Package {idiographic}

idiographic: Idiographic Network Estimation from Intensive Longitudinal Data

Description

Person-specific and within-person network estimation from intensive longitudinal / ESM panel data: preprocessing audits (preprocess()), edge-stability diagnostics (estimate_stability()), model-comparison reports (compare_idiographic()), rolling forecast validation (validate_forecast()), rolling ordinary vector autoregression (fit_rolling_var()), rolling graphical vector autoregression (fit_rolling_graphical_var()), ordinary vector autoregression (fit_var()), graphical vector autoregression (fit_graphical_var()), multilevel vector autoregression (fit_mlvar()), unified SEM (fit_usem()), Group Iterative Multiple Model Estimation (fit_gimme()), and idiographic supervised machine-learning models (fit_ml()) for individualized prediction. Use fit_idiographic() for registry-driven dispatch or the direct ⁠fit_*()⁠ functions. Every result has tidy as.data.frame(), summary(), and print methods; network estimators also share edges(), coefs(), nodes(), matrices(), and plotting methods. equivalence() reports the exact validation scope attached to each method.

Author(s)

Maintainer: Mohammed Saqr saqr@saqr.me [copyright holder]

Authors:

• Sonsoles López-Pernas sonsoles.lopez@uef.fi

See Also

Useful links:

• https://github.com/mohsaqr/idiographic

• Report bugs at https://github.com/mohsaqr/idiographic/issues

Argument-by-argument validation coverage

Description

Builds a tidy, executable ledger from the actual formals of every registered entry point. Each argument is classified according to the strongest honest evidence contract available for that method: direct oracle/engine equality, frozen statistical fixtures, recovery/internal validation, delegated forwarding, a supported extension, or an explicit rejection boundary. Consequently a newly added formal cannot silently disappear from the audit: package tests require every current formal to occur exactly once here.

Usage

argument_coverage(method = NULL)

Arguments

Value

A data frame with one row per public method argument.

Examples

argument_coverage()
argument_coverage("mlvar")

Coerce to a netobject

Description

Returns netobjects unchanged; promotes a bare cograph_network.

Usage

as_netobject(x, ...)

Arguments

Value

A c("netobject", "cograph_network") object.

Examples

W <- matrix(c(0, 0.3, -0.2, 0), 2, 2,
            dimnames = list(c("A", "B"), c("A", "B")))
x <- structure(list(weights = W, method = "relative", directed = TRUE),
               class = "cograph_network")
as_netobject(x)

Coerce a gvar_result to plottable netobjects

Description

Returns the temporal lag layer(s) and contemporaneous network as netobjects, so each renders directly with cograph::splot() (or any netobject verb) without the caller transposing matrices or dropping intercept columns. The temporal network is oriented [from = predictor(t-1), to = outcome(t)].

Usage

## S3 method for class 'gvar_result'
as_netobject(x, ...)

Arguments

Value

A netobject_group: a named list with $temporal for a lag-1 model, or one temporal_lagN element per multi-lag model, plus $contemporaneous.

Plottable netobject(s) from a GIMME fit

Description

Returns the GIMME result as matrix-backed netobjects. By default these encode the same quantity the gimme package plots — the proportion of subjects that have each path (path_counts / n_subjects) — not the group-average coefficient (which dilutes toward zero and is not what GIMME displays). For the faithful single mixed network (dashed lag / solid contemporaneous, group/individual colouring, autoregressive self-loops) use plot_gimme().

Usage

## S3 method for class 'net_gimme'
as_netobject(x, style = c("pnode", "unified"), weight = c("prop", "coef"), ...)

Arguments

Value

For style = "pnode", a netobject_group with ⁠$temporal⁠ and ⁠$contemporaneous⁠. For style = "unified", one c("netobject", "cograph_network") object with ⁠2p⁠ nodes.

See Also

plot_gimme() for the faithful gimme-style mixed plot.

Plottable netobjects from an mlVAR fit

Description

Returns the three networks as netobjects oriented for plotting (temporal edges run predictor -> outcome, matching fit_graphical_var), so cograph::splot() renders them consistently. The raw fit$temporal$weights keep mlVAR's ⁠[outcome, predictor]⁠ layout for equivalence.

Usage

## S3 method for class 'net_mlvar'
as_netobject(x, ...)

Arguments

Value

A netobject_group with ⁠$temporal⁠, ⁠$contemporaneous⁠, ⁠$between⁠.

Coerce a var_bayes_result to plottable netobjects

Description

Coerce a var_bayes_result to plottable netobjects

Usage

## S3 method for class 'var_bayes_result'
as_netobject(x, ...)

Arguments

Value

A netobject_group with temporal (directed) and contemporaneous (undirected) netobjects.

Tidy coefficients from a fitted mlvar model

Description

Generic accessor for the tidy coefficient table stored on a fit_mlvar() result. Returns a data.frame with one row per ⁠(outcome, predictor)⁠ pair and columns outcome, predictor, beta, se, t, p, ci_lower, ci_upper, significant.

Usage

## S3 method for class 'idioml_result'
coefs(x, ...)

coefs(x, ...)

## S3 method for class 'net_mlvar'
coefs(x, ...)

## Default S3 method:
coefs(x, ...)

## S3 method for class 'net_mlvar_bayes'
coefs(x, ...)

## S3 method for class 'net_usem'
coefs(x, ...)

## S3 method for class 'var_result'
coefs(x, ...)

## S3 method for class 'var_bayes_result'
coefs(x, ...)

## S3 method for class 'gvar_result'
coefs(x, ...)

## S3 method for class 'net_gimme'
coefs(x, ...)

## S3 method for class 'var_list'
coefs(x, ...)

## S3 method for class 'gvar_list'
coefs(x, ...)

Arguments

Details

Only the within-person (temporal) coefficients are tabulated — these are the lagged fixed effects that populate fit$temporal. The between-subjects effects that go into fit$between are handled via the D (I - Gamma) transformation and are not exposed as a separate tidy table.

Value

A tidy data.frame of coefficient estimates.

Examples

set.seed(1)
n_id <- 8; n_t <- 30; vars <- c("A", "B", "C")
rows <- lapply(seq_len(n_id), function(i) {
  m <- as.data.frame(matrix(rnorm(n_t * 3), ncol = 3))
  names(m) <- vars
  m$id <- i; m$day <- 1L; m$beep <- seq_len(n_t)
  m
})
d <- do.call(rbind, rows)
fit <- fit_mlvar(d, vars = vars, id = "id", day = "day", beep = "beep")
print(fit)
summary(fit)

Compare idiographic estimators on one dataset

Description

Fits one or more idiographic estimators to the same data and returns a tidy per-method/per-network comparison table. This is a reporting layer: it does not define a new model, and each row is computed from the estimator's own summary() method plus common edge-table accessors.

Usage

compare_idiographic(
  data,
  vars,
  estimators = c("var", "graphical_var"),
  id = NULL,
  day = NULL,
  beep = NULL,
  estimator_args = list(),
  keep_fits = FALSE
)

Arguments

Value

A model_comparison object with ⁠$comparison⁠, ⁠$failures⁠, and optionally ⁠$fits⁠. ⁠$comparison⁠ is a tidy data.frame with one row per method/network.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:4, each = 15),
                beep = rep(1:15, 4),
                A = rnorm(60), B = rnorm(60), C = rnorm(60))
cmp <- compare_idiographic(
  d, vars = c("A", "B", "C"), id = "id", day = "day", beep = "beep",
  estimators = c("var", "graphical_var"),
  estimator_args = list(graphical_var = list(n_lambda = 5))
)
cmp$comparison

Tidy edge table for any idiographic result

Description

A single tidy verb for every network idiographic produces. Returns one row per edge with columns network (e.g. "temporal", "contemporaneous", "between"), from, to, weight – and, for GIMME, level ("group"/"individual"). Directed networks (temporal) keep every edge; undirected networks (contemporaneous, between) report each pair once.

