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Type 'q()' to quit R. > pkgname <- "EGAnet" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('EGAnet') EGAnet (version 1.2.3) For help getting started, type browseVignettes("EGAnet") For bugs and errors, submit an issue to > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("CFA") > ### * CFA > > flush(stderr()); flush(stdout()) > > ### Name: CFA > ### Title: CFA Fit of 'EGA' Structure > ### Aliases: CFA > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA( > ##D data = wmt, > ##D plot.EGA = FALSE # No plot for CRAN checks > ##D ) > ##D > ##D # Fit CFA model to EGA results > ##D cfa.wmt <- CFA( > ##D ega.obj = ega.wmt, estimator = "WLSMV", > ##D plot.CFA = FALSE, # No plot for CRAN checks > ##D data = wmt > ##D ) > ##D > ##D # Additional fit measures > ##D lavaan::fitMeasures(cfa.wmt$fit, fit.measures = "all") > ## End(Not run) > > > > > cleanEx() > nameEx("EBICglasso.qgraph") > ### * EBICglasso.qgraph > > flush(stderr()); flush(stdout()) > > ### Name: EBICglasso.qgraph > ### Title: 'EBICglasso' from 'qgraph' 1.4.4 > ### Aliases: EBICglasso.qgraph > > ### ** Examples > > # Obtain data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Compute graph with tuning = 0 (BIC) > ##D BICgraph <- EBICglasso.qgraph( > ##D data = wmt, gamma = 0 > ##D ) > ##D > ##D # Compute graph with tuning = 0.5 (EBIC) > ##D EBICgraph <- EBICglasso.qgraph( > ##D data = wmt, gamma = 0.5 > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("EGA") > ### * EGA > > flush(stderr()); flush(stdout()) > > ### Name: EGA > ### Title: Applies the Exploratory Graph Analysis technique > ### Aliases: EGA > > ### ** Examples > > # Obtain data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA( > ##D data = wmt, > ##D plot.EGA = FALSE # No plot for CRAN checks > ##D ) > ##D > ##D # Summary statistics > ##D summary(ega.wmt) > ##D > ##D # Produce Methods section > ##D methods.section(ega.wmt) > ##D > ##D # Estimate EGAtmfg > ##D ega.wmt.tmfg <- EGA( > ##D data = wmt, model = "TMFG" > ##D ) > ##D > ##D # Estimate EGA with Louvain algorithm > ##D ega.wmt.louvain <- EGA( > ##D data = wmt, algorithm = "louvain" > ##D ) > ##D > ##D # Estimate EGA with Leiden algorithm > ##D ega.wmt.leiden <- EGA( > ##D data = wmt, algorithm = "leiden" > ##D ) > ##D > ##D # Estimate EGA with Spinglass algorithm > ##D ega.wmt.spinglass <- EGA( > ##D data = wmt, > ##D algorithm = igraph::cluster_spinglass > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("EGA.estimate") > ### * EGA.estimate > > flush(stderr()); flush(stdout()) > > ### Name: EGA.estimate > ### Title: A Sub-routine Function for 'EGA' > ### Aliases: EGA.estimate > > ### ** Examples > > # Obtain data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA.estimate(data = wmt) > ##D > ##D # Estimate EGAtmfg > ##D ega.wmt.tmfg <- EGA.estimate(data = wmt, model = "TMFG") > ##D > ##D # Estimate EGA with Louvain algorithm > ##D ega.wmt.louvain <- EGA.estimate(data = wmt, algorithm = "louvain") > ##D > ##D # Estimate EGA with Spinglass algorithm > ##D ega.wmt.spinglass <- EGA.estimate( > ##D data = wmt, > ##D algorithm = igraph::cluster_spinglass # any {igraph} algorithm > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("EGA.fit") > ### * EGA.fit > > flush(stderr()); flush(stdout()) > > ### Name: EGA.fit > ### Title: 'EGA' Optimal Model Fit using the Total Entropy Fit Index > ### ('tefi') > ### Aliases: EGA.fit > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA( > ##D data = wmt, > ##D plot.EGA = FALSE # No plot for CRAN checks > ##D ) > ##D > ##D # Estimate optimal EGA > ##D fit.wmt <- EGA.fit(data = wmt) > ##D > ##D # Plot optimal fit > ##D plot(fit.wmt$EGA) > ##D > ##D # Estimate CFAs > ##D cfa.ega <- CFA(ega.wmt, estimator = "WLSMV", data = wmt) > ##D cfa.fit <- CFA(fit.wmt$EGA, estimator = "WLSMV", data = wmt) > ##D > ##D # Compare CFAs > ##D lavaan::lavTestLRT( > ##D cfa.ega$fit, cfa.fit$fit, > ##D method = "satorra.bentler.2001" > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("Embed") > ### * Embed > > flush(stderr()); flush(stdout()) > > ### Name: Embed > ### Title: Time-delay Embedding > ### Aliases: Embed > > ### ** Examples > > > # A time series with 8 time points > tseries <- 49:56 > embed.tseries <- Embed(tseries, E = 4, tau = 1) > > > > > > cleanEx() > nameEx("LCT") > ### * LCT > > flush(stderr()); flush(stdout()) > > ### Name: LCT > ### Title: Loadings Comparison Test > ### Aliases: LCT > > ### ** Examples > > > ## Not run: > ##D # Dynamic LCT > ##D LCT(sim.dynEGA[sim.dynEGA$ID == 1,1:24], dynamic = TRUE) > ## End(Not run) > > > > > > cleanEx() > nameEx("TMFG") > ### * TMFG > > flush(stderr()); flush(stdout()) > > ### Name: TMFG > ### Title: Triangulated Maximally Filtered Graph > ### Aliases: TMFG > > ### ** Examples > > # Pearson's correlation only for CRAN checks > A <- TMFG(cor(wmt2[,7:24]))$A > > > > > cleanEx() > nameEx("UVA") > ### * UVA > > flush(stderr()); flush(stdout()) > > ### Name: UVA > ### Title: Unique Variable Analysis > ### Aliases: UVA > > ### ** Examples > > # Select Five Factor Model personality items only > idx <- na.omit(match(gsub("-", "", unlist(psychTools::spi.keys[1:5])), colnames(psychTools::spi))) > items <- psychTools::spi[,idx] > > # Change names in redundancy output to each item's description > key.ind <- match(colnames(items), as.character(psychTools::spi.dictionary$item_id)) > key <- as.character(psychTools::spi.dictionary$item[key.ind]) > > ## Not run: > ##D # Automated selection of local dependence (default) > ##D uva.results <- UVA(data = items, key = key) > ##D > ##D # Produce Methods section > ##D methods.section(uva.results) > ## End(Not run) > > # Manual selection of local dependence > if(interactive()){ + uva.results <- UVA(data = items, key = key, auto = FALSE)} > > > > > cleanEx() > nameEx("boot.ergoInfo") > ### * boot.ergoInfo > > flush(stderr()); flush(stdout()) > > ### Name: boot.ergoInfo > ### Title: Bootstrap Test for the Ergodicity Information Index > ### Aliases: boot.ergoInfo > > ### ** Examples > > # Obtain simulated data > sim.data <- sim.dynEGA > > ## Not run: > ##D # Dynamic EGA individual and population structures > ##D dyn1 <- dynEGA.ind.pop( > ##D data = sim.dynEGA[,-26], n.embed = 5, tau = 1, > ##D delta = 1, id = 25, use.derivatives = 1, > ##D model = "glasso", ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # Empirical Ergodicity Information Index > ##D eii1 <- ergoInfo(dynEGA.object = dyn1, use = "weighted") > ##D > ##D # Bootstrap Test for Ergodicity Information Index > ##D testing.ergoinfo <- boot.ergoInfo( > ##D dynEGA.object = dyn1, EII = eii1, > ##D ncores = 2 > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("boot.wmt") > ### * boot.wmt > > flush(stderr()); flush(stdout()) > > ### Name: boot.wmt > ### Title: 'bootEGA' Results of 'wmt2'Data > ### Aliases: boot.wmt > ### Keywords: datasets > > ### ** Examples > > data("boot.wmt") > > > > > cleanEx() > nameEx("bootEGA") > ### * bootEGA > > flush(stderr()); flush(stdout()) > > ### Name: bootEGA > ### Title: Dimension Stability Analysis of 'EGA' > ### Aliases: bootEGA > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Standard EGA example > ##D boot.wmt <- bootEGA( > ##D data = wmt, iter = 500, > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Produce Methods section > ##D methods.section(boot.wmt) > ##D > ##D # Louvain example > ##D boot.wmt.louvain <- bootEGA( > ##D data = wmt, iter = 500, > ##D algorithm = "louvain", > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Spinglass example > ##D boot.wmt.spinglass <- bootEGA( > ##D data = wmt, iter = 500, > ##D algorithm = igraph::cluster_spinglass, # use any function from {igraph} > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # EGA fit example > ##D boot.wmt.fit <- bootEGA( > ##D data = wmt, iter = 500, > ##D EGA.type = "EGA.fit", > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Hierarchical EGA example > ##D boot.wmt.hier <- bootEGA( > ##D data = wmt, iter = 500, > ##D EGA.type = "hierEGA", > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Random-intercept EGA example > ##D boot.wmt.ri <- bootEGA( > ##D data = wmt, iter = 500, > ##D EGA.type = "riEGA", > ##D type = "parametric", ncores = 2 > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("color_palette_EGA") > ### * color_palette_EGA > > flush(stderr()); flush(stdout()) > > ### Name: color_palette_EGA > ### Title: 'EGA' Color Palettes > ### Aliases: color_palette_EGA > > ### ** Examples > > # Default > color_palette_EGA(name = "polychrome", wc = ega.wmt$wc) [1] "#90DDF0" "#90DDF0" "#90DDF0" "#90DDF0" "#90DDF0" "#F03D2D" "#F03D2D" [8] "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" [15] "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" > > # Blue Ridge Moutains 1 > color_palette_EGA(name = "blue.ridge1", wc = ega.wmt$wc) [1] "#24547E" "#24547E" "#24547E" "#24547E" "#24547E" "#272A39" "#272A39" [8] "#272A39" "#272A39" "#272A39" "#272A39" "#272A39" "#272A39" "#272A39" [15] "#272A39" "#272A39" "#272A39" "#272A39" > > # Custom > color_palette_EGA(name = "#7FD1B9", wc = ega.wmt$wc) [1] "#90DDF0" "#90DDF0" "#90DDF0" "#90DDF0" "#90DDF0" "#F03D2D" "#F03D2D" [8] "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" [15] "#F03D2D" "#F03D2D" "#F03D2D" "#F03D2D" > > > > > cleanEx() > nameEx("compare.EGA.plots") > ### * compare.EGA.plots > > flush(stderr()); flush(stdout()) > > ### Name: compare.EGA.plots > ### Title: Visually Compares 'EGAnet' plots > ### Aliases: compare.EGA.plots > > ### ** Examples > > # Obtain SAPA items > items <- psychTools::spi[,c(11:20)] > > # Draw random samples > sample1 <- items[sample(1:nrow(items), 1000),] > sample2 <- items[sample(1:nrow(items), 1000),] > > ## Not run: > ##D # Estimate EGAs > ##D ega1 <- EGA(sample1) > ##D ega2 <- EGA(sample2) > ##D > ##D # Compare EGAs via plot > ##D compare.EGA.plots( > ##D ega1, ega2, > ##D base.plot = 1, # use "ega1" as base for comparison > ##D labels = c("Sample 1", "Sample 2"), > ##D rows = 1, columns = 2 > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("convert2igraph") > ### * convert2igraph > > flush(stderr()); flush(stdout()) > > ### Name: convert2igraph > ### Title: Convert networks to 'igraph' > ### Aliases: convert2igraph > > ### ** Examples > > convert2igraph(ega.wmt$network) IGRAPH ce74e7a U-W- 18 96 -- + attr: FALSE (v/c), weight (e/n) + edges from ce74e7a: [1] 1-- 2 1-- 3 1-- 4 1-- 5 1--10 1--11 1--16 1--17 2-- 3 2-- 4 [11] 2-- 5 2-- 6 2-- 8 2-- 9 2--10 2--14 3-- 4 3-- 6 3-- 7 3-- 8 [21] 3--10 3--11 3--12 3--13 3--16 3--18 4-- 5 4-- 6 4-- 7 4-- 8 [31] 4-- 9 4--10 4--15 4--17 5-- 6 5-- 7 5-- 8 5--10 5--11 5--12 [41] 5--13 5--16 5--17 6-- 7 6-- 8 6-- 9 6--10 6--11 6--12 6--13 [51] 6--15 6--16 6--17 7-- 8 7--10 7--11 7--13 7--14 7--15 7--17 [61] 8-- 9 8--11 8--12 8--16 