Package {MEMWAS}

Type:	Package
Title:	Mixed-Effects Models with Autocorrelation Structures
Version:	0.9.3
Date:	2026-08-04
Description:	Fits longitudinal generalized mixed-effects models through the 'MEMWAS' interface and a registered 'C++' numerical backend. Supported serial covariance structures include first-order autoregressive (AR(1)), exponential or Ornstein-Uhlenbeck, higher-order autoregressive (AR(p)), first-order autoregressive moving-average (ARMA(1,1)), compound symmetry, Toeplitz, and unstructured covariance. Serial processes can be unified or attached independently to numeric predictor loadings. Candidate temporal structures can be ranked on a common sample by primary-cluster grouped cross-validation, the Akaike information criterion, the Bayesian information criterion, or log-likelihood. Clustered, crossed, and nested random intercepts and slopes are assembled jointly with diagonal or term-specific unstructured covariance. Available approximation methods include Laplace, saddlepoint likelihood with latent Laplace integration, adaptive Gaussian quadrature, full-covariance Gaussian variational inference, and penalized quasi-likelihood. Subject-grouped tuning requires every validation fold to succeed and supports fold-local nonlinear screening, bootstrap inference, prediction inference, and effective degrees of freedom for penalized information criteria. The mixed-effects framework follows Laird and Ware (1982) <doi:10.2307/2529876>; generalized-model approximations follow Breslow and Clayton (1993) <doi:10.1080/01621459.1993.10594284>; and serial covariance formulations follow Pinheiro and Bates (2000) <doi:10.1007/b98882>. The run-time fitting interface imports no third-party 'R' packages.
License:	GPL (≥ 3)
Depends:	R (≥ 4.1.0)
Imports:	stats, utils
Config/roxygen2/version:	8.0.0
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr, rmarkdown
Config/testthat/edition:	3
NeedsCompilation:	yes
Language:	en-US
Encoding:	UTF-8
Packaged:	2026-08-04 03:02:10 UTC; KYN
Author:	Enoch Kang [aut, cre, trl]
Maintainer:	Enoch Kang <y.enoch.kang@gmail.com>
Repository:	CRAN
Date/Publication:	2026-08-08 14:00:02 UTC

MEMWAS package

Description

Longitudinal generalized mixed-effects models with flexible random-effect and serial dependence structures, common-sample temporal-structure ranking, grouped tuning, optional nonlinearity screening, optional assumption diagnostics, and compiled C++ numerical kernels accessed through base R's '.Call' interface.

Author(s)

Maintainer: Enoch Kang y.enoch.kang@gmail.com (ORCID) [translator]

Authors:

• Enoch Kang y.enoch.kang@gmail.com (ORCID) [translator]

Report MEMWAS fitting capabilities

Description

Returns the model and approximation contract used by 'fit_MEMWAS()', 'tune_MEMWAS()', and 'rank_autocorrelation_structures()'.

Usage

MEMWAS_capabilities()

Value

Data frame with one row per approximation and columns describing supported families and links, random effects, covariance structures, serial processes, penalties, estimation, initialization, standard-error methods, prediction modes, computational kernels, and unavailable combinations. The 'registry_version' attribute records the contract version.

Examples

MEMWAS_capabilities()

Check assumptions for a fitted MEMWAS model

Description

Runs selected diagnostic screens for temporal autocorrelation, response distribution and link adequacy, residual conditional independence, random-effect normality, random-effect–predictor independence, and variance homogeneity. Diagnostics are separate from ordinary model fitting and can be requested after inspecting the fitted model.

Usage

check_MEMWAS_assumptions(
  object,
  autocorrelation_check = "All",
  distribution_link_check = "All",
  conditional_independence_check = "All",
  random_effects_normality_check = "All",
  random_effects_predictor_independence_check = "All",
  homogeneity_variance_check = "All",
  alpha = 0.05,
  ...
)

## S3 method for class 'MEMWAS_assumption_checks'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

Details

Holm adjustment is applied jointly to every requested diagnostic with an available finite p-value. Structurally unavailable diagnostics remain in the output with 'available = FALSE'; an unavailable row is not evidence that its assumption holds. Method names describe the calculations actually performed and should not be interpreted as exact implementations of similarly named textbook tests unless the result detail explicitly says so.

