Package {psychnets}

psychnets: Clean-Room Base-R Psychometric Network Estimation

Description

Estimates cross-sectional psychometric network models in pure base R, with no compiled dependencies and dependency-free correctness certificates. See psychnet() for the unified entry point and ebic_glasso(), cor_network(), pcor_network(), ising_fit(), and mgm_fit() for the individual estimators.

Certification

Regularized estimators are graded against their own convex objective rather than an external solver. For the Gaussian graphical model, glasso_kkt() returns the stationarity (KKT) residual: zero certifies the unique global optimum. Every ebic_glasso() result carries this value in ⁠$kkt⁠.

Author(s)

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

Authors:

• Sonsoles López-Pernas

See Also

Useful links:

• https://pak.dynasite.org/psychnets

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

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

Back-compatible field access for a psychnet object

Description

The canonical (str-visible) fields are the lean netobject set; this method adds virtual aliases so older/external accessors keep working without storing redundant fields.

Usage

## S3 method for class 'psychnet'
x$name

Arguments

Value

The requested field, or NULL if neither a canonical field nor a known alias.

Examples

fit <- pcor_network(SRL_GPT)
fit$method
fit$labels

Self-regulated-learning (MSLQ) construct scores simulated by large language models

Description

Five data sets of Motivated Strategies for Learning Questionnaire (MSLQ) construct scores, each a random sample of 300 cases generated by one large language model in the study "Delving into the psychology of Machines: Exploring the structure of self-regulated learning via LLM-generated survey responses" (Computers in Human Behavior, 2025). One data set per model: SRL_GPT, SRL_Gemini, SRL_Claude, SRL_Mistral, and SRL_LLaMa.

Usage

SRL_GPT

SRL_Gemini

SRL_Claude

SRL_Mistral

SRL_LLaMa

Format

Each is a data frame with 300 rows and 5 variables – the MSLQ construct scores (each the mean of that construct's Likert items, range 1–7):

CSU

cognitive strategy use

IV

intrinsic value

SE

self-efficacy

SR

self-regulation

TA

test anxiety

Details

Only the five models whose responses carry estimable structure are included. Two other generators from the original study (ChatGPT and LeChat) produced near-independent items (mean absolute inter-item correlation \approx 0.03), so their network is the empty graph; they are omitted. The five retained models span the spectrum the paper describes, from realistically structured (SRL_GPT) to strongly "over-coherent" (SRL_Gemini, SRL_Claude).

Source

Saqr, M. (2025). Delving into the psychology of Machines: Exploring the structure of self-regulated learning via LLM-generated survey responses. Computers in Human Behavior, 173, 108769. doi:10.1016/j.chb.2025.108769

Examples

# partial-correlation network of the five constructs for one model
net <- ebic_glasso(SRL_GPT)
net
net_centralities(net)

Tidy edge list for a psychnet network

Description

Tidy edge list for a psychnet network

Usage

## S3 method for class 'psychnet'
as.data.frame(x, row.names = NULL, optional = FALSE, ..., include_zero = FALSE)

Arguments

Value

A one-row-per-edge data.frame with columns from, to, weight.

Examples

fit <- pcor_network(SRL_GPT)
as.data.frame(fit)
as.data.frame(fit, include_zero = TRUE)

Tidy a network bootstrap

Description

Tidy a network bootstrap

Usage

## S3 method for class 'psychnet_bootstrap'
as.data.frame(x, row.names = NULL, optional = FALSE, ..., significant = FALSE)

Arguments

Value

The tidy ⁠$edges⁠ data frame (one row per edge, with its percentile interval, inclusion proportion, and significant flag).

Edge-weight stability coefficient (case-dropping subset bootstrap)

Description

The edge-vector complement to net_stability(). For each drop proportion the network is re-estimated on random case-dropped subsets and the subset edge-weight vector is compared with the full-sample one. The edge-weight CS-coefficient is the largest drop proportion at which the edge-vector correlation stays ⁠>= threshold⁠ with probability ⁠>= certainty⁠ (Epskamp, Borsboom & Fried 2018).

Usage

casedrop_reliability(
  data,
  method = "glasso",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  iter = 100L,
  threshold = 0.7,
  certainty = 0.95,
  cor_method = c("spearman", "pearson", "kendall"),
  labels = NULL,
  ...
)

Arguments

Value

A tidy data.frame (class psychnet_casedrop), one row per metric per drop proportion, with columns metric, drop_prop, mean, sd. The edge-weight CS-coefficient is carried in attr(x, "cs") and shown when the result is printed. Visualise it with plot.psychnet_casedrop().

References

Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy. Behavior Research Methods, 50(1), 195-212.

Examples

# `iter` and `drop_prop` are kept small here so the example runs quickly;
# the defaults (iter = 100, drop_prop = seq(0.1, 0.9, 0.1)) are what a real
# reliability assessment should use.
casedrop_reliability(SRL_Claude, iter = 5, drop_prop = c(0.25, 0.5))

Correctness certificate of a fitted network

Description

Every regularized or constrained estimator in psychnet self-certifies: it reports how far the returned network sits from the unique optimum of its own convex objective (a KKT / stationarity residual), or – for the structural methods – whether the graph satisfies the identity that defines it. This verb returns that certificate as a tidy one-row data.frame, so correctness is read the same way for every method.

Usage

certificate(x, tol = 1e-06)

Arguments

Details

The residual is near machine zero for a correctly solved problem. cor and pcor have no optimization to certify and report NA.

Value

A one-row data.frame with columns method, certificate (the residual; smaller is better), kind ("kkt" for the optimization certificates, "structural" for TMFG/relimp, "none" for cor/pcor), and certified (logical: residual at or below tol).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
certificate(ebic_glasso(cor_matrix = S, n = 250))
certificate(tmfg_network(cor_matrix = S))

Condition a moderated network at a moderator value

Description

Extracts the effective pairwise network implied by a moderated MGM (mgm_fit() with moderators) at a given value of the moderator, mirroring mgm::condition(): it applies the AND-rule pre-filter, absorbs the moderator value into the main-effect coefficients, and re-aggregates the pairwise edges.