Usage

## S3 method for class 'net_usem'
edges(
  x,
  sort_by = "weight",
  include_self = FALSE,
  network = NULL,
  n = NULL,
  ...
)

## S3 method for class 'var_result'
edges(
  x,
  sort_by = "weight",
  include_self = FALSE,
  network = NULL,
  n = NULL,
  ...
)

edges(x, ...)

## S3 method for class 'netobject'
edges(
  x,
  sort_by = "weight",
  include_self = FALSE,
  network = NULL,
  n = NULL,
  ...
)

## S3 method for class 'netobject_group'
edges(
  x,
  sort_by = "weight",
  include_self = FALSE,
  network = NULL,
  n = NULL,
  ...
)

## S3 method for class 'gvar_result'
edges(
  x,
  sort_by = "weight",
  include_self = FALSE,
  network = NULL,
  n = NULL,
  ...
)

## S3 method for class 'net_mlvar'
edges(
  x,
  sort_by = "weight",
  include_self = FALSE,
  network = NULL,
  n = NULL,
  ...
)

## S3 method for class 'net_gimme'
edges(
  x,
  sort_by = "weight",
  include_self = TRUE,
  weight = c("prop", "coef"),
  network = NULL,
  n = NULL,
  ...
)

## S3 method for class 'var_list'
edges(x, ...)

## S3 method for class 'gvar_list'
edges(x, ...)

Arguments

Value

A tidy data.frame, one row per edge.

Examples

set.seed(1)
d <- data.frame(id = 1, A = rnorm(80), B = rnorm(80), C = rnorm(80))
fit <- fit_graphical_var(d, vars = c("A", "B", "C"), id = "id", n_lambda = 8)
edges(fit)            # tidy: network / from / to / weight

Report method-equivalence evidence

Description

Returns the equivalence declaration attached by fit_idiographic(). For a result created by a direct ⁠fit_*()⁠ call, the registry can infer the method from a unique registered result class. The declaration describes the scope of committed validation; it is not a new statistical equivalence test.

Usage

equivalence(x)

Arguments

Value

An idiographic_equivalence list with method, status, reference, scope, tolerance, notes, and source.

Examples

set.seed(2)
d <- data.frame(A = rnorm(40), B = rnorm(40))
fit <- fit_var(d, vars = c("A", "B"), scale = FALSE)
equivalence(fit)

Package-wide equivalence and validation ledger

Description

Returns one tidy row for every registered estimator and workflow. Unlike equivalence(), which refines the declaration for one fitted object, equivalence_table() exposes the package-wide evidence boundary before a model is fitted. A closed evidence status means the declared scope has an executable oracle, engine, recovery, or internal-consistency contract; it does not turn native extensions into claims about an unrelated package.

Usage

equivalence_table(method = NULL)

Arguments

Value

A data frame with method, kind, declared status, evidence status, reference, numerical tolerance bounds, scope, and notes.

Examples

equivalence_table()
equivalence_table("gimme")

Momentary self-regulated-learning experience-sampling data

Description

An anonymized intensive longitudinal data set in which 41 students rated their momentary self-regulation, motivation, and anxiety several times per day over the course of a study, giving roughly 70 to 80 occasions each. Unlike the once-per-day srl panel, the occasions here are within-day momentary assessments, so the series are well suited to person-specific (idiographic) VAR, graphical VAR, and unified SEM. The data are fully anonymized: the participant identifiers are fictional names, and the calendar dates have been shifted by a constant offset (preserving all within-person spacing) so that no real dates or identities remain.

Usage

esm_srl

Format

A data.frame with 2820 rows and 12 columns:

name

Fictional participant identifier (41 unique students).

occasion

Within-person occasion index, ordered in time.

date

Anonymized (constant-shifted) assessment date.

efficacy

Momentary self-efficacy (motivation).

value

Momentary task value (motivation).

planning

Momentary planning (self-regulation).

monitoring

Momentary monitoring (self-regulation).

effort

Momentary effort regulation (self-regulation).

regulation

Momentary strategy regulation (self-regulation).

motivated

Momentary felt motivation (motivation).

enjoyment

Momentary enjoyment (motivation).

anxiety

Momentary anxiety.

Details

The nine indicators span three domains: self-regulation (planning, monitoring, effort, regulation), motivation (efficacy, value, motivated, enjoyment), and anxiety. Each variable is on a 0-100 scale. Rows are one person-occasion each, ordered within person by occasion.

Examples

data(esm_srl)
summary(esm_srl)
head(esm_srl)

Estimate edge stability by block resampling (experimental)

Description

Experimental. The resampling design is methodologically grounded (block bootstrap for dependent data; edge-stability summaries in the spirit of bootnet), but unlike the estimators in this package it has no external reference implementation to validate against, and its interface, defaults, and reported statistics may change in a future release.

Refit an idiographic estimator across deterministic block resamples and summarize edge-level stability. Blocks preserve within-block time order: subject-day blocks when id and day are supplied, subjects when only id is supplied, days when only day is supplied, or consecutive row blocks for a single series. Duplicate blocks receive temporary ids/day labels before fitting so lag construction never connects two sampled copies.

Usage

estimate_stability(
  data,
  vars,
  estimator = c("var", "graphical_var", "mlvar", "usem", "gimme"),
  id = NULL,
  day = NULL,
  beep = NULL,
  n_resamples = 100L,
  resample = c("block", "split_half"),
  block_size = NULL,
  threshold = 1e-08,
  seed = NULL,
  keep_fits = FALSE,
  ...
)

Arguments

Value

A stability_result with ⁠$stability⁠ edge statistics, ⁠$original⁠ fit, ⁠$resample_edges⁠, ⁠$failures⁠, and ⁠$config⁠.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:4, each = 12),
                beep = rep(1:12, 4),
                A = rnorm(48), B = rnorm(48), C = rnorm(48))
st <- estimate_stability(d, vars = c("A", "B", "C"), id = "id",
                         day = "day", beep = "beep",
                         n_resamples = 5, seed = 1)
head(st$stability)

Inspect a registered estimator

Description

Inspect a registered estimator

Usage

estimator_info(method)

Arguments

Value

estimator_info() returns the complete registration as a list.

Examples

estimator_info("var")

Tidy edge table from a network object

Description

Returns a one-row-per-edge data.frame with node labels, for any netobject / cograph_network (or a gvar_result constituent).

Usage

extract_edges(model, sort_by = "weight", include_self = FALSE)

Arguments

Value

A data.frame with columns from, to, weight.

Examples

W <- matrix(c(0, 0.3, -0.2, 0), 2, 2,
            dimnames = list(c("A", "B"), c("A", "B")))
x <- structure(list(weights = W, method = "relative", directed = TRUE),
               class = "cograph_network")
extract_edges(x)

GIMME: Group Iterative Multiple Model Estimation

Description

Estimates person-specific directed networks from intensive longitudinal data using the unified Structural Equation Modeling (uSEM) framework. Implements a data-driven search that identifies:

1. Group-level paths: Directed edges present for a majority (default 75\

2. Individual-level paths: Additional edges specific to each person, found after group paths are established.

Uses lavaan for SEM estimation and modification indices. Accepts a single data frame with an ID column (not CSV directories).