Value

Object of class 'MEMWAS_assumption_checks'. It contains the normalized selector specification, requested-method table, full results, flagged rows, unavailable rows with explanations, temporal-autocorrelation subset, alpha, multiplicity method, settings, and call.

The input object, invisibly.

Examples

toy <- data.frame(
  id = rep(seq_len(6), each = 3),
  time = rep(0:2, times = 6),
  x = rep(c(-1, 0, 1), times = 6)
)
toy$y <- 1 + 0.6 * toy$x +
  rep(c(-0.15, -0.08, 0, 0.05, 0.10, 0.18), each = 3) +
  rep(c(-0.03, 0.04, -0.01), times = 6)
toy_fit <- fit_MEMWAS(
  y ~ x, data = toy, id = "id", time = "time",
  random = NULL, autocor = "NONE", se_method = "none",
  control = list(n_starts = 1L, cold_start_verification = FALSE),
  verbose = FALSE
)
check_MEMWAS_assumptions(
  toy_fit,
  autocorrelation_check = "WithinClusterLag1CorrelationScreen",
  distribution_link_check = "None",
  conditional_independence_check = "None",
  random_effects_normality_check = "None",
  random_effects_predictor_independence_check = "None",
  homogeneity_variance_check = "None"
)


dat <- data.frame(
  id = rep(seq_len(15), each = 4),
  time = rep(0:3, times = 15),
  x = stats::rnorm(60)
)
dat$y <- 0.5 + 0.7 * dat$x + stats::rnorm(60)
fit <- fit_MEMWAS(
  y ~ x, data = dat, id = "id", time = "time", random = ~ 1,
  autocor = "NONE", verbose = FALSE
)
checks <- check_MEMWAS_assumptions(fit)
print(checks)

Methods for fitted MEMWAS models

Description

Extracts coefficient versions, covariance matrices, likelihood/objective values, information criteria, fitted values, residuals, observation counts, summaries, and printed representations from fitted MEMWAS models.

Usage

## S3 method for class 'MEMWAS_fit'
coef(object, type = c("adjusted", "original", "both"), ...)

## S3 method for class 'MEMWAS_family_fit'
coef(object, ...)

## S3 method for class 'MEMWAS_fit'
vcov(object, full = FALSE, ...)

## S3 method for class 'MEMWAS_family_fit'
vcov(object, full = FALSE, ...)

## S3 method for class 'MEMWAS_fit'
logLik(object, ...)

## S3 method for class 'MEMWAS_family_fit'
logLik(object, ...)

## S3 method for class 'MEMWAS_fit'
AIC(object, ..., k = 2)

## S3 method for class 'MEMWAS_family_fit'
AIC(object, ..., k = 2)

## S3 method for class 'MEMWAS_fit'
fitted(object, ...)

## S3 method for class 'MEMWAS_family_fit'
fitted(object, ...)

## S3 method for class 'MEMWAS_fit'
residuals(object, type = c("response", "pearson"), ...)

## S3 method for class 'MEMWAS_family_fit'
residuals(object, type = c("response", "pearson"), ...)

## S3 method for class 'MEMWAS_fit'
nobs(object, ...)

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

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

## S3 method for class 'MEMWAS_fit'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

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

## S3 method for class 'summary.MEMWAS_fit'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

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

Arguments

Details

'coef(type = "adjusted")' returns the coefficients used for fitted values and predictions. 'coef(type = "original")' returns the unpenalized reference coefficients for the same selected formula and dependence specification. 'coef(type = "both")' reports both vectors, their signed adjustment, and a regularization indicator. For approximation-specific objectives that are not marginal likelihoods, 'logLik()' returns a labelled objective value and 'AIC()' returns 'NA' with a warning rather than treating unlike objectives as likelihoods.

Value

The requested numeric vector, matrix, scalar, summary object, or the input object invisibly for print methods. 'coef(type = "both")' returns a data frame containing original coefficients, adjusted coefficients, and regularization adjustments.

Methods for tuned MEMWAS objects

Description

Extracts selected coefficients or penalty parameters, creates a structured tuning summary, and prints tuning or summary objects.

Usage

## S3 method for class 'tune_MEMWAS'
coef(object, ...)

## S3 method for class 'tune_MEMWAS'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

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

## S3 method for class 'summary.tune_MEMWAS'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

Details

When a final refit exists, 'coef()' supports the coefficient 'type' choices documented for 'coef.MEMWAS_fit()'. Without a final refit it returns the selected penalty parameters. The summary includes grouped-fold performance, stability, convergence, selected penalties, and the propagated Original Model and Final equations when available.

