator = NULL,
  compare = c("both", "individual", "pooled"),
  test_prop = 0.2,
  min_train = 10L,
  min_test = 1L,
  lambda = 1,
  alpha = 0.5,
  k = 5L,
  n_components = NULL,
  max_iter = 100L,
  tol = 1e-06,
  standardize = TRUE,
  keep_fits = FALSE,
  ...,
  y,
  x
)

Arguments

data

A data frame or matrix.

outcome, predictors

Historical idiographic outcome and predictor arguments.

id

Person identifier column.

day, beep

Optional historical ordering columns.

task

Outcome task: "auto", "regression", or "classification".

model

One or more model names.

estimator

Optional implementation backend.

compare

Historical scope selector: "both", "individual", or "pooled".

test_prop

Proportion of each person's ordered rows used for testing.

min_train, min_test

Minimum training and test rows.

lambda, alpha

Penalized-model controls.

k

Number of neighbours for nearest-neighbour models.

n_components

Number of principal components for the historical API.

max_iter, tol

Iteration limit and convergence tolerance.

standardize

Use training-only predictor standardization?

keep_fits

Retain fitted backend objects?

...

Arguments passed to the selected implementation.

y, x

Consolidated outcome and predictor selectors. Supply both.

Value

An idioml_result for historical calls or an idiographic_fit for consolidated y/x calls.