Usage

condition(object, value, rule = NULL)

Arguments

Value

A psychnet network object (the moderator node carries no edges).

Examples

set.seed(1)
x1 <- stats::rnorm(400); x2 <- stats::rnorm(400)
mod <- rep(0:1, each = 200)
y <- x1 * (mod == 1) + stats::rnorm(400)   # x1-y edge only when mod == 1
d <- data.frame(x1 = x1, x2 = x2, y = y, mod = mod)
fit <- mgm_fit(d, types = c("g", "g", "g", "c"), moderators = 4)
condition(fit, value = 0)   # no x1-y edge
condition(fit, value = 1)   # the x1-y edge appears

Automatic correlation matrix (polychoric / polyserial / Pearson)

Description

Detects ordinal variables (integer-valued with at most ordinal_max_levels levels) and returns the correlation matrix using a polychoric correlation for ordinal-ordinal pairs, a polyserial correlation for ordinal-continuous pairs, and Pearson otherwise, projected to the nearest positive-definite matrix. The base-R counterpart of qgraph::cor_auto(); this is the correlation bootnet/qgraph use by default for Likert data.

Usage

cor_auto(data, ordinal_max_levels = 7L, na_method = c("pairwise", "listwise"))

Arguments

Value

A correlation matrix with the variable names as dimnames.

Examples

set.seed(1)
z <- matrix(stats::rnorm(300 * 4), 300, 4) %*% chol(0.5^abs(outer(1:4, 1:4, "-")))
x <- apply(z, 2, function(col) as.integer(cut(col, 5)))   # 5-level Likert
cor_auto(x)

Correlation network

Description

Marginal (zero-order) association network: the Pearson correlation matrix with the diagonal removed. Equivalent to bootnet's "cor" default.

Usage

cor_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  threshold = 0,
  alpha = NULL,
  adjust = "none",
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the thresholded correlation matrix, with ⁠$cor_matrix⁠, ⁠$n_eff⁠, ⁠$na_method⁠ (and ⁠$p_values⁠ when alpha is used).

Examples

x <- matrix(stats::rnorm(200 * 4), 200, 4)
cor_network(x)
cor_network(x, alpha = 0.05, adjust = "BH")

Dichotomize numeric columns to 0/1

Description

Splits each column of a numeric matrix or data frame into a binary 0/1 variable. This is the usual preprocessing step before fitting an Ising network (ising_fit(), ising_sampler()) to Likert or other ordinal/continuous data, which require binary input.

Usage

dichotomize(data, method = c("median", "mean", "rank"))

Arguments

Value

An integer matrix of 0/1 values with the same dimensions and dimnames as data.

Examples

b <- dichotomize(SRL_GPT, method = "median")
table(b)                          # values are 0/1 only

Bootstrapped difference test for edges or centralities

Description

Tests, within a single network, whether two edge weights or two node centralities differ. For every pair it forms the per-resample difference from the stored bootstrap draws, takes the percentile interval of that difference, and flags the pair significant when the interval excludes zero; it also reports the two-sided bootstrap p-value (Epskamp, Borsboom & Fried 2018). This is the within-network counterpart to the edge accuracy intervals reported by net_boot().

Usage

difference_test(boot, type = "edge", ci = NULL, p_adjust = "none")

Arguments

Value

A tidy data frame, one row per pair, with item1, item2, the two observed values, their observed difference, the percentile interval of the bootstrap difference (lower, upper), the two-sided p_value, and a logical significant.

Examples

set.seed(1)
x <- matrix(stats::rnorm(150 * 5), 150, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
bs <- net_boot(x, n_boot = 50, cores = 1)   # n_boot >= 1000 for real use
difference_test(bs, type = "strength")

EBIC-regularized Gaussian graphical model (graphical lasso)

Description

Selects an L1 penalty by the extended BIC (Foygel & Drton 2010) over a log-spaced path, then refits the chosen penalty to machine precision so the returned network is the certified global optimum of the convex objective. Equivalent in purpose to qgraph::EBICglasso() / bootnet's "EBICglasso" default, but pure base R and self-certified (see glasso_kkt()).

Usage

ebic_glasso(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  gamma = 0.5,
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  threshold = 0,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the partial-correlation matrix, with ⁠$precision⁠, ⁠$lambda⁠, ⁠$gamma⁠, ⁠$cor_matrix⁠, ⁠$ebic⁠, ⁠$native⁠, and ⁠$kkt⁠ (the stationarity residual of the returned network).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 250)
fit
as.data.frame(fit)

Action frequencies from an event log

Description

Converts a long event log – one row per event, with an actor, an action, and optionally a session and time – into a frequency table: one row per occasion, one column per distinct action, holding that action's count. The conversion mirrors the "frequency" format of the Nestimate event pipeline. An occasion is one (actor, session) cell; with compute_sessions = TRUE the time column is used to split each actor into sessions wherever the gap between consecutive events exceeds time_threshold. This is the frequency input that psychnet() builds internally for event data (source = "eventdata").

Usage

event_frequencies(
  data,
  actor = "Actor",
  action = "Action",
  session = NULL,
  time = NULL,
  compute_sessions = TRUE,
  time_threshold = 900
)

Arguments

Value

A data.frame with an actor column, a session index, and one integer count column per action (one row per occasion).

Examples

ev <- data.frame(
  Actor   = rep(c("a", "b"), each = 6),
  Session = rep(rep(1:2, each = 3), 2),
  Action  = c("read","quiz","read", "quiz","read","note",
              "note","note","read", "read","quiz","quiz"))
event_frequencies(ev, session = "Session")

Stepwise Gaussian graphical model selection (ggmModSelect)

Description

Selects a GGM by extended-BIC model search over edge sets generated from the glasso path, refitting the unregularized maximum-likelihood precision on each candidate graph, with an optional stepwise add/drop search. Unlike the graphical lasso, retained edges are not shrunk. Equivalent in purpose to qgraph::ggmModSelect(), but pure base R and self-certified via ggm_support_kkt().