Usage

fit_gimme(
  data,
  vars,
  id,
  time = NULL,
  day = NULL,
  beep = NULL,
  min_obs = NULL,
  subject = NULL,
  ar = TRUE,
  standardize = FALSE,
  groupcutoff = 0.75,
  subcutoff = 0.75,
  paths = NULL,
  exogenous = NULL,
  hybrid = FALSE,
  VAR = FALSE,
  rmsea_cutoff = 0.05,
  srmr_cutoff = 0.05,
  nnfi_cutoff = 0.95,
  cfi_cutoff = 0.95,
  n_excellent = 2L,
  seed = NULL,
  group_correct = "Bonferroni Group",
  indiv_correct = "Bonferroni",
  alpha = 0.05,
  stop_crit = "model fit",
  subgroup = FALSE,
  outcome = NULL,
  conv_vars = NULL,
  mult_vars = NULL,
  lv_model = NULL,
  lasso_model_crit = NULL,
  ms_allow = FALSE,
  ordered = NULL,
  dir_prop_cutoff = 0,
  out = NULL,
  sep = NULL,
  header = NULL,
  plot = FALSE,
  sub_feature = "lag & contemp",
  sub_method = "Walktrap",
  sub_sim_thresh = "lowest",
  confirm_subgroup = NULL,
  conv_length = 16,
  conv_interval = 1,
  mean_center_mult = FALSE,
  diagnos = FALSE,
  ms_tol = 1e-05,
  lv_estimator = "miiv",
  lv_scores = "regression",
  lv_miiv_scaling = "first.indicator",
  lv_final_estimator = "miiv"
)

Arguments

Value

An S3 object of class "net_gimme" containing:

temporal

p x p matrix of group-level temporal (lagged) path counts – entry [i,j] = number of individuals with path j(t-1)->i(t).

contemporaneous

p x p matrix of group-level contemporaneous path counts – entry [i,j] = number of individuals with path j(t)->i(t).

coefs

List of per-person q x (q + p) coefficient matrices (q non-exogenous rows; columns = [lagged, contemporaneous]).

psi

List of per-person q x (q + p) standardized residual covariance matrices, with non-exogenous current variables in rows and c(lag_names, varnames) in columns, matching gimme::gimme()'s returned psi contract.

fit

Data frame of per-person fit indices (chisq, df, pvalue, rmsea, srmr, nnfi, cfi, bic, aic, logl, status).

path_counts

p x 2p matrix: how many individuals have each path.

paths

List of per-person character vectors of lavaan path syntax.

group_paths

Character vector of group-level paths found.

individual_paths

List of per-person character vectors of individual-level paths (beyond group).

syntax

List of per-person full lavaan syntax strings.

labels

Character vector of variable names.

n_subjects

Integer. Number of individuals.

n_obs

Integer vector. Time points per individual.

config

List of configuration parameters.

See Also

fit_mlvar, fit_graphical_var, as_netobject

Examples

# Create simple panel data (3 subjects, 4 variables, 50 time points).
set.seed(42)
n_sub <- 3; n_t <- 50; vars <- paste0("V", 1:4)
rows <- lapply(seq_len(n_sub), function(i) {
  d <- as.data.frame(matrix(rnorm(n_t * 4), ncol = 4))
  names(d) <- vars; d$id <- i; d
})
panel <- do.call(rbind, rows)
res <- fit_gimme(panel, vars = vars, id = "id")
print(res)

Graphical VAR Estimation

Description

Estimate a graphical vector autoregressive (GVAR) model from time series or panel data. Jointly estimates a sparse temporal network (L1-penalized VAR coefficients) and a sparse contemporaneous network (graphical lasso on residuals) using EBIC model selection over a lambda grid.

Usage

fit_graphical_var(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  lags = 1L,
  n_lambda = 50L,
  gamma = 0.5,
  scale = TRUE,
  center_within = TRUE,
  lambda_min_ratio = 0.05,
  lambda_min_kappa = NULL,
  lambda_min_beta = NULL,
  penalize_diagonal = TRUE,
  lambda_beta = NULL,
  lambda_kappa = NULL,
  regularize_mat_beta = NULL,
  regularize_mat_kappa = NULL,
  maxit_in = 100L,
  maxit_out = 100L,
  delete_missings = TRUE,
  likelihood = c("unpenalized", "penalized"),
  ebic_tol = 1e-04,
  mimic = "current",
  verbose = FALSE,
  min_obs = NULL,
  subject = NULL
)

Arguments

Details

This is a clean-room reimplementation of the Rothman/Epskamp two-step estimator that is numerically equivalent to graphicalVAR::graphicalVAR(): identical data preparation (global scaling, optional within-person centring, intercept column, lag-1 construction within id/day blocks), identical lambda grids (generate_lambdas), the coupled MRCE beta-update / glasso kappa-update loop, the unpenalized-likelihood EBIC, and the same tie-broken model selection. The committed end-to-end regression tests use tolerance 1e-6, covering both well-conditioned and numerically difficult fits. That equivalence claim is limited to mimic = "current" and lags = 1; multiple lags are an idiographic extension and are labelled as such in the returned equivalence metadata.

Value

A list of class gvar_result containing:

beta

Temporal coefficient matrix, outcome x (intercept + predictors), in graphicalVAR's convention.

temporal

The first requested p x p temporal layer as [outcome, predictor]; unchanged for the default lag 1 fit.

temporal_layers

Named p x p coefficient matrices for every lag.

kappa

Precision matrix (p x p, symmetric).

PCC

Partial contemporaneous correlations -cov2cor(kappa), diagonal zeroed.

PDC

Partial directed correlations.

contemporaneous

Alias for PCC.

labels

Variable names.

n_obs

Number of valid lag-pair observations.

lambda_beta, lambda_kappa

Selected penalties.

gamma, EBIC

EBIC gamma used and the selected EBIC.

References

Epskamp, S., Waldorp, L. J., Mottus, R., & Borsboom, D. (2018). The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivariate Behavioral Research, 53(4), 453-480.

Rothman, A. J., Levina, E., & Zhu, J. (2010). Sparse multivariate regression with covariance estimation. JCGS, 19(4), 947-962.

Examples

set.seed(1)
d <- data.frame(A = rnorm(60), B = rnorm(60))
fit <- fit_graphical_var(d, vars = c("A", "B"), n_lambda = 3,
                         scale = FALSE)
fit$temporal
fit$contemporaneous

Fit a graphical VAR for every subject

Description

Applies fit_graphical_var() to each subject separately, returning one person-specific network per individual — the idiographic "all individuals" workflow. Subjects that cannot be fit (too few lag pairs after listwise deletion) are dropped with a warning.

Usage

fit_graphical_var_each(
  data,
  vars,
  id,
  day = NULL,
  beep = NULL,
  min_obs = NULL,
  ...
)

Arguments

Value

A named list of gvar_result objects (class gvar_list), one element per subject, named by subject id.

Examples

set.seed(2)
d <- data.frame(id = rep(1:2, each = 35),
                A = rnorm(70), B = rnorm(70))
fits <- fit_graphical_var_each(d, vars = c("A", "B"), id = "id",
                               n_lambda = 3, scale = FALSE)
names(fits)

Fit an idiographic model through the unified interface

Description

fit_idiographic() dispatches every built-in estimator and workflow through the same entry point. Arguments may be supplied directly or in params, which makes a stored configuration directly replayable. Direct arguments and params must both be named and cannot overlap; this turns otherwise ambiguous duplicate arguments into an immediate, informative error.

Usage

fit_idiographic(data, method, ..., params = list())

Arguments

Value

The selected method's result, unchanged except for lightweight dispatch and equivalence metadata attributes.

Examples

set.seed(1)
d <- data.frame(A = rnorm(80), B = rnorm(80))
fit <- fit_idiographic(d, "var", vars = c("A", "B"), scale = FALSE)
fit2 <- fit_idiographic(d, "ols-var",
                        params = list(vars = c("A", "B"), scale = FALSE))
equivalence(fit)

Fit idiographic supervised machine-learning models

Description

Fits train/test supervised prediction models in an idiographic design: each subject can receive a model trained only on that subject's earlier rows, and the same held-out rows can also be scored by a pooled model trained on all subjects' earlier rows. This mirrors individualized modelling designs where person-specific prediction is compared against a nomothetic pooled baseline.