Value

'coef()' returns a coefficient vector, coefficient-version table, or selected penalty vector. 'summary()' returns an object of class 'summary.tune_MEMWAS'. Print methods return their input invisibly.

Compare MEMWAS approximation requests

Description

Fits the same model under each requested approximation and reports the dispatched method, computational kernel, objective semantics, information criteria, and convergence state.

Usage

compare_approximations(approximations = c("laplace"), ...)

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

Arguments

Value

Object of class 'MEMWAS_approximation_comparison' containing a summary table and the fitted model for every requested approximation.

Examples

toy <- data.frame(
  id = rep(seq_len(6), each = 3),
  time = rep(0:2, times = 6),
  x = rep(c(-1, 0, 1), times = 6)
)
toy$y <- 1 + 0.6 * toy$x +
  rep(c(-0.15, -0.08, 0, 0.05, 0.10, 0.18), each = 3) +
  rep(c(-0.03, 0.04, -0.01), times = 6)
one_method <- compare_approximations(
  "laplace", formula = y ~ x, data = toy, id = "id", time = "time",
  random = NULL, autocor = "NONE", se_method = "none",
  control = list(n_starts = 1L, cold_start_verification = FALSE)
)
one_method$table


dat <- data.frame(
  id = rep(seq_len(10), each = 4),
  time = rep(0:3, times = 10),
  x = rep(c(-1, -0.25, 0.25, 1), times = 10)
)
dat$y <- 1 + 0.5 * dat$x
comparison <- compare_approximations(
  c("laplace", "variational_inference"),
  formula = y ~ x, data = dat, id = "id", time = "time"
)
comparison$table

Diagnose a MEMWAS approximation

Description

Summarizes objective semantics, optimizer stationarity, boundary conditions, design rank, covariance-Jacobian rank, and approximation-specific convergence.

Usage

diagnose_approximation(object)

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

Arguments

Value

Object of class 'MEMWAS_approximation_diagnostics' containing objective, optimizer, gradient, step, KKT, boundary, stationarity, rank, and approximation metadata.

Examples

toy <- data.frame(
  id = rep(seq_len(6), each = 3),
  time = rep(0:2, times = 6),
  x = rep(c(-1, 0, 1), times = 6)
)
toy$y <- 1 + 0.6 * toy$x +
  rep(c(-0.15, -0.08, 0, 0.05, 0.10, 0.18), each = 3) +
  rep(c(-0.03, 0.04, -0.01), times = 6)
toy_fit <- fit_MEMWAS(
  y ~ x, data = toy, id = "id", time = "time",
  random = NULL, autocor = "NONE", se_method = "none",
  control = list(n_starts = 1L, cold_start_verification = FALSE),
  verbose = FALSE
)
diagnose_approximation(toy_fit)


dat <- data.frame(
  id = rep(seq_len(10), each = 4),
  time = rep(0:3, times = 10),
  x = rep(c(-1, -0.25, 0.25, 1), times = 10)
)
dat$y <- 1 + 0.5 * dat$x
fit <- fit_MEMWAS(
  y ~ x, data = dat, id = "id", time = "time", verbose = FALSE
)
diagnose_approximation(fit)

Fit a MEMWAS mixed-effects model

Description

Fits longitudinal generalized mixed-effects models with clustered, crossed, or nested random effects and unified or predictor-specific serial processes. Nonlinearity screening and assumption diagnostics are separate optional stages and are disabled by default so that ordinary fitting avoids their additional computation.