Usage

ggm_modselect(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  gamma = 0,
  stepwise = TRUE,
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  threshold = 0,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the partial-correlation matrix, with ⁠$precision⁠, ⁠$support⁠ (the selected graph), ⁠$gamma⁠, ⁠$ebic⁠, ⁠$cor_matrix⁠, and ⁠$kkt⁠.

Examples

S <- 0.5^abs(outer(1:6, 1:6, "-"))
ggm_modselect(cor_matrix = S, n = 250)

Constrained Gaussian-MRF (graph-restricted MLE) stationarity residual

Description

Certificate for an unregularized Gaussian graphical model whose precision is constrained to a fixed graph (the estimator behind ggm_modselect() and logo_network()). The maximum-likelihood / maximum-entropy conditions for a Gaussian Markov random field on a graph G are exact: W_{ij} = S_{ij} for every (i,j) on the graph and on the diagonal (W = \Theta^{-1}), and \Theta_{ij} = 0 for every (i,j) not on the graph. A near-zero return certifies the constrained optimum with no reference solver.

Usage

ggm_support_kkt(theta, cor_matrix, support, active_tol = 1e-08)

Arguments

Value

Maximum absolute stationarity violation (scalar); 0 = exact optimum.

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
fit <- ggm_modselect(cor_matrix = S, n = 250)
ggm_support_kkt(fit$precision, S, fit$support)

Graphical-lasso stationarity (KKT) residual

Description

A dependency-free correctness certificate for a fitted Gaussian graphical model. For the convex objective

\min_{\Theta \succ 0} -\log\det\Theta + \mathrm{tr}(S\Theta) + \rho \sum_{i \neq j} |\Theta_{ij}|

(off-diagonal penalty), let W = \Theta^{-1}. The subgradient optimality conditions are W_{ii} = S_{ii}; W_{ij} - S_{ij} = \rho\,\mathrm{sign}(\Theta_{ij}) where \Theta_{ij} \neq 0; and |W_{ij} - S_{ij}| \le \rho otherwise. By strict convexity, a precision matrix with zero violation is the unique global optimum, so a near-zero return certifies correctness independently of any reference solver.

Usage

glasso_kkt(theta, cor_matrix, rho, active_tol = 1e-08)

Arguments

Value

Maximum absolute stationarity violation (scalar); 0 = exact optimum.

Examples

S <- 0.5^abs(outer(1:5, 1:5, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 200)
glasso_kkt(fit$precision, S, fit$lambda)

Stationarity (KKT) residual of an L1-penalized GLM fit

Description

Dependency-free correctness certificate for a nodewise lasso, analogous to glasso_kkt() for the graphical lasso. With standardized predictors X and fitted mean mu (identity link for gaussian, logistic for binomial), the subgradient conditions are n^{-1} X_j^\top (y - \mu) = \lambda\, \mathrm{sign}(\beta_j) for active coordinates and |n^{-1} X_j^\top (y - \mu)| \le \lambda otherwise. Near-zero certifies the penalized-likelihood optimum.

Usage

glm_lasso_kkt(
  X,
  y,
  b0,
  beta,
  lambda,
  family = "gaussian",
  weights = NULL,
  active_tol = 1e-08
)

Arguments

Value

Maximum absolute stationarity violation (scalar). Near-zero certifies the fit is at the penalized-likelihood optimum.

Examples

set.seed(1)
x <- scale(matrix(stats::rnorm(200 * 3), 200, 3))
y <- as.numeric(x %*% c(0.5, 0, -0.3) + stats::rnorm(200))
fit <- stats::lm.fit(cbind(1, x), y)
glm_lasso_kkt(x, y, fit$coefficients[1], fit$coefficients[-1], lambda = 0)

Nonparanormal graphical model (huge)

Description

Estimates a Gaussian graphical model after a rank-based nonparanormal transform that relaxes the multivariate-normal assumption, then selects the L1 penalty by EBIC and refits to the certified optimum. Equivalent in purpose to huge::huge() (nonparanormal) / bootnet's "huge" default, but pure base R and self-certified via glasso_kkt() on the transformed correlation.

Usage

huge_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  npn = c("shrinkage", "truncation", "skeptic"),
  gamma = 0.5,
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  threshold = 0,
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the partial-correlation matrix, with ⁠$precision⁠, ⁠$lambda⁠, ⁠$gamma⁠, ⁠$cor_matrix⁠ (the transformed correlation), ⁠$npn⁠, ⁠$ebic⁠, and ⁠$kkt⁠.

Examples

set.seed(1)
x <- matrix(stats::rnorm(300 * 5), 300, 5)
x <- exp(x %*% chol(0.4^abs(outer(1:5, 1:5, "-"))))   # break normality
huge_network(x)

Ising network for binary data

Description

Estimates an Ising model by nodewise L1-penalized logistic regression with EBIC selection, combined by the AND (default) or OR rule. Equivalent in purpose to IsingFit::IsingFit(), but pure base R and self-certified: each node's regression reports its stationarity (KKT) residual (see glm_lasso_kkt()).

Usage

ising_fit(
  data,
  gamma = 0.25,
  rule = c("AND", "OR"),
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  min_sum = NULL,
  weights = NULL,
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the symmetric weight matrix, with ⁠$thresholds⁠ (node intercepts) and ⁠$kkt⁠ (the worst nodewise stationarity residual).

Examples

set.seed(1)
z <- matrix(stats::rnorm(400 * 2), 400, 2)
x <- cbind(z[, 1], z[, 1], z[, 2], z[, 2]) + matrix(stats::rnorm(400 * 4), 400)
b <- (x > 0) * 1L
colnames(b) <- paste0("V", 1:4)
ising_fit(b)

Unregularized Ising network for binary data

Description

Estimates an Ising model by unpenalized nodewise logistic regression, with optional Wald p-value edge pruning, combined by the AND (default) or OR rule. The unregularized counterpart of ising_fit(); self-certified by the maximum-likelihood score residual (see glm_lasso_kkt() at lambda = 0).