The implementation is dependency-free beyond base R. Regression supports mean baseline, ordinary least squares, ridge, lasso, elastic net, principal component regression, k-nearest neighbours, and a one-split regression tree. Binary classification supports majority baseline, logistic regression, ridge/lasso/elastic-net logistic regression, linear discriminant analysis, Gaussian naive Bayes, k-nearest neighbours, and a one-split classification tree. Predictors are standardized using training rows only.

Usage

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,
  ...
)

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,
  ...
)

Arguments

Value

An idioml_result with ⁠$predictions⁠, ⁠$metrics⁠, ⁠$coefficients⁠, ⁠$failures⁠, and optionally ⁠$fits⁠.

Examples

set.seed(1)
d <- data.frame(
  id = rep(1:4, each = 40),
  beep = rep(seq_len(40), 4),
  x1 = rnorm(160),
  x2 = rnorm(160)
)
d$y <- 0.4 * d$x1 - 0.2 * d$x2 + rep(c(-1, 0, 1, 0.5), each = 40) +
  rnorm(160, sd = 0.4)
fit <- fit_ml(d, outcome = "y", predictors = c("x1", "x2"),
              id = "id", beep = "beep",
              model = c("linear", "ridge", "knn"))
fit$metrics
coefs(fit)

Build a Multilevel Vector Autoregression (mlVAR) network

Description

Estimates three networks from ESM/EMA panel data, matching validated mlVAR::mlVAR() configurations at machine precision: (1) a directed temporal network of fixed-effect lagged regression coefficients, (2) an undirected contemporaneous network of partial correlations among residuals, and (3) an undirected between-subjects network of partial correlations derived from the person-mean fixed effects.

Usage

fit_mlvar(
  data,
  vars,
  id,
  day = NULL,
  beep = NULL,
  lags = 1L,
  estimator = c("lmer", "default", "lm", "Mplus"),
  temporal = c("fixed", "correlated", "orthogonal", "unique", "default"),
  contemporaneous = c("fixed", "correlated", "orthogonal", "unique", "default"),
  AR = FALSE,
  scale = FALSE,
  scaleWithin = FALSE,
  nCores = 1L,
  verbose = FALSE,
  lag = NULL,
  standardize = NULL,
  min_obs = NULL,
  subject = NULL,
  engine = c("frequentist", "bayes", "mplus", "reference"),
  standardize_mode = NULL,
  missing = c("omit", "fail", "model"),
  compare_to_lags = NULL,
  true_means = NULL,
  detrend = c("none", "position"),
  na_rm = TRUE,
  orthogonal = NULL,
  ...
)

Arguments

Details

The algorithm follows mlVAR's lmer pipeline exactly:

1. Drop rows with NA in id/day/beep and optionally grand-mean standardize each variable.

2. Expand the per-(id, day) beep grid and right-join original values, producing the augmented panel (augData).

3. Add within-person lagged predictors (⁠L1_*⁠) and person-mean predictors (⁠PM_*⁠).

4. For each outcome variable fit lmer(y ~ within + between-except-own-PM + (1 | id)) with REML = FALSE. Collect the fixed-effect temporal matrix B, between-effect matrix Gamma, random-intercept SDs (mu_SD), and lmer residual SDs.

5. Contemporaneous network: cor2pcor(D %*% cov2cor(cor(resid)) %*% D).

6. Between-subjects network: cor2pcor(pseudoinverse(forcePositive(D (I - Gamma)))).

The committed oracle matrix validates fixed temporal/contemporaneous lmer fits at lags 1 and 1+2, preprocessing controls (scale, scaleWithin, compareToLags, trueMeans, and position detrending), and lag-1 estimator = "lm", temporal = "unique" fits across every supported contemporaneous structure. Other configurations carry a narrower declaration available through equivalence().

Value

A dual-class c("net_mlvar", "netobject_group") object — a named list of three full netobjects, one per network, plus model-level metadata stored as attributes. Each element is a standard c("netobject", "cograph_network") weight-matrix wrapper (no raw ⁠$data⁠), so print(), summary(), coefs(), and cograph::splot(fit$temporal) work directly. The three constituents are matrix-wrapped and carry no underlying panel data, so any data-resampling workflow (bootstrap, reliability, stability) must start from the original panel rather than from these wrappers. Structure:

fit$temporal

Directed netobject for the ⁠d x d⁠ matrix of fixed-effect lagged coefficients. ⁠$weights[i, j]⁠ is the effect of variable j at t-lag on variable i at t. method = "mlvar_temporal", directed = TRUE.

fit$contemporaneous

Undirected netobject for the ⁠d x d⁠ partial-correlation network of within-person lmer residuals. method = "mlvar_contemporaneous", directed = FALSE.

fit$between

Undirected netobject for the ⁠d x d⁠ partial-correlation network of person means, derived from D (I - Gamma). method = "mlvar_between", directed = FALSE. Convention: when a random-intercept SD is 0 the between network is not estimable; idiographic returns an all-zero matrix (with a warning) as a plotting-oriented convention, whereas mlVAR returns an all-NA matrix. The contemporaneous network follows the same zero-on-degeneracy convention. This is a deliberate departure from strict reference equivalence in the singular case.

attr(fit, "coefs") / coefs()

Tidy data.frame with one row per ⁠(outcome, predictor)⁠ pair and columns outcome, predictor, beta, se, t, p, ci_lower, ci_upper, significant. Filter, sort, or plot with base R or the tidyverse. Retrieve with coefs(fit).

attr(fit, "n_obs")

Number of rows in the augmented panel after na.omit.

attr(fit, "n_subjects")

Number of unique subjects remaining.

attr(fit, "lag")

Lag order used.

attr(fit, "standardize")

Logical; whether pre-augmentation standardization was applied.

Observation keys

When beep is supplied, every complete ⁠(id, day, beep)⁠ key (or ⁠(id, beep)⁠ when day = NULL) must be unique. Duplicate keys often indicate that a study-period/session column was lost during data conversion. Because upstream join behaviour is row-order dependent in that case, fit_mlvar() errors and asks you to resolve or explicitly deduplicate the source data.

See Also

fit_gimme(), fit_graphical_var(), as_netobject()

Examples

set.seed(1)
n_id <- 8; n_t <- 30; vars <- c("A", "B", "C")
rows <- lapply(seq_len(n_id), function(i) {
  m <- as.data.frame(matrix(rnorm(n_t * 3), ncol = 3))
  names(m) <- vars
  m$id <- i; m$day <- 1L; m$beep <- seq_len(n_t)
  m
})
d <- do.call(rbind, rows)
fit <- fit_mlvar(d, vars = vars, id = "id", day = "day", beep = "beep")
print(fit)
summary(fit)

Build a Bayesian multilevel VAR network (Mplus DSEM-targeted)

Description

Native, pure-R Bayesian estimator for a two-level VAR(1) that statistically reproduces Mplus DSEM output (the estimator behind mlVAR::mlVAR(estimator = "Mplus")) without needing Mplus installed. A conjugate Gibbs sampler estimates a fixed temporal matrix, a within-person residual (contemporaneous) network, and a between-person network, using latent mean centring and Mplus's default priors. Point estimates are posterior medians with posterior SDs and 95% credible intervals.