Usage

fit_MEMWAS(
  formula,
  family = "gaussian",
  data,
  id,
  time,
  random = ~1,
  autocor = "AR(1)",
  serial = NULL,
  residual_autocor = NULL,
  predictor_autocor = NULL,
  L1_penalty = 0,
  L2_penalty = 0,
  control = list(),
  method = "ML",
  random_cov = "unstructured",
  approximation = "laplace",
  init_approximation = "variational_inference",
  quadrature_points = 7L,
  se_method = "hessian",
  link = NULL,
  offset = NULL,
  weights = NULL,
  subset = NULL,
  na.action = NULL,
  theta = NULL,
  shape = NULL,
  start = NULL,
  dot_predictors = NULL,
  dot_alternative = NULL,
  dot_threshold = 0,
  dot_alpha = 0.05,
  nonlinear_alpha = 0.05,
  spline_knots = 4L,
  spline_probs = NULL,
  nonlinear_predictors = NULL,
  screen_nonlinear = FALSE,
  nonlinear_multiplicity = "holm",
  nonlinear_screening_method = "nuisance_adjusted_score",
  nonlinear_bootstrap_reps = 100L,
  nonlinear_bootstrap_seed = NULL,
  check_assumptions = FALSE,
  run_checks_and_screening = NULL,
  bootstrap_inference = FALSE,
  bootstrap_reps = 100L,
  bootstrap_conf_level = 0.95,
  bootstrap_seed = NULL,
  bootstrap_min_success_fraction = 0.8,
  post_selection_inference = "none",
  prediction_inference = FALSE,
  prediction_level = 0.95,
  prediction_interval = "confidence",
  autocorrelation_check = "All",
  distribution_link_check = "All",
  conditional_independence_check = "All",
  random_effects_normality_check = "All",
  random_effects_predictor_independence_check = "All",
  homogeneity_variance_check = "All",
  verbose = TRUE,
  ...
)

Arguments

Details

The fitted fixed-effect vector is the adjusted coefficient vector used by the final penalized objective. When a fixed-effect penalty is positive, the same selected model is also refitted without fixed-effect penalties to obtain the original coefficient vector. The fit stores both vectors, their differences, and two equations:

g\{E(Y_{it}\mid b_i,u_i)\}=X_{it}\beta^{(0)}+Z_{it}b_i+S_{it}u_i+o_{it}

for the Original Model equation and

g\{E(Y_{it}\mid b_i,u_i)\}=X_{it}\tilde\beta+Z_{it}b_i+S_{it}u_i+o_{it}

for the Final equation, where 'beta^(0)' is the unpenalized reference and 'tilde(beta)' is the adjusted vector from the selected penalized fit.

Score-based nonlinearity screening uses one unpenalized ML–Laplace null fit, nuisance residualization, and a shared primary-cluster maxT multiplier bootstrap. Likelihood-ratio screening rebuilds and refits each candidate design. Assumption diagnostics are evaluated only when requested.

Value

Object of class 'MEMWAS_fit'. It contains adjusted and original fixed-effect coefficients, coefficient adjustments, Original Model and Final equations, covariance parameters, random and serial modes, approximation metadata, inference, diagnostics, and convergence details.

Examples

toy <- data.frame(
  id = rep(seq_len(6), each = 3),
  time = rep(0:2, times = 6),
  x = rep(c(-1, 0, 1), times = 6)
)
toy$y <- 1 + 0.6 * toy$x +
  rep(c(-0.15, -0.08, 0, 0.05, 0.10, 0.18), each = 3) +
  rep(c(-0.03, 0.04, -0.01), times = 6)
toy_fit <- fit_MEMWAS(
  y ~ x, data = toy, id = "id", time = "time",
  random = NULL, autocor = "NONE", se_method = "none",
  control = list(n_starts = 1L, cold_start_verification = FALSE),
  verbose = FALSE
)
coef(toy_fit)


dat <- data.frame(
  id = rep(seq_len(12), each = 4),
  time = rep(0:3, times = 12),
  x = rep(c(-1, -0.25, 0.25, 1), times = 12)
)
dat$y <- 1 + 0.6 * dat$x + rep(seq(-0.4, 0.4, length.out = 12), each = 4)
fit <- fit_MEMWAS(
  y ~ x, data = dat, id = "id", time = "time", random = ~ 1,
  autocor = "AR(1)", L2_penalty = 0.1, verbose = FALSE
)
coef(fit, type = "both")
summary(fit)

Predict from a fitted MEMWAS model

Description

Computes conditional, zero-latent, population-marginal, or new-cluster predictions from a fitted MEMWAS model or a tuned object with a final refit.

Usage

## S3 method for class 'MEMWAS_fit'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  mode = NULL,
  include_random = is.null(newdata),
  include_serial = is.null(newdata),
  allow_new_levels = TRUE,
  offset = NULL,
  interval = NULL,
  level = NULL,
  se.fit = FALSE,
  trials = NULL,
  ...
)

## S3 method for class 'MEMWAS_family_fit'
predict(object, ...)

## S3 method for class 'tune_MEMWAS'
predict(object, ...)

Arguments

Value

A numeric prediction vector when neither an interval nor standard errors are requested. Otherwise, a data frame containing 'fit', 'se.fit', 'lower', and 'upper', with additional predictive-variance components when applicable.