Usage

ising_sampler(
  data,
  rule = c("AND", "OR"),
  alpha = NULL,
  adjust = "none",
  min_sum = NULL,
  weights = NULL,
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the symmetric weight matrix, with ⁠$thresholds⁠ (node intercepts), ⁠$rule⁠, ⁠$p_values⁠, ⁠$nodewise⁠ (for net_predict()), and ⁠$kkt⁠ (worst nodewise score residual).

Examples

set.seed(1)
z <- matrix(stats::rnorm(500 * 2), 500, 2)
x <- cbind(z[, 1], z[, 1], z[, 2], z[, 2]) + matrix(stats::rnorm(500 * 4), 500)
b <- (x > 0) * 1L
colnames(b) <- paste0("V", 1:4)
ising_sampler(b)

Relative-importance (LMG / Shapley) certificate

Description

By Shapley efficiency, the importance shares a node receives from its predictors sum exactly to that node's full-model R-squared. Returns the maximum absolute deviation from that identity; near zero certifies the decomposition.

Usage

lmg_certificate(x)

Arguments

Value

Maximum absolute deviation of incoming-share sums from the per-node R-squared (scalar); 0 = exact decomposition.

Examples

S <- 0.4^abs(outer(1:5, 1:5, "-"))
lmg_certificate(relimp_network(cor_matrix = S))

Local-Global sparse inverse covariance (LoGo)

Description

Estimates a sparse Gaussian graphical model whose conditional-independence structure is the chordal TMFG: the precision is the closed-form Gaussian Markov random field on that graph (Barfuss et al. 2016). Equivalent in purpose to NetworkToolbox::LoGo() / bootnet's "LoGo" default, pure base R and self-certified via ggm_support_kkt() (the precision reproduces S exactly on the TMFG support).

Usage

logo_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  threshold = 0,
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the partial-correlation matrix, with ⁠$precision⁠, ⁠$support⁠ (the TMFG graph), ⁠$cor_matrix⁠, and ⁠$kkt⁠.

Examples

set.seed(1)
x <- matrix(stats::rnorm(300 * 6), 300, 6)
logo_network(x)

Mixed graphical model

Description

Estimates a mixed graphical model by nodewise L1-penalized regression – a gaussian (linear) lasso for continuous nodes and a logistic lasso for binary nodes – with per-node EBIC selection, combined by the AND rule. Equivalent in purpose to mgm::mgm(), but pure base R and self-certified: each node's regression reports its stationarity (KKT) residual (see glm_lasso_kkt()).

Usage

mgm_fit(
  data,
  gamma = 0.25,
  types = NULL,
  nlambda = 100L,
  lambda_min_ratio = 0.01,
  threshold = c("LW", "HW", "none"),
  rule = c("AND", "OR"),
  moderators = NULL,
  weights = NULL,
  na_method = c("pairwise", "listwise"),
  native = TRUE,
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the symmetric standardized weight matrix, with ⁠$types⁠ and ⁠$kkt⁠ (the worst nodewise residual). A binary-binary edge carries the sign of its nodewise-logistic coefficient; mgm::mgm() reports the same edge as a magnitude only (its sign is undefined for a categorical-categorical interaction), so compare such edges on abs(). Continuous columns are standardized internally, binary predictors enter the graph on their 0/1 dummy scale, and binary-response logit coefficients are converted to mgm's two-class multinomial scale before edge aggregation. With these conventions the edge magnitudes match mgm::mgm closely for gaussian-gaussian, gaussian-binary, and binary-binary edges alike; weak edges near the EBIC/threshold boundary can still differ in support because the penalty is selected on an independent base-R path.

Examples

set.seed(1)
f <- stats::rnorm(400)
g1 <- f + stats::rnorm(400); g2 <- f + stats::rnorm(400)
b1 <- (f + stats::rnorm(400) > 0) * 1L
d <- data.frame(g1 = g1, g2 = g2, b1 = b1, n = stats::rnorm(400))
mgm_fit(d)

Aggregate a network's communities into super-nodes

Description

Collapses each community of items into a single super-node. score methods build a composite column per community from the raw data and return a reduced data.frame (re-estimate the macro network with psychnet() on it). assoc methods summarise each community pair's multivariate association directly and return the macro network as a psychnet.

Usage

net_aggregate(
  data,
  communities,
  method = "mean",
  estimator = "glasso",
  scale = TRUE,
  labels = NULL,
  ...
)

Arguments

Value

For a score method, a data.frame with one column per community (one row per observation). For an association method, a psychnet macro network among communities.

Examples

net_aggregate(SRL_Claude, communities = c(1, 1, 2, 2, 2))            # reduced data
net_aggregate(SRL_Claude, communities = c(1, 1, 2, 2, 2), method = "rv")  # macro net

Bootstrap a psychometric network

Description

Resamples observations with replacement, re-estimates the network on each resample, and summarizes the sampling distribution of every edge weight and node centrality (mean, percentile confidence interval, and edge inclusion proportion). An edge is flagged significant when its percentile interval excludes zero. The raw per-resample draws are stored on the returned object for use by difference_test().

Usage

net_boot(
  data,
  method = "glasso",
  n_boot = 1000L,
  ci = 0.95,
  measures = c("strength", "expected_influence"),
  centrality_fn = NULL,
  predictability = FALSE,
  threshold = FALSE,
  diff_test = FALSE,
  p_adjust = "none",
  labels = NULL,
  cores = NULL,
  engine = NULL,
  ...
)

Arguments

Value

An object of class psychnet_bootstrap: tidy ⁠$edges⁠ (with a significant flag) and ⁠$centrality⁠ data frames, the observed network in ⁠$observed⁠, raw resample draws in ⁠$edge_boot⁠, ⁠$str_boot⁠, ⁠$ei_boot⁠, and the general ⁠$centrality_boot⁠ (named list, one matrix per measure). Optional ⁠$predictability⁠, ⁠$thresholded⁠, ⁠$edge_diff_p⁠, ⁠$centrality_diff_p⁠, plus ⁠$lambda_path⁠/⁠$lambda_selected⁠ when the estimator reports them.