Usage

fit_mlvar_bayes(
  data,
  vars,
  id,
  day = NULL,
  beep = NULL,
  lags = 1L,
  temporal = c("fixed", "default", "random"),
  contemporaneous = c("fixed", "default"),
  residual = c("fixed", "random"),
  scale = TRUE,
  scaleWithin = FALSE,
  tinterval = NULL,
  impute = FALSE,
  n_iter = 4000L,
  n_burnin = NULL,
  n_chains = 2L,
  thin = 1L,
  seed = NULL,
  min_obs = NULL,
  subject = NULL,
  verbose = FALSE
)

Arguments

Details

The sampler alternates five conjugate full-conditional draws per iteration: the latent person means mu_i (Gaussian), the fixed temporal matrix B (matrix-normal), the within residual covariance Sigma_W (inverse-Wishart), the grand mean alpha (Gaussian), and the between covariance Sigma_B (inverse-Wishart). The lagged predictor is recentred on the current mu_i draw every iteration (latent mean centring). Data are globally standardized first (matching mlVAR's scale = TRUE); the first observation of each block is used only as a lag (condition-on-first).

Validated to statistical (Monte-Carlo-error) equivalence against real Mplus 9 DSEM output on standardized synthetic panels: posterior medians of B, Sigma_W, Sigma_B agree with Mplus to well within a posterior SD.

Value

A net_mlvar_bayes object (also inheriting net_mlvar), a named list of three netobjects (temporal, contemporaneous, between) with posterior-summary attributes. coefs() returns a tidy table with estimate (posterior median), posterior_sd, ci_lower, ci_upper, p (one-tailed), and significant (95% CI excludes 0). Posterior draws and the max Gelman-Rubin PSR are kept in attributes.

See Also

fit_mlvar() (frequentist lmer path), fit_mlvar_mplus() (true-Mplus wrapper).

Examples

set.seed(1)
n_id <- 10; n_t <- 40; vars <- c("A", "B")
rows <- lapply(seq_len(n_id), function(i) {
  y <- matrix(0, n_t, 2)
  for (t in 2:n_t) y[t, ] <- c(0.3, 0.15) * y[t - 1, ] + rnorm(2)
  data.frame(id = i, beep = seq_len(n_t), A = y[, 1], B = y[, 2])
})
d <- do.call(rbind, rows)
fit <- fit_mlvar_bayes(d, vars = vars, id = "id", beep = "beep",
                         n_iter = 500, seed = 1)
print(fit)
coefs(fit)

Build an Mplus-backed multilevel VAR network

Description

Runs the Mplus Bayesian estimator exposed by mlVAR::mlVAR(estimator = "Mplus") and converts the returned posterior summaries into idiographic's network/tidy accessors. This is a true Mplus backend: Mplus must be installed and discoverable by MplusAutomation::detectMplus().

Usage

fit_mlvar_mplus(
  data,
  vars,
  id,
  day = NULL,
  beep = NULL,
  lags = 1L,
  temporal = c("fixed", "correlated", "orthogonal", "default"),
  contemporaneous = c("fixed", "correlated", "orthogonal", "default"),
  nCores = 1L,
  scale = TRUE,
  scaleWithin = FALSE,
  MplusSave = TRUE,
  MplusName = "mlVAR_mplus",
  iterations = "(2000)",
  chains = nCores,
  signs,
  min_obs = NULL,
  subject = NULL,
  workdir = NULL,
  verbose = TRUE,
  ...
)

Arguments

Value

A net_mplus object, also inheriting from net_mlvar, with temporal, contemporaneous, and between networks plus Mplus metadata in attributes. The original mlVAR/Mplus object is available as attr(x, "mplus").

See Also

fit_mlvar()

Examples

## Not run: 
fit <- fit_mlvar_mplus(
  data, vars = c("A", "B", "C"), id = "id", beep = "time",
  temporal = "fixed", contemporaneous = "fixed",
  MplusName = "my_mplus_mlvar"
)
edges(fit)
attr(fit, "mplus")$output$summaries

## End(Not run)

Estimate rolling-window graphical VAR networks

Description

Fits fit_graphical_var() over ordered, overlapping windows within each subject. This is the time-varying graphical VAR companion to fit_rolling_var(): every window uses graphical VAR's lag construction, EBIC/penalty settings, and tidy coefficient access, then returns one coefficient table per window.

Usage

fit_rolling_graphical_var(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  window_size,
  step = 1L,
  scale = TRUE,
  center_within = TRUE,
  delete_missings = TRUE,
  min_obs = NULL,
  subject = NULL,
  keep_fits = FALSE,
  ...
)

Arguments

Value

A rolling_gvar_result with ⁠$estimates⁠, ⁠$windows⁠, ⁠$failures⁠, and optionally ⁠$fits⁠. ⁠$estimates⁠ is a tidy coefficient table with subject/window metadata plus network, from, to, and weight.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:5, each = 20),
                beep = rep(1:20, 5),
                A = rnorm(100), B = rnorm(100), C = rnorm(100))
tv <- fit_rolling_graphical_var(d, vars = c("A", "B", "C"), id = "id",
                            day = "day", beep = "beep",
                            window_size = 50, step = 25,
                            scale = FALSE, n_lambda = 5)
head(tv$estimates)

Estimate rolling-window ordinary VAR networks

Description

Fits fit_var() over ordered, overlapping windows within each subject. This is a simple time-varying idiographic baseline: every window uses the same lag construction, scaling, within-person centring, and tidy coefficient access as fit_var(), but returns one coefficient table per window.

Usage

fit_rolling_var(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  window_size,
  step = 1L,
  scale = TRUE,
  center_within = TRUE,
  delete_missings = TRUE,
  min_obs = NULL,
  subject = NULL,
  keep_fits = FALSE
)

Arguments

Value

A rolling_var_result with ⁠$estimates⁠, ⁠$windows⁠, ⁠$failures⁠, and optionally ⁠$fits⁠. ⁠$estimates⁠ is a tidy coefficient table with subject/window metadata plus network, from, to, and weight.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:5, each = 20),
                beep = rep(1:20, 5),
                A = rnorm(100), B = rnorm(100), C = rnorm(100))
tv <- fit_rolling_var(d, vars = c("A", "B", "C"), id = "id",
                  day = "day", beep = "beep",
                  window_size = 40, step = 20, scale = FALSE)
head(tv$estimates)

Build a user-specified unified SEM network

Description

Fits person-specific unified Structural Equation Models (uSEM) for intensive longitudinal data. A uSEM combines lagged directed effects, optional contemporaneous directed effects, and optional residual covariances in one SEM. Unlike fit_gimme(), this function does no automated path search: the model is fixed by temporal, contemporaneous, residual_cov, and paths. With trim = TRUE, idiographic uses an independent clean-room modification-index entry and z-value pruning layer over the declared candidate set.

Usage

fit_usem(
  data,
  vars,
  id,
  time = NULL,
  day = NULL,
  beep = NULL,
  min_obs = NULL,
  subject = NULL,
  temporal = c("ar", "all", "none"),
  contemporaneous = c("none", "all"),
  residual_cov = TRUE,
  trim = FALSE,
  trim_alpha = 0.05,
  trim_fit_criteria = 3L,
  cfi_cutoff = 0.95,
  tli_cutoff = 0.95,
  rmsea_cutoff = 0.08,
  srmr_cutoff = 0.08,
  paths = NULL,
  exogenous = NULL,
  standardize = FALSE,
  estimator = "ml",
  seed = NULL
)

Arguments

Value

A net_usem object with average ⁠$temporal⁠, ⁠$contemporaneous⁠, and ⁠$residual_cov⁠ matrices, per-subject matrices in ⁠$subjects⁠, a tidy coefficient table from coefs(), fit indices, syntax, labels, and configuration metadata.