Rank temporal autocorrelation structures

Description

Fits and ranks candidate temporal covariance specifications by primary-ID grouped cross-validation, AIC, BIC, or reported log-likelihood. Candidate specifications may combine an outcome-loaded residual process with separate predictor-loaded residual processes. All viable candidates are evaluated on the same complete-case rows. Model approximation, covariance construction, likelihood evaluation, and optimization are performed by MEMWAS's registered C++ backend; grouped fold assignment and validation metrics are also native.

Usage

rank_autocorrelation_structures(
  formula,
  family = "gaussian",
  data,
  id,
  time,
  random = ~1,
  candidates = c("NONE", "AR(1)", "OU", "CS"),
  criterion = "grouped_cv",
  K = 5L,
  metric = "RMSE",
  cv_aggregate = c("median", "mean"),
  cv_prediction_mode = c("population_marginal_mean", "zero_random_effect"),
  fold_accept = c("operational", "strict"),
  random_cov = "diagonal",
  control = list(),
  method = "ML",
  approximation = "laplace",
  init_approximation = "variational_inference",
  quadrature_points = 7L,
  link = NULL,
  offset = NULL,
  weights = NULL,
  subset = NULL,
  na.action = NULL,
  theta = NULL,
  shape = NULL,
  L1_penalty = 0,
  L2_penalty = 0,
  mape_epsilon = 1e-08,
  seed = NULL,
  refit_best = TRUE,
  refit_se_method = c("hessian", "none"),
  keep_fits = FALSE,
  keep_fold_fits = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'MEMWAS_autocorrelation_ranking'
print(x, ..., digits = 4L)

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

## S3 method for class 'summary.MEMWAS_autocorrelation_ranking'
print(x, ..., digits = 4L)

Arguments

Details

For subject 'i', the native conditional covariance may contain

V_i=K_{0i}(\theta_0)+\sum_j \operatorname{diag}(x_{ij})K_{ji}(\theta_j) \operatorname{diag}(x_{ij}),

where 'K0' is an optional outcome-loaded residual process and each 'Kj' is an independently parameterized predictor-loaded residual process. This models residual temporal heterogeneity associated with observed predictor loadings; it does not fit a joint stochastic time-series model for the predictors.

AIC, BIC, and log-likelihood are accepted only when the fit reports an available likelihood criterion. AIC/BIC candidates use the package's effective degrees of freedom when penalties are active. Grouped CV uses one native fold assignment for every candidate and requires every fold to fit, predict, and return a finite metric.

Value

Object of class 'MEMWAS_autocorrelation_ranking'. It contains a sorted 'ranking' table, fold-level results, common analysis-row indices, the selected candidate, an optional selected refit, and optionally retained criterion/fold fits.

'print()' returns 'x' invisibly. 'summary()' returns an object of class 'summary.MEMWAS_autocorrelation_ranking' containing ranking and fold-failure summaries.

Examples

toy <- data.frame(
  id = rep(seq_len(6), each = 3),
  time = rep(0:2, times = 6),
  x = rep(c(-1, 0, 1), times = 6)
)
toy$y <- 1 + 0.6 * toy$x +
  rep(c(-0.15, -0.08, 0, 0.05, 0.10, 0.18), each = 3) +
  rep(c(-0.03, 0.04, -0.01), times = 6)
toy_rank <- rank_autocorrelation_structures(
  y ~ x, data = toy, id = "id", time = "time", random = NULL,
  candidates = c(independent = "NONE", ar1 = "AR(1)"),
  criterion = "AIC", refit_best = FALSE,
  control = list(n_starts = 1L, cold_start_verification = FALSE),
  verbose = FALSE
)
toy_rank$ranking


dat <- data.frame(
  id = rep(seq_len(20), each = 5),
  time = rep(0:4, times = 20),
  x1 = stats::rnorm(100), x2 = stats::rnorm(100)
)
dat$y <- 0.5 + 0.6 * dat$x1 - 0.3 * dat$x2 + stats::rnorm(100)

rank_autocorrelation_structures(
  y ~ x1 + x2, data = dat, id = "id", time = "time",
  random = NULL,
  candidates = list(
    independent = "NONE",
    residual_ar1 = list(residual_autocor = "AR(1)"),
    predictor_specific = list(
      residual_autocor = "AR(1)",
      predictor_autocor = c(x1 = "OU", x2 = "AR(1)")
    )
  ),
  criterion = "grouped_cv", K = 5, verbose = FALSE
)