Examples

set.seed(1)
x <- matrix(stats::rnorm(150 * 5), 150, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
bs <- net_boot(x, n_boot = 50, cores = 1)
as.data.frame(bs)

Bridge centrality

Description

Computes bridge centrality for an undirected weighted network: how strongly each node connects to communities other than its own. You supply the community membership; psychnets does not detect it.

Usage

net_bridge(x, communities, normalize = FALSE, labels = NULL)

Arguments

Value

A tidy data.frame (class psychnet_bridge), one row per node, with columns node, community, bridge_strength, bridge_betweenness, bridge_closeness, bridge_ei1, bridge_ei2. Visualise with plot.psychnet_bridge().

References

Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge centrality. Multivariate Behavioral Research, 56(2), 353-367.

Examples

S <- 0.3^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 400)
net_bridge(fit, communities = c(1, 1, 1, 2, 2, 2))

Node centrality

Description

Node centrality

Usage

net_centralities(
  x,
  measures = c("strength", "expected_influence"),
  centrality_fn = NULL,
  ...
)

Arguments

Value

A tidy data.frame, one row per node, with a node column and one column per requested measure (strength = sum of absolute edge weights, expected_influence = sum of signed edge weights, by default).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
net_centralities(ebic_glasso(cor_matrix = S, n = 250))

Weighted clustering coefficients

Description

Per-node clustering coefficients for a weighted (optionally signed) network: Watts-Strogatz, Zhang-Horvath, Onnela, and Barrat. For signed networks the signed variants are also returned (a closed triangle of three negative or one negative edge lowers the signed coefficient). Ported from qgraph::clustcoef_auto.

Usage

net_clustering(x, labels = NULL)

Arguments

Value

A tidy data.frame (class psychnet_clustering), one row per node: node, clustWS, signed_clustWS, clustZhang, signed_clustZhang, clustOnnela, signed_clustOnnela, clustBarrat.

References

Costantini, G., & Perugini, M. (2014); Watts & Strogatz (1998); Zhang & Horvath (2005); Onnela et al. (2005); Barrat et al. (2004).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
net_clustering(ebic_glasso(cor_matrix = S, n = 400))

Network Comparison Test

Description

Permutation test for whether two groups' Gaussian graphical models differ, on three invariants: global strength (M), maximum edge difference (S), and per-edge differences (E). Networks are EBIC graphical lassos (clean-room pure R). Equivalent in purpose to NetworkComparisonTest::NCT().

Usage

net_compare(
  data1,
  data2 = NULL,
  iter = 1000L,
  gamma = 0.5,
  paired = FALSE,
  abs = TRUE,
  weighted = TRUE,
  p_adjust = "none"
)

Arguments

Value

An object of class psychnet_nct with ⁠$nw1⁠, ⁠$nw2⁠, and ⁠$M⁠, ⁠$S⁠, ⁠$E⁠ (each observed, perm, p_value); ⁠$E⁠ also carries edge_names, a from/to data frame aligned to the per-edge vector.

Examples

set.seed(1)
a <- matrix(stats::rnorm(150 * 5), 150, 5)
b <- matrix(stats::rnorm(150 * 5), 150, 5)
colnames(a) <- colnames(b) <- paste0("V", 1:5)
fit <- net_compare(a, b, iter = 25)
fit

Argument crosswalk: psychnet as a substitute for qgraph / IsingFit / mgm

Description

A tidy, one-row-per-argument map from each reference package's estimator to its psychnet equivalent, so users migrating from qgraph::EBICglasso, qgraph::cor_auto, qgraph::ggmModSelect, IsingFit::IsingFit, or mgm::mgm can find the matching argument and see what psychnet changes by default. Cross-sectional estimators only (temporal models are out of scope).

Usage

net_crosswalk(
  reference = c("all", "EBICglasso", "cor_auto", "ggmModSelect", "IsingFit", "mgm")
)

Arguments

Value

A tidy data.frame, one row per argument, with columns reference (the pkg::fn being substituted), psychnet (the psychnet verb), ref_arg, psychnet_arg ("-" when there is no counterpart), status (identical / renamed / ⁠default differs⁠ / ⁠semantics differ⁠ / ⁠reference only⁠ / ⁠psychnet only⁠), and a short note.

Examples

net_crosswalk("EBICglasso")
net_crosswalk("IsingFit")

Edge betweenness centrality

Description

For each edge, the share of weighted shortest paths (across all node pairs) that pass through it - a high value marks an edge that bridges otherwise distant parts of the network. Geodesics are computed on inverse absolute weights, so strong edges count as short, matching net_centralities()'s node betweenness/closeness.

Usage

net_edge_betweenness(x, invert = TRUE, labels = NULL)

Arguments

Value

A tidy data.frame, one row per edge: from, to, edge_betweenness. Undirected networks give one row per unordered edge.

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
net_edge_betweenness(ebic_glasso(cor_matrix = S, n = 400))

Node predictability

Description

Reports how well each node is predicted by the others in a fitted network. For Gaussian graphical models this is the closed-form variance explained (R-squared) from the precision matrix and needs no data. For the nodewise models (ising_fit(), ising_sampler(), mgm_fit()) it requires the data and reports R-squared for Gaussian nodes and classification accuracy (CC) plus normalized accuracy (nCC) for binary nodes.

Usage

net_predict(x, data = NULL, ...)

Arguments

Value

A tidy data.frame, one row per node, with columns node, type ("gaussian" or "binary"), metric ("R2" or "nCC"), predictability, and accuracy (classification accuracy for binary nodes, NA for Gaussian).

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
net_predict(ebic_glasso(cor_matrix = S, n = 250))

Small-world index

Description

The Humphries & Gurney (2008) small-world index sigma: observed transitivity and average shortest-path length compared with degree-preserving random graphs (sigma = (C/C_rand) / (L/L_rand); sigma > 1 indicates small-worldness). Computed on the binarised network. Ported from qgraph::smallworldness.