See Also

fit_gimme(), fit_graphical_var(), fit_mlvar()

Examples

set.seed(1)
d <- data.frame(
  id = rep(1:4, each = 30),
  t = rep(seq_len(30), 4),
  A = rnorm(120), B = rnorm(120), C = rnorm(120)
)
fit <- fit_usem(d, vars = c("A", "B", "C"), id = "id", time = "t")
edges(fit)

Build an ordinary least-squares VAR network

Description

Fits a transparent VAR(1) baseline from intensive longitudinal data using ordinary least squares: current variables are regressed on an intercept and lag-1 predictors. The lag construction, scaling, within-person centring, and day-boundary behaviour match fit_graphical_var(), but no regularization or EBIC model selection is applied.

Usage

fit_var(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  lags = 1L,
  scale = TRUE,
  center_within = TRUE,
  delete_missings = TRUE,
  min_obs = NULL,
  subject = NULL
)

Arguments

Value

A var_result object with temporal OLS coefficients, residual covariance, residual precision, contemporaneous partial correlations, and tidy access through edges(), coefs(), nodes(), and summary().

Examples

set.seed(1)
d <- data.frame(id = 1, A = rnorm(80), B = rnorm(80), C = rnorm(80))
fit <- fit_var(d, vars = c("A", "B", "C"), id = "id")
edges(fit)

Build a Bayesian VAR(1) network (unregularized, Mplus-targeted)

Description

Native, pure-R Bayesian VAR(1) that reproduces Mplus's Bayesian (DSEM/time-series) estimates without needing Mplus. It is the unregularized Bayesian counterpart of fit_graphical_var(): instead of a graphical-lasso / EBIC sparse fit, it estimates a full VAR(1) with a flat prior on the temporal coefficients and an inverse-Wishart prior on the residual precision, then reports the temporal network B and the contemporaneous partial-correlation network derived from the residual covariance. With more than one subject the data are within-person centred and pooled (as in fit_graphical_var()).

Usage

fit_var_bayes(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  lags = 1L,
  scale = TRUE,
  center_within = TRUE,
  n_iter = 4000L,
  n_burnin = NULL,
  n_chains = 2L,
  thin = 1L,
  seed = NULL,
  min_obs = NULL,
  subject = NULL,
  verbose = FALSE
)

Arguments

Value

A var_bayes_result object (a cograph group with temporal and contemporaneous netobjects) carrying beta, temporal, kappa, PCC, PDC, posterior draws, and a tidy coefs() table (posterior median, SD, 95% CI, one-tailed p, significance by CI excluding 0).

See Also

fit_graphical_var() (regularized GLASSO/EBIC), fit_var() (OLS), fit_mlvar_bayes() (multilevel Bayesian VAR).

Examples

set.seed(1)
y <- matrix(0, 200, 2)
for (t in 2:200) y[t, ] <- c(0.4, 0.3) * y[t - 1, ] + rnorm(2)
d <- data.frame(A = y[, 1], B = y[, 2])
fit <- fit_var_bayes(d, vars = c("A", "B"), n_iter = 500, seed = 1)
print(fit)
coefs(fit)

Fit an ordinary least-squares VAR for every subject

Description

Applies fit_var() to each subject separately, returning one transparent person-specific OLS VAR result per individual. This is the unregularized companion to fit_graphical_var_each() and is useful as an equivalence baseline for checking lag construction, scaling, and temporal coefficient direction.

Usage

fit_var_each(data, vars, id, day = NULL, beep = NULL, min_obs = NULL, ...)

Arguments

Value

A named list of var_result objects (class var_list), one element per subject, named by subject id. Subjects that cannot be fit are dropped with a warning.

Examples

set.seed(1)
d <- data.frame(
  id = rep(1:3, each = 40),
  day = rep(1, 120),
  beep = rep(seq_len(40), 3),
  A = rnorm(120), B = rnorm(120), C = rnorm(120)
)
fits <- fit_var_each(d, vars = c("A", "B", "C"), id = "id",
                       day = "day", beep = "beep")
fits[["1"]]

Get a registered estimator function

Description

Get a registered estimator function

Usage

get_estimator(method)

Arguments

Value

get_estimator() returns the registered fitting function.

Examples

var_fitter <- get_estimator("var")
is.function(var_fitter)

Registered idiographic estimators

Description

idiographic uses a small registry to give estimators and workflows one stable dispatch interface. Package methods are registered lazily, so the registry does not depend on source-file load order. Third-party methods can register either a function or the name of a function available in the package namespace or calling environment.

Usage

list_estimators(kind = NULL)

Arguments

Value

list_estimators() returns one row per registration.

Examples

list_estimators()
list_estimators("workflow")

Print model matrices for idiographic results

Description

matrices() is the matrix-oriented companion to summary() and edges(). It returns the core estimated matrices invisibly and prints each matrix compactly with rounding, so users can inspect coefficients without digging through object internals.

Usage

matrices(x, ...)

## Default S3 method:
matrices(x, digits = 3, ...)

## S3 method for class 'cograph_network'
matrices(x, digits = 3, ...)

## S3 method for class 'netobject'
matrices(x, digits = 3, ...)

## S3 method for class 'netobject_group'
matrices(x, digits = 3, ...)

## S3 method for class 'gvar_result'
matrices(x, digits = 3, ...)

## S3 method for class 'var_result'
matrices(x, digits = 3, ...)

## S3 method for class 'net_mlvar'
matrices(x, digits = 3, ...)

## S3 method for class 'net_usem'
matrices(x, digits = 3, ...)

## S3 method for class 'net_gimme'
matrices(x, digits = 3, ...)

## S3 method for class 'preprocess_result'
matrices(x, digits = 3, ...)

## S3 method for class 'rolling_var_result'
matrices(x, fit = 1L, digits = 3, ...)

## S3 method for class 'rolling_gvar_result'
matrices(x, fit = 1L, digits = 3, ...)

## S3 method for class 'stability_result'
matrices(x, digits = 3, ...)

## S3 method for class 'model_comparison'
matrices(x, fit = 1L, digits = 3, ...)

## S3 method for class 'var_list'
matrices(x, subject = 1L, digits = 3, ...)

## S3 method for class 'gvar_list'
matrices(x, subject = 1L, digits = 3, ...)

Arguments

Value

Invisibly, a named list of matrices.

Examples

W <- matrix(c(0, 0.3, -0.2, 0), 2, 2,
            dimnames = list(c("A", "B"), c("A", "B")))
x <- structure(list(weights = W, method = "relative", directed = TRUE),
               class = "cograph_network")
matrices(as_netobject(x))

Tidy per-node strength table for any idiographic result

Description

One row per node per network with strength (sum of absolute incident edge weights) and, for directed networks, out_strength / in_strength (NA for undirected). Self-loops are excluded.

Usage

## S3 method for class 'net_usem'
nodes(x, ...)

## S3 method for class 'var_result'
nodes(x, ...)

nodes(x, ...)

## S3 method for class 'netobject'
nodes(x, ...)

## S3 method for class 'netobject_group'
nodes(x, ...)

## S3 method for class 'gvar_result'
nodes(x, ...)

## S3 method for class 'net_mlvar'
nodes(x, ...)

## S3 method for class 'net_gimme'
nodes(x, ...)

## S3 method for class 'var_list'
nodes(x, ...)

## S3 method for class 'gvar_list'
nodes(x, ...)

Arguments

Value

A tidy data.frame.

Examples

W <- matrix(c(0, 0.3, -0.2, 0), 2, 2,
            dimnames = list(c("A", "B"), c("A", "B")))
x <- structure(list(weights = W, method = "relative", directed = TRUE),
               class = "cograph_network")
nodes(as_netobject(x))

Faithful GIMME network plot (the gimme-package convention, via cograph)

Description

Draws a GIMME result the way the gimme package does: a single p-node network where dashed edges are lag-1 (temporal) and solid edges are lag-0 (contemporaneous), edge width is the proportion of subjects that have the path, black edges are group-level paths and grey edges individual-level, and autoregression shows as a dashed self-loop. Rendered with cograph::splot(), so a lag and a contemporaneous effect between the same pair are drawn as two parallel edges.