Screen MEMWAS fixed effects for nonlinearity

Description

Evaluates restricted-cubic-spline additions to an unpenalized ML–Laplace null model. The function can be called independently of 'fit_MEMWAS()' so that screening is performed only when explicitly requested.

Usage

screen_MEMWAS_nonlinearity(
  object,
  nonlinear_predictors = NULL,
  nonlinear_alpha = 0.05,
  spline_knots = 4L,
  spline_probs = NULL,
  nonlinear_screening_method = "nuisance_adjusted_score",
  nonlinear_multiplicity = "holm",
  nonlinear_bootstrap_reps = 499L,
  nonlinear_bootstrap_seed = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'MEMWAS_nonlinearity_screen'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

Details

The score method forms candidate spline blocks from retained analysis rows, residualizes them against the null fixed-effect design using model-based Fisher weights, and calibrates all candidate statistics with one shared maxT distribution. Its single-cluster bootstrap requires every random-effect grouping factor to be nested within the primary ID. For crossed grouping structures, use '"likelihood_ratio"'.

The likelihood-ratio method reconstructs the fixed-effect design for every augmented formula and refits each nested candidate under unpenalized ML–Laplace estimation. Binomial trial totals, offsets, subsets, and missing- data handling retained by the null fit are reused during reconstruction.

Value

Object of class 'MEMWAS_nonlinearity_screen'. It contains the complete screening table, selected predictors, knot and basis definitions, candidate names, selected formula, selected data, call, and method settings.

The input object, invisibly.

Examples

toy <- data.frame(
  id = rep(seq_len(6), each = 3),
  time = rep(0:2, times = 6),
  x = rep(c(-1, 0, 1), times = 6)
)
toy$y <- 1 + 0.6 * toy$x +
  rep(c(-0.15, -0.08, 0, 0.05, 0.10, 0.18), each = 3) +
  rep(c(-0.03, 0.04, -0.01), times = 6)
intercept_fit <- fit_MEMWAS(
  y ~ 1, data = toy, id = "id", time = "time",
  random = NULL, autocor = "NONE", se_method = "none",
  control = list(n_starts = 1L, cold_start_verification = FALSE),
  verbose = FALSE
)
empty_screen <- screen_MEMWAS_nonlinearity(intercept_fit, verbose = FALSE)
empty_screen$summary


dat <- data.frame(
  id = rep(seq_len(20), each = 5),
  time = rep(0:4, times = 20),
  x = rep(seq(-1, 1, length.out = 5), times = 20)
)
dat$y <- 0.4 + 1.2 * dat$x^2 + stats::rnorm(nrow(dat), sd = 0.2)
baseline <- fit_MEMWAS(
  y ~ x, data = dat, id = "id", time = "time", random = NULL,
  autocor = "NONE", se_method = "none", verbose = FALSE
)
screen <- screen_MEMWAS_nonlinearity(
  baseline, nonlinear_predictors = "x",
  nonlinear_bootstrap_reps = 99L, nonlinear_bootstrap_seed = 11L,
  verbose = FALSE
)
print(screen)

Define a MEMWAS serial covariance component

Description

Describes one latent serial coefficient process for use in 'fit_MEMWAS()' or 'tune_MEMWAS()'. Components may load the outcome uniformly or load one numeric predictor/design column.

Usage

serial_component(
  structure = "AR(1)",
  predictor = NULL,
  design = NULL,
  order = NULL,
  name = NULL,
  control = list()
)

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

Arguments

Value

Object of class 'MEMWAS_serial_component' containing the normalized structure, loading specification, order, component name, and controls.

Examples

ar1 <- serial_component("AR(1)", name = "within_subject")
exp_ou <- serial_component(
  "expOU", predictor = "exposure", name = "exposure_persistence"
)
free_lag <- serial_component("TOEP", order = 4, name = "free_lag")
list(ar1 = ar1, exp_ou = exp_ou, free_lag = free_lag)