Usage

net_smallworld(x, n_rand = 100L, seed = NULL)

Arguments

Value

A tidy one-row data.frame (class psychnet_smallworld): smallworldness, transitivity, aspl, transitivity_rand, aspl_rand.

References

Humphries, M. D., & Gurney, K. (2008). PLoS ONE, 3(4), e0002051.

Examples

S <- 0.4^abs(outer(1:8, 1:8, "-"))
net_smallworld(ebic_glasso(cor_matrix = S, n = 400), n_rand = 50, seed = 1)

Centrality-stability coefficient (case-dropping subset bootstrap)

Description

Centrality-stability coefficient (case-dropping subset bootstrap)

Usage

net_stability(
  data,
  method = "glasso",
  measures = c("strength", "expected_influence"),
  centrality_fn = NULL,
  drop_prop = seq(0.1, 0.9, by = 0.1),
  iter = 100L,
  threshold = 0.7,
  certainty = 0.95,
  labels = NULL,
  ...
)

Arguments

Value

An object of class psychnet_stability with ⁠$cs⁠ (CS-coefficient per measure) and a tidy ⁠$table⁠ (columns measure, drop_prop, mean_cor, sd_cor, prop_above) of the case-dropping correlations by drop proportion. Visualise it with plot.psychnet_stability().

Examples

set.seed(1)
x <- matrix(stats::rnorm(200 * 5), 200, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
cs <- net_stability(x, drop_prop = c(0.3, 0.6), iter = 10)
cs$cs

Split-half reliability of the network edge structure

Description

Repeatedly splits the sample into two halves, estimates a network on each, and compares their edge-weight vectors. Reports, across splits, the edge-weight correlation between halves plus the mean/median/maximum absolute edge deviation - a psychometric reliability view of the estimated structure.

Usage

network_reliability(
  data,
  method = "glasso",
  iter = 100L,
  split = 0.5,
  cor_method = c("pearson", "spearman", "kendall"),
  labels = NULL,
  ...
)

Arguments

Value

A tidy data.frame (class psychnet_reliability), one row per metric with columns metric, mean, sd, lower, upper. The per-split draws are carried in attr(x, "iterations") for plot.psychnet_reliability().

Examples

# `iter` is kept small here so the example runs quickly; the default
# (iter = 100) is what a real reliability assessment should use.
network_reliability(SRL_Claude, iter = 10)

Node predictability as a plotting vector

Description

A thin companion to net_predict() that returns predictability as a plain numeric vector in node order, clamped to ⁠[0, 1]⁠ – the form cograph::splot() expects for pie_values (the predictability ring drawn around each node). Use net_predict() when you want the full tidy table.

Usage

node_predictability(x, data = NULL)

Arguments

Value

A named numeric vector, one value per node (node order), each in ⁠[0, 1]⁠.

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
node_predictability(ebic_glasso(cor_matrix = S, n = 250))

Partial correlation network

Description

Conditional (full-order) association network: each edge is the correlation between two variables with all others partialled out, obtained from the inverse correlation matrix. Equivalent to bootnet's "pcor" default.

Usage

pcor_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  threshold = 0,
  alpha = NULL,
  adjust = "none",
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the thresholded partial-correlation matrix, with ⁠$precision⁠, ⁠$cor_matrix⁠ (and ⁠$p_values⁠ when alpha is used).

Examples

x <- matrix(stats::rnorm(200 * 4), 200, 4)
pcor_network(x)
pcor_network(x, alpha = 0.05, adjust = "holm")

Plot a psychnet network

Description

Renders the estimated network with cograph::splot() (a Suggested package); psychnet objects inherit from cograph_network for exactly this purpose. For the bootstrap / centrality / difference / stability diagnostics use the dedicated plot() methods for those result objects instead.

Usage

## S3 method for class 'psychnet'
plot(x, ...)

Arguments

Value

The value of cograph::splot(), invisibly.

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 300)
if (requireNamespace("cograph", quietly = TRUE)) {
  plot(fit)
}

Plot a network bootstrap

Description

Visualises a net_boot() result. type = "edges" (default) draws the bootstrapped edge-weight confidence intervals sorted by the observed weight (bootnet's edge-accuracy plot); type = "centrality" draws the bootstrapped centrality intervals, one sorted panel per measure; type = "edge_diff" and type = "centrality_diff" draw the bootstrapped difference "significance box" matrix for edges or for one centrality; type = "predictability" draws the node predictability intervals (only when net_boot() was run with predictability = TRUE).

Usage

## S3 method for class 'psychnet_bootstrap'
plot(
  x,
  type = c("edges", "centrality", "edge_diff", "centrality_diff", "predictability"),
  measure = NULL,
  ...
)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

set.seed(1)
x <- matrix(stats::rnorm(150 * 5), 150, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
bs <- net_boot(x, n_boot = 50, cores = 1)   # n_boot >= 1000 for real use
plot(bs)                       # edge-weight CIs
plot(bs, type = "centrality")  # centrality CIs

Plot bridge centrality

Description

One sorted horizontal panel per bridge measure (nodes coloured by community).

Usage

## S3 method for class 'psychnet_bridge'
plot(x, measures = NULL, ...)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

S <- 0.3^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 400)
plot(net_bridge(fit, communities = c(1, 1, 1, 2, 2, 2)))

Plot edge-weight case-dropping stability

Description

Draws the four similarity metrics (correlation, mean/median/max absolute deviation) against the proportion of cases dropped, each with a +/- 1 SD band; the correlation panel carries the acceptance threshold and CS-coefficient.

Usage

## S3 method for class 'psychnet_casedrop'
plot(x, ...)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

# Small `iter` / `drop_prop` for a fast example; see [casedrop_reliability()]
# for the defaults a real assessment should use.
plot(casedrop_reliability(SRL_Claude, iter = 5, drop_prop = c(0.25, 0.5)))

Plot node centralities

Description

Draws the centrality table returned by net_centralities(). type = "bar" gives one sorted horizontal lollipop panel per measure; type = "line" gives the qgraph/bootnet centrality plot — one faceted panel per measure, nodes on a shared vertical axis, a line-and-marker series within each panel.