Usage

plot_gimme(
  x,
  weight = c("prop", "coef"),
  group_color = "black",
  individual_color = "grey60",
  layout = "circle",
  curvature = 0.25,
  edge_scale = 5,
  ...
)

Arguments

Value

Invisibly, the mixed cograph_network object that was plotted.

See Also

as_netobject() for the matrix view.

Examples

set.seed(1)
panel <- data.frame(
  id = rep(1:5, each = 30),
  t  = rep(seq_len(30), 5),
  A  = rnorm(150), B = rnorm(150), C = rnorm(150)
)
gm <- fit_gimme(panel, vars = c("A", "B", "C"), id = "id", time = "t")
plot_gimme(gm)

Plot an idiographic network result

Description

S3 plot() methods that render any idiographic result with cograph::splot(). Call plot(fit) to draw the full result (every network panel) or pass layer to draw a single network – "temporal", "contemporaneous", "between" (mlVAR), or "residual_cov" (uSEM) – without indexing into the object.

Usage

## S3 method for class 'var_result'
plot(x, layer = NULL, mixed = FALSE, ...)

## S3 method for class 'gvar_result'
plot(x, layer = NULL, mixed = FALSE, ...)

## S3 method for class 'var_bayes_result'
plot(x, layer = NULL, mixed = FALSE, ...)

## S3 method for class 'net_mlvar'
plot(x, layer = NULL, mixed = FALSE, ...)

## S3 method for class 'net_usem'
plot(x, layer = NULL, mixed = FALSE, ...)

## S3 method for class 'net_gimme'
plot(x, layer = NULL, weight = c("prop", "coef"), ...)

## S3 method for class 'var_list'
plot(x, subject = 1L, layer = NULL, ...)

## S3 method for class 'gvar_list'
plot(x, subject = 1L, layer = NULL, ...)

## S3 method for class 'rolling_var_result'
plot(x, fit = 1L, layer = NULL, ...)

## S3 method for class 'rolling_gvar_result'
plot(x, fit = 1L, layer = NULL, ...)

## S3 method for class 'stability_result'
plot(x, layer = NULL, ...)

Arguments

Value

Invisibly, the object that was plotted (a cograph/ggplot object).

Examples

set.seed(1)
d <- data.frame(id = 1, A = rnorm(80), B = rnorm(80), C = rnorm(80))
fit <- fit_var(d, vars = c("A", "B", "C"), id = "id")
plot(fit)
plot(fit, layer = "temporal")

Predict from an idiographic ML result

Description

Predict from an idiographic ML result

Usage

## S3 method for class 'idioml_result'
predict(
  object,
  newdata = NULL,
  scope = c("pooled", "individual"),
  model = NULL,
  estimator = NULL,
  type = c("response", "class"),
  ...
)

Arguments

Value

A data.frame of predictions.

Preprocess and audit idiographic time-series data

Description

Builds the same lag-1 design used by fit_graphical_var() and fit_var(), optionally detrends or differences each series, and returns tidy diagnostics for missingness, day-boundary drops, simple linear trends, AR(1) persistence, split-half mean/variance drift, an ADF-style unit-root screen, and zero-variance variables. It makes the modelling input explicit before estimating VAR, graphical VAR, uSEM, GIMME, or mlVAR models; with detrend it also cleans a non-stationary series in place so the flags can be rechecked on the transformed data.

Usage

preprocess(
  data,
  vars,
  id = NULL,
  day = NULL,
  beep = NULL,
  scale = TRUE,
  center_within = TRUE,
  detrend = "none",
  checks = c("trend", "high_ar", "unit_root", "mean_shift", "sd_shift", "zero_variance"),
  delete_missings = TRUE,
  min_obs = NULL,
  subject = NULL,
  trend_alpha = 0.05,
  ar_threshold = 0.95,
  mean_shift_threshold = 0.8,
  sd_ratio_threshold = 2,
  unit_root_t_cutoff = -2.86
)

Arguments

Value

A preprocess_result object with:

pairs

The ordered current/lagged design table, including intercept and ⁠L1_*⁠ columns.

counts

Per-subject/per-day row and lag-pair counts.

diagnostics

Per-subject/per-variable missingness, trend, AR(1), split-half drift, unit-root screen, and stationarity risk indicators.

matrices

The exact data_c and data_l matrices returned by the VAR/GVAR preprocessing path.

Examples

set.seed(1)
d <- data.frame(id = 1, day = 1, beep = 1:40,
                A = cumsum(rnorm(40)), B = rnorm(40))
pp <- preprocess(d, vars = c("A", "B"), id = "id", day = "day", beep = "beep")
pp$counts
pp$diagnostics
# Difference the trending series and recheck the flags:
preprocess(d, vars = c("A", "B"), id = "id", day = "day", beep = "beep",
           detrend = "difference")$diagnostics

Print method for forecast validation results

Description

Print method for forecast validation results

Usage

## S3 method for class 'forecast_result'
print(x, ...)

Arguments

Value

x, invisibly.

Print a list of per-subject graphical VARs

Description

Print a list of per-subject graphical VARs

Usage

## S3 method for class 'gvar_list'
print(x, ...)

Arguments

Value

x, invisibly.

Print Method for gvar_result

Description

Print Method for gvar_result

Usage

## S3 method for class 'gvar_result'
print(x, digits = 2, ...)

Arguments

Value

The input object, invisibly.

Print method for idiographic ML fits

Description

Print method for idiographic ML fits

Usage

## S3 method for class 'idioml_result'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for model comparisons

Description

Print method for model comparisons

Usage

## S3 method for class 'model_comparison'
print(x, ...)

Arguments

Value

x, invisibly.

Print Method for net_gimme

Description

Print Method for net_gimme

Usage

## S3 method for class 'net_gimme'
print(x, digits = 2, ...)

Arguments

Value

The input object, invisibly.

Examples

set.seed(1)
panel <- data.frame(
  id = rep(1:5, each = 20),
  t  = rep(seq_len(20), 5),
  A  = rnorm(100), B = rnorm(100), C = rnorm(100)
)
gm <- fit_gimme(panel, vars = c("A","B","C"), id = "id", time = "t")
print(gm)

Print method for net_mlvar

Description

Print method for net_mlvar

Usage

## S3 method for class 'net_mlvar'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x.

Examples

set.seed(1)
n_id <- 8; n_t <- 30; vars <- c("A", "B", "C")
rows <- lapply(seq_len(n_id), function(i) {
  m <- as.data.frame(matrix(rnorm(n_t * 3), ncol = 3))
  names(m) <- vars
  m$id <- i; m$day <- 1L; m$beep <- seq_len(n_t)
  m
})
d <- do.call(rbind, rows)
fit <- fit_mlvar(d, vars = vars, id = "id", day = "day", beep = "beep")
print(fit)
summary(fit)

Print method for net_mlvar_bayes

Description

Print method for net_mlvar_bayes

Usage

## S3 method for class 'net_mlvar_bayes'
print(x, digits = 2, ...)

Arguments

Value

Invisibly returns x.

Print method for uSEM fits

Description

Print method for uSEM fits

Usage

## S3 method for class 'net_usem'
print(x, digits = 2, ...)

Arguments

Value

x, invisibly.

Print method for preprocessing results

Description

Print method for preprocessing results

Usage

## S3 method for class 'preprocess_result'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for rolling graphical VAR results

Description

Print method for rolling graphical VAR results

Usage

## S3 method for class 'rolling_gvar_result'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for rolling VAR results

Description

Print method for rolling VAR results

Usage

## S3 method for class 'rolling_var_result'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for stability results

Description

Print method for stability results

Usage

## S3 method for class 'stability_result'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for var_bayes_result

Description

Print method for var_bayes_result

Usage

## S3 method for class 'var_bayes_result'
print(x, digits = 2, ...)

Arguments

Value

Invisibly x.