Usage

## S3 method for class 'psychnet_centrality'
plot(
  x,
  type = c("bar", "line"),
  scale = c("raw", "z", "relative"),
  measures = NULL,
  ...
)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

S <- 0.4^abs(outer(1:6, 1:6, "-"))
fit <- ebic_glasso(cor_matrix = S, n = 300)
plot(net_centralities(fit))
plot(net_centralities(fit), type = "line")

Plot a bootstrapped difference test

Description

Draws the bootnet-style "significance box" matrix for the pairwise difference test returned by difference_test(): items on both axes ordered by their observed value, the diagonal showing the observed value, and each off-diagonal cell filled when that pair differs significantly (red when the row item is larger, blue when smaller). style = "forest" instead draws a forest plot: one row per pair, the bootstrapped difference as a point with its confidence interval, a reference line at zero, and significant pairs (interval excluding zero) emphasised.

Usage

## S3 method for class 'psychnet_difference'
plot(x, style = c("box", "forest"), ...)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

set.seed(1)
x <- matrix(stats::rnorm(150 * 5), 150, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
bs <- net_boot(x, n_boot = 50, cores = 1)   # n_boot >= 1000 for real use
plot(difference_test(bs, type = "strength"))                   # box matrix
plot(difference_test(bs, type = "strength"), style = "forest") # forest plot

Plot a Network Comparison Test

Description

Visualises a net_compare() result. type = "strength" (default) and type = "structure" draw the permutation null distribution for the global strength invariance (M) and the maximum edge-difference (S) statistics, with the observed value and p-value marked; type = "edges" draws the observed per-edge absolute differences, coloured by whether each edge differs significantly.

Usage

## S3 method for class 'psychnet_nct'
plot(x, type = c("strength", "structure", "edges"), alpha = 0.05, ...)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

set.seed(1)
mk <- function(s) { set.seed(s)
  matrix(stats::rnorm(120 * 4), 120, 4) %*% chol(0.3^abs(outer(1:4, 1:4, "-"))) }
cmp <- net_compare(mk(1), mk(2), iter = 25)
plot(cmp)                  # global strength permutation null
plot(cmp, type = "edges")  # per-edge differences

Plot split-half reliability

Description

Histograms of the four between-halves edge metrics across split-half iterations, with each observed mean marked.

Usage

## S3 method for class 'psychnet_reliability'
plot(x, ...)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

# Small `iter` for a fast example; see [network_reliability()] for the
# default a real assessment should use.
plot(network_reliability(SRL_Claude, iter = 10))

Plot centrality stability (case-dropping)

Description

Draws the case-dropping stability curves from net_stability(): mean rank correlation with the full-sample centrality against the proportion of cases dropped, one line per measure, with a +/- 1 SD band, the acceptance threshold, and the CS-coefficient annotated in the legend.

Usage

## S3 method for class 'psychnet_stability'
plot(x, ...)

Arguments

Value

x, invisibly. Called for the plot it draws.

Examples

set.seed(1)
d <- matrix(stats::rnorm(200 * 5), 200, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
s <- net_stability(d, drop_prop = c(0.3, 0.6), iter = 10)
plot(s)

Print a psychnet network

Description

Print a psychnet network

Usage

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

Arguments

Value

x, invisibly.

Examples

fit <- pcor_network(SRL_GPT)
print(fit)

Print a network bootstrap

Description

Print a network bootstrap

Usage

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

Arguments

Value

x, invisibly.

Print an edge-weight stability result

Description

Print an edge-weight stability result

Usage

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

Arguments

Value

x, invisibly.

Per-group psychometric networks

Description

The object returned by psychnet() when group is supplied: a named list of psychnet networks, one per level of the grouping variable. It plots as a grid with cograph::splot() and is consumed per level by the framework verbs (net_centralities(), net_predict(), net_boot(), net_stability(), net_compare()), each returning a ⁠*_group⁠ result.

Usage

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

## S3 method for class 'psychnet_group'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

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

Arguments

Value

x, invisibly (for print).

For as.data.frame, the per-group edge lists stacked with a group column.

For summary, a data frame with one row per group (node/edge counts and mean absolute edge weight).

Print a moderated MGM fit

Description

Print a moderated MGM fit

Usage

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

Arguments

Value

x, invisibly.

Within / between event-data networks

Description

The object returned by psychnet() for nested event data with standardize = FALSE: a pair of Gaussian graphical networks decomposing the actor-by-action frequency covariance into a within-actor and a between-actor part.

Usage

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

## S3 method for class 'psychnet_multilevel'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

x, invisibly (for print).

For as.data.frame, the two edge lists stacked with a level column.

Print a Network Comparison Test

Description

Print a Network Comparison Test

Usage

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

Arguments

Value

x, invisibly.

Print a redundancy result

Description

Print a redundancy result

Usage

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

Arguments

Value

x, invisibly.

Print a split-half reliability result

Description

Print a split-half reliability result

Usage

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

Arguments

Value

x, invisibly.

Per-group framework results

Description

The list returned by a framework verb (net_centralities(), net_predict(), net_boot(), net_stability()) applied to a psychnet_group: one result per group level, keyed by level. as.data.frame() stacks the per-group tables with a leading group column.

Usage

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

## S3 method for class 'psychnet_result_group'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

x, invisibly (for print).

For as.data.frame, the per-group tables stacked with a group column.

Print a centrality-stability result

Description

Print a centrality-stability result

Usage

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

Arguments

Value

x, invisibly.

Estimate a psychometric network

Description

The package's main entry point. Supply data and a method; everything else is optional fine-grained control. psychnet() reads two kinds of input and picks the right one automatically (source = "auto"):

Usage

psychnet(
  data,
  method = c("glasso", "cor", "pcor", "ising", "mgm", "huge", "ggm", "tmfg", "logo",
    "relimp", "ising_sampler"),
  threshold = 0,
  gamma = NULL,
  labels = NULL,
  vars = NULL,
  group = NULL,
  source = c("auto", "data", "eventdata"),
  actor = NULL,
  action = "Action",
  session = NULL,
  time = NULL,
  compute_sessions = TRUE,
  time_threshold = 900,
  id = NULL,
  standardize = TRUE,
  ...
)

Arguments

Details

• a numeric table (source = "data") – the ordinary cross-sectional case: one row per observation, one column per variable;

• a long event log (source = "eventdata") – one row per event, with an actor and an action column (and optionally session / time). The log is converted to action frequencies with event_frequencies() and, when actors contribute several occasions, decomposed into within- and between-actor networks. Passing actor switches this on automatically.

method speaks the package's own short vocabulary – "glasso", "ggm", "tmfg", "logo", "relimp", "ising", "ising_sampler", "huge", "mgm", "cor", "pcor". For interoperability it also accepts the qgraph/bootnet spellings ("EBICglasso", "ggmModSelect", "TMFG", "LoGo", "IsingFit", "IsingSampler"), which resolve to the same estimators. Whichever you pass in, the stored ⁠$method⁠ is the short name.

Value

A psychnet object; a psychnet_group (named list of networks) when group is supplied; or, for nested event data with standardize = FALSE, a psychnet_multilevel object carrying ⁠$within⁠ and ⁠$between⁠ networks.

Examples

x <- matrix(stats::rnorm(200 * 5), 200, 5)
colnames(x) <- c("joy", "fear", "calm", "anger", "trust")
psychnet(x, method = "glasso")
psychnet(x, method = "pcor", vars = joy:anger)     # name range
psychnet(x, method = "glasso", vars = 1:3)         # index range
psychnet(x, method = "EBICglasso")                 # qgraph alias, same result

ev <- data.frame(
  Actor  = rep(paste0("s", 1:30), each = 20),
  Action = sample(c("read", "quiz", "note", "watch"), 600, replace = TRUE))
psychnet(ev, actor = "Actor", action = "Action")   # event data, auto-detected

d <- data.frame(g = rep(c("A", "B"), each = 100),
                matrix(stats::rnorm(200 * 4), 200, 4,
                       dimnames = list(NULL, paste0("V", 1:4))))
psychnet(d, method = "glasso", group = "g")         # one network per level

Detect redundant node pairs ("goldbricker")

Description

Flags pairs of items that behave redundantly in a network: their correlations with all other items are mostly statistically indistinguishable (a small proportion of significantly different correlations) and the two items are themselves strongly correlated. Ported from networktools::goldbricker.

Usage

redundancy(
  data,
  p = 0.05,
  threshold = 0.25,
  cor_min = 0.5,
  cor_method = c("auto", "pearson", "spearman", "kendall")
)

Arguments

Value

A tidy data.frame (class psychnet_redundancy), one row per flagged pair (sorted most-redundant first), with columns item1, item2, proportion (share of significantly different correlations) and correlation. Zero rows when nothing is flagged. The full proportion matrix is in attr(x, "proportion_matrix").

References

Hallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2021). Problems with centrality measures in psychopathology networks. Multivariate Behavioral Research, 56(2), 199-223.

Examples

redundancy(SRL_Claude)

Relative-importance network (LMG / Shapley)

Description

Builds a directed network in which the edge predictor -> outcome is the predictor's LMG (Shapley) share of the outcome node's regression R-squared. Equivalent in purpose to relaimpo::calc.relimp(type = "lmg") applied nodewise / bootnet's "relimp" default, pure base R and self-certified via lmg_certificate().

Usage

relimp_network(
  data = NULL,
  cor_matrix = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  max_nodes = 21L,
  normalized = FALSE,
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the directed importance matrix (weights[k, j] = importance of k for outcome j), with ⁠$r2⁠ (per-node full-model R-squared), ⁠$cor_matrix⁠, and ⁠$kkt⁠ (the decomposition residual). With normalized = FALSE (default) each outcome's incoming shares sum to its R-squared (⁠colSums($weights) == $r2⁠); with normalized = TRUE they are rescaled to sum to 1, ⁠$raw_importance⁠ holds the unscaled shares, and ⁠$kkt⁠ (like lmg_certificate()) is computed from those raw shares.

Examples

S <- 0.4^abs(outer(1:5, 1:5, "-"))
relimp_network(cor_matrix = S)

Summarize a psychnet network

Description

Summarize a psychnet network

Usage

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

Arguments

Value

The tidy edge list (invisibly); prints a summary as a side effect.

Examples

fit <- pcor_network(SRL_GPT)
summary(fit)

Structural certificate for a TMFG network

Description

A TMFG has no convex objective, so its correctness is certified structurally: a valid TMFG on p >= 3 nodes has exactly 3(p - 2) edges, is connected, and is chordal (every cycle of length >= 4 has a chord). Returns a non-negative score that is 0 for a valid TMFG.

Usage

tmfg_certificate(x)

Arguments

Value

Scalar; 0 certifies a valid TMFG (correct edge count, connected, chordal), otherwise a positive integer counting the violated invariants.

Examples

set.seed(1)
x <- matrix(stats::rnorm(200 * 6), 200, 6)
tmfg_certificate(tmfg_network(x))

Triangulated Maximally Filtered Graph (TMFG)

Description

Builds a sparse, planar, chordal association network by greedily retaining the 3(p - 2) most informative edges (Massara et al. 2016). Equivalent in purpose to NetworkToolbox::TMFG() / bootnet's "TMFG" default, pure base R; correctness is certified structurally by tmfg_certificate().

Usage

tmfg_network(
  data = NULL,
  cor_matrix = NULL,
  n = NULL,
  cor_method = c("pearson", "spearman", "kendall", "auto"),
  na_method = c("pairwise", "listwise"),
  labels = NULL
)

Arguments

Value

A psychnet object whose ⁠$weights⁠ is the filtered (signed) correlation matrix on the retained edges, with ⁠$adjacency⁠, ⁠$cliques⁠, ⁠$separators⁠ (the chordal decomposition used by logo_network()), and ⁠$cor_matrix⁠.

Examples

set.seed(1)
x <- matrix(stats::rnorm(200 * 6), 200, 6)
tmfg_network(x)