Print a list of per-subject ordinary VARs

Description

Print a list of per-subject ordinary VARs

Usage

## S3 method for class 'var_list'
print(x, ...)

Arguments

Value

x, invisibly.

Print method for ordinary VAR fits

Description

Print method for ordinary VAR fits

Usage

## S3 method for class 'var_result'
print(x, digits = 2, ...)

Arguments

Value

x, invisibly.

Register an idiographic estimator or workflow

Description

Register an idiographic estimator or workflow

Usage

register_estimator(
  name,
  fit,
  aliases = character(),
  kind = c("estimator", "workflow"),
  description = "",
  result_class = character(),
  equivalence = list(),
  overwrite = FALSE
)

Arguments

Value

register_estimator() invisibly returns the new registration.

Examples

demo_fitter <- function(data, ...) structure(list(data = data),
                                             class = "demo_result")
register_estimator("demo", demo_fitter, result_class = "demo_result")
get_estimator("demo")
remove_estimator("demo")

Remove a registered estimator

Description

Remove a registered estimator

Usage

remove_estimator(method, missing_ok = FALSE)

Arguments

Value

Invisibly returns the removed registration, or NULL when missing_ok = TRUE and no registration exists.

Examples

temp_fitter <- function(data, ...) data
register_estimator("temporary", temp_fitter)
remove_estimator("temporary")
remove_estimator("temporary", missing_ok = TRUE)

Self-regulated learning intensive longitudinal data (Chapter 20)

Description

The self-regulated learning (SRL) experience-sampling data used in the Learning Analytics Methods book, Chapter 20 (Vector Autoregression). Each of 36 students reported nine self-regulated-learning indicators once per study occasion for 156 occasions, giving a balanced person-by-time panel suitable for the idiographic time-series methods in this package.

Usage

srl

Format

A data.frame with 5616 rows and 11 columns:

name

Student name (36 unique students).

day

Within-person occasion index, 1-156.

efficacy

Self-efficacy.

value

Task value.

planning

Planning.

monitoring

Monitoring.

effort

Effort regulation.

control

Control of learning.

help

Help seeking.

social

Social support.

organizing

Organizing.

Details

The columns have already been tidied for modelling: rows are ordered by name then day, and day is a within-person occasion index (1-156) you can pass as the time argument to fit_usem() and fit_gimme(). No further ordering, indexing, or column selection is needed before fitting a model.

Source

Learning Analytics Methods, Book 2, Chapter 20 (VAR): https://lamethods.org/book2/chapters/ch20-var/ch20-var.html. Original data: https://github.com/lamethods/data2/raw/main/srl/srl.RDS, licensed under CC BY-NC-SA 4.0. See the package COPYRIGHTS file for attribution and transformation details.

Examples

data(srl)
summary(srl)
head(srl)

Summary Method for gvar_result

Description

Summary Method for gvar_result

Usage

## S3 method for class 'gvar_result'
summary(object, ...)

Arguments

Value

A tidy data.frame of per-network metrics: one row per network (temporal, contemporaneous) with n_nodes, n_edges, density, mean_abs_weight, n_positive, n_negative. Use edges(object) / coefs(object) for the estimates and nodes(object) for node strengths.

Summary method for idiographic ML fits

Description

Summary method for idiographic ML fits

Usage

## S3 method for class 'idioml_result'
summary(object, ...)

Arguments

Value

The metrics table.

Summary Method for net_gimme

Description

Summary Method for net_gimme

Usage

## S3 method for class 'net_gimme'
summary(object, ...)

Arguments

Value

A tidy data.frame of per-network metrics (one row per network: temporal, contemporaneous), with n_edges/density/etc. computed from the proportion-of-subjects networks. Per-subject fit indices are in object$fit; coefs(object) gives the per-person estimates, edges(object) the tidy edge list, and nodes(object) node strengths.

Examples

set.seed(1)
panel <- data.frame(
  id = rep(1:5, each = 20),
  t  = rep(seq_len(20), 5),
  A  = rnorm(100), B = rnorm(100), C = rnorm(100)
)
gm <- fit_gimme(panel, vars = c("A","B","C"), id = "id", time = "t")
summary(gm)

Summary method for net_mlvar

Description

Summary method for net_mlvar

Usage

## S3 method for class 'net_mlvar'
summary(object, ...)

Arguments

Value

A tidy data.frame of per-network metrics (one row per network: temporal, contemporaneous, between). Use coefs(object) for the fixed-effect coefficient table, edges(object) for the edge list, and nodes(object) for node strengths.

Examples

set.seed(1)
n_id <- 8; n_t <- 30; vars <- c("A", "B", "C")
rows <- lapply(seq_len(n_id), function(i) {
  m <- as.data.frame(matrix(rnorm(n_t * 3), ncol = 3))
  names(m) <- vars
  m$id <- i; m$day <- 1L; m$beep <- seq_len(n_t)
  m
})
d <- do.call(rbind, rows)
fit <- fit_mlvar(d, vars = vars, id = "id", day = "day", beep = "beep")
print(fit)
summary(fit)

Summary method for uSEM fits

Description

Summary method for uSEM fits

Usage

## S3 method for class 'net_usem'
summary(object, ...)

Arguments

Value

A tidy per-network metrics data.frame.

Summary method for preprocessing results

Description

Compact per-variable roll-up of the diagnostics: one row per variable with its mean spread and the number of subject-series that tripped each stationarity flag. Use x$diagnostics for the full per-subject table.

Usage

## S3 method for class 'preprocess_result'
summary(object, ...)

Arguments

Value

A tidy per-variable data.frame.

Summary method for var_bayes_result

Description

Summary method for var_bayes_result

Usage

## S3 method for class 'var_bayes_result'
summary(object, ...)

Arguments

Value

A tidy per-network metrics data.frame.

Summary method for ordinary VAR fits

Description

Summary method for ordinary VAR fits

Usage

## S3 method for class 'var_result'
summary(object, ...)

Arguments

Value

A tidy per-network metrics data.frame.

Validate one-step forecasts from idiographic VAR models (experimental)

Description

Experimental. The rolling-origin design follows standard time-series cross-validation practice, but unlike the estimators in this package it has no external reference implementation to validate against, and its interface, defaults, and reported metrics may change in a future release.

Performs rolling-origin one-step prediction from fit_var() or fit_graphical_var(). Each split fits the estimator on earlier blocks and predicts current variables in the next block from their lag-1 values. Scaling and within-person centring parameters are learned from the training split only, then applied to the assessment split before prediction.

Usage

validate_forecast(
  data,
  vars,
  estimator = c("var", "graphical_var"),
  id = NULL,
  day = NULL,
  beep = NULL,
  initial = NULL,
  assess = 1L,
  step = 1L,
  n_splits = NULL,
  block_size = NULL,
  scale = TRUE,
  center_within = TRUE,
  delete_missings = TRUE,
  keep_fits = FALSE,
  ...
)

Arguments

Value

A forecast_result with ⁠$predictions⁠, ⁠$metrics⁠, ⁠$splits⁠, ⁠$failures⁠, and optionally ⁠$fits⁠.

Examples

set.seed(1)
d <- data.frame(id = 1, day = rep(1:5, each = 12),
                beep = rep(1:12, 5),
                A = rnorm(60), B = rnorm(60), C = rnorm(60))
fc <- validate_forecast(d, vars = c("A", "B", "C"), id = "id",
                        day = "day", beep = "beep",
                        initial = 3, n_splits = 2, scale = FALSE)
fc$metrics