8--18 9--10 9--12 9--13 9--14 9--15 [71] 9--16 10--11 10--13 10--14 10--16 10--18 11--12 11--15 11--16 11--17 + ... omitted several edges > > > > > cleanEx() > nameEx("depression") > ### * depression > > flush(stderr()); flush(stdout()) > > ### Name: depression > ### Title: Depression Data > ### Aliases: depression > ### Keywords: datasets > > ### ** Examples > > data("depression") > > > > > cleanEx() > nameEx("dimensionStability") > ### * dimensionStability > > flush(stderr()); flush(stdout()) > > ### Name: dimensionStability > ### Title: Dimension Stability Statistics from 'bootEGA' > ### Aliases: dimensionStability > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate bootstrap EGA > ##D boot.wmt <- bootEGA( > ##D data = wmt, iter = 500, > ##D type = "parametric", ncores = 2 > ##D ) > ## End(Not run) > > # Estimate stability statistics > res <- dimensionStability(boot.wmt) Item Stability Analysis Organizing data...done Computing results...done > res$dimension.stability $structural.consistency 1 2 0.416 0.884 $average.item.stability 1 2 0.867 0.961 > > ## Not run: > ##D # Produce Methods section > ##D methods.section( > ##D boot.wmt, > ##D stats = "dimensionStability" > ##D ) > ## End(Not run) > > > > > > cleanEx() > nameEx("dnn.weights") > ### * dnn.weights > > flush(stderr()); flush(stdout()) > > ### Name: dnn.weights > ### Title: Loadings Comparison Test Deep Learning Neural Network Weights > ### Aliases: dnn.weights > ### Keywords: datasets > > ### ** Examples > > data("dnn.weights") > > > > > cleanEx() > nameEx("dynEGA") > ### * dynEGA > > flush(stderr()); flush(stdout()) > > ### Name: dynEGA > ### Title: Dynamic Exploratory Graph Analysis > ### Aliases: dynEGA > > ### ** Examples > > # Obtain data > sim.dynEGA <- sim.dynEGA # bypasses CRAN checks > > ## Not run: > ##D # Population structure > ##D dyn.random <- dynEGA( > ##D data = sim.dynEGA, n.embed = 5, tau = 1, > ##D delta = 1, id = 25, group = 26, use.derivatives = 1, > ##D level = "population", ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # Plot population structure > ##D plot(dyn.random) > ##D > ##D # Group structure > ##D dyn.group <- dynEGA( > ##D data = sim.dynEGA, n.embed = 5, tau = 1, > ##D delta = 1, id = 25, group = 26, use.derivatives = 1, > ##D level = "group", ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # Plot group structure > ##D plot(dyn.group, ncol = 2, nrow = 1) > ##D > ##D # Intraindividual structure > ##D dyn.individual <- dynEGA( > ##D data = sim.dynEGA, n.embed = 5, tau = 1, > ##D delta = 1, id = 25, group = 26, use.derivatives = 1, > ##D level = "individual", ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # Plot individual structure (participant 1) > ##D plot(dyn.individual, id = 1) > ## End(Not run) > > > > > cleanEx() > nameEx("dynEGA.ind.pop") > ### * dynEGA.ind.pop > > flush(stderr()); flush(stdout()) > > ### Name: dynEGA.ind.pop > ### Title: Dynamic EGA > ### Aliases: dynEGA.ind.pop > > ### ** Examples > > # Obtain data > sim.dynEGA <- sim.dynEGA # bypasses CRAN checks > > ## Not run: > ##D # Dynamic EGA individual and population structure > ##D dyn.ega1 <- dynEGA.ind.pop( > ##D data = sim.dynEGA, n.embed = 5, tau = 1, > ##D delta = 1, id = 25, use.derivatives = 1, > ##D ncores = 2, corr = "pearson" > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("ega.wmt") > ### * ega.wmt > > flush(stderr()); flush(stdout()) > > ### Name: ega.wmt > ### Title: 'EGA' Network of 'wmt2'Data > ### Aliases: ega.wmt > ### Keywords: datasets > > ### ** Examples > > data("ega.wmt") > > > > > cleanEx() > nameEx("entropyFit") > ### * entropyFit > > flush(stderr()); flush(stdout()) > > ### Name: entropyFit > ### Title: Entropy Fit Index > ### Aliases: entropyFit > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA model > ##D ega.wmt <- EGA(data = wmt) > ## End(Not run) > > # Compute entropy indices > entropyFit(data = wmt, structure = ega.wmt$wc) Total.Correlation Total.Correlation.MM Entropy.Fit Entropy.Fit.MM 1 0.2429484 0.2222733 -1.103339 -1.127812 Average.Entropy 1 -1.836265 > > > > > cleanEx() > nameEx("ergoInfo") > ### * ergoInfo > > flush(stderr()); flush(stdout()) > > ### Name: ergoInfo > ### Title: Ergodicity Information Index > ### Aliases: ergoInfo > > ### ** Examples > > # Obtain data > sim.dynEGA <- sim.dynEGA # bypasses CRAN checks > > ## Not run: > ##D # Dynamic EGA individual and population structure > ##D dyn.ega1 <- dynEGA.ind.pop( > ##D data = sim.dynEGA[,-26], n.embed = 5, tau = 1, > ##D delta = 1, id = 25, use.derivatives = 1, > ##D ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # Compute empirical ergodicity information index > ##D eii <- ergoInfo( > ##D dynEGA.object = dyn.ega1, > ##D use = "weighted" > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("glla") > ### * glla > > flush(stderr()); flush(stdout()) > > ### Name: glla > ### Title: Generalized Local Linear Approximation > ### Aliases: glla > > ### ** Examples > > # A time series with 8 time points > tseries <- 49:56 > deriv.tseries <- glla(tseries, n.embed = 4, tau = 1, delta = 1, order = 2) > > > > > cleanEx() > nameEx("hierEGA") > ### * hierEGA > > flush(stderr()); flush(stdout()) > > ### Name: hierEGA > ### Title: Hierarchical 'EGA' > ### Aliases: hierEGA > > ### ** Examples > > # Obtain example data > data <- optimism > > ## Not run: > ##D # hierEGA example > ##D opt.hier<- hierEGA( > ##D data = optimism, > ##D algorithm = "louvain" > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("infoCluster") > ### * infoCluster > > flush(stderr()); flush(stdout()) > > ### Name: infoCluster > ### Title: Information Theoretic Mixture Clustering for 'dynEGA' > ### Aliases: infoCluster > > ### ** Examples > > # Obtain data > sim.dynEGA <- sim.dynEGA # bypasses CRAN checks > > ## Not run: > ##D # Dynamic EGA individual and population structure > ##D dyn.ega1 <- dynEGA.ind.pop( > ##D data = sim.dynEGA, n.embed = 5, tau = 1, > ##D delta = 1, id = 25, use.derivatives = 1, > ##D ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # Perform information-theoretic clustering > ##D clust1 <- infoCluster(dynEGA.object = dyn.ega1) > ## End(Not run) > > > > > cleanEx() > nameEx("intelligenceBattery") > ### * intelligenceBattery > > flush(stderr()); flush(stdout()) > > ### Name: intelligenceBattery > ### Title: Intelligence Data > ### Aliases: intelligenceBattery > ### Keywords: datasets > > ### ** Examples > > data("intelligenceBattery") > > > > > cleanEx() > nameEx("invariance") > ### * invariance > > flush(stderr()); flush(stdout()) > > ### Name: invariance > ### Title: Measurement Invariance of 'EGA' Structure > ### Aliases: invariance > > ### ** Examples > > # Load data > wmt <- wmt2[-1,7:24] > > # Groups > groups <- rep(1:2, each = nrow(wmt) / 2) > > ## Not run: > ##D # Measurement invariance > ##D results <- invariance(wmt, groups, ncores = 2) > ## End(Not run) > > > > > cleanEx() > nameEx("itemStability") > ### * itemStability > > flush(stderr()); flush(stdout()) > > ### Name: itemStability > ### Title: Item Stability Statistics from 'bootEGA' > ### Aliases: itemStability > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Standard EGA example > ##D boot.wmt <- bootEGA( > ##D data = wmt, iter = 100, # recommended 500 > ##D plot.typicalStructure = FALSE, # No plot for CRAN checks > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Standard item stability > ##D wmt.is <- itemStability(boot.wmt) > ##D > ##D # Produce Methods section > ##D methods.section( > ##D boot.wmt, > ##D stats = "itemStability" > ##D ) > ##D > ##D # EGA fit example > ##D boot.wmt.fit <- bootEGA( > ##D data = wmt, iter = 500, > ##D EGA.type = "EGA.fit", > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # EGA fit item stability > ##D wmt.is.fit <- itemStability(boot.wmt.fit) > ##D > ##D # Hierarchical EGA example > ##D boot.wmt.hier <- bootEGA( > ##D data = wmt, iter = 500, > ##D EGA.type = "hierEGA", > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Hierarchical EGA item stability > ##D wmt.is.hier <- itemStability(boot.wmt.hier) > ##D > ##D # Random-intercept EGA example > ##D boot.wmt.ri <- bootEGA( > ##D data = wmt, iter = 500, > ##D EGA.type = "riEGA", > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # Random-intercept EGA item stability > ##D wmt.is.ri <- itemStability(boot.wmt.ri) > ## End(Not run) > > > > > cleanEx() > nameEx("jsd") > ### * jsd > > flush(stderr()); flush(stdout()) > > ### Name: jsd > ### Title: Jensen-Shannon Distance > ### Aliases: jsd > > ### ** Examples > > # Obtain wmt2 data > wmt <- wmt2[,7:24] > > # Set seed (for reproducibility) > set.seed(1234) > > # Split data > split1 <- sample( + 1:nrow(wmt), floor(nrow(wmt) / 2) + ) > split2 <- setdiff(1:nrow(wmt), split1) > > # Obtain split data > data1 <- wmt[split1,] > data2 <- wmt[split2,] > > # Perform EBICglasso > glas1 <- EBICglasso.qgraph(data1) Variables detected as ordinal: wmt1; wmt2; wmt3; wmt4; wmt5; wmt6; wmt7; wmt8; wmt9; wmt10; wmt11; wmt12; wmt13; wmt14; wmt15; wmt16; wmt17; wmt18 > glas2 <- EBICglasso.qgraph(data2) Variables detected as ordinal: wmt1; wmt2; wmt3; wmt4; wmt5; wmt6; wmt7; wmt8; wmt9; wmt10; wmt11; wmt12; wmt13; wmt14; wmt15; wmt16; wmt17; wmt18 > > # Spectral JSD > jsd(glas1, glas2) # 0.1618195 [1] 0.1595892 > > # Spectral JSS (similarity) > 1 - jsd(glas1, glas2) # 0.8381805 [1] 0.8404108 > > # Jensen-Shannon Divergence > jsd(glas1, glas2, method = "kld") # 0.1923636 [1] 0.1393618 > > > > > cleanEx() > nameEx("jsd.ergoInfo") > ### * jsd.ergoInfo > > flush(stderr()); flush(stdout()) > > ### Name: jsd.ergoInfo > ### Title: Jensen-Shannon Distance Test for Ergodicity > ### Aliases: jsd.ergoInfo > > ### ** Examples > > ## Not run: > ##D # Dynamic EGA individual and population structures > ##D dyn1 <- dynEGA.ind.pop( > ##D data = sim.dynEGA[,-26], n.embed = 5, tau = 1, > ##D delta = 1, id = 25, use.derivatives = 1, > ##D model = "glasso", ncores = 2, corr = "pearson" > ##D ) > ##D > ##D # JSD Ergodicity Test > ##D testing.ergoinfo <- jsd.ergoInfo( > ##D dynEGA.object = dyn1 > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("louvain") > ### * louvain > > flush(stderr()); flush(stdout()) > > ### Name: louvain > ### Title: Louvain Community Detection Algorithm > ### Aliases: louvain > > ### ** Examples > > # Load data > dep <- depression[,24:44] > > # Estimate correlations > corr <- qgraph::cor_auto(dep) Variables detected as ordinal: BDI1; BDI2; BDI3; BDI4; BDI5; BDI6; BDI7; BDI8; BDI9; BDI10; BDI11; BDI12; BDI13; BDI14; BDI15; BDI16; BDI17; BDI18; BDI19; BDI20; BDI21 > > # Estimate network > net <- EBICglasso.qgraph(corr, n = nrow(dep)) > > # Estimate communities using modularity > louvain(net, method = "modularity") $wc BDI1 BDI2 BDI3 BDI4 BDI5 BDI6 BDI7 BDI8 BDI9 BDI10 BDI11 BDI12 BDI13 BDI14 1 2 3 1 2 1 1 1 1 1 5 4 2 3 3 2 3 4 2 3 2 2 2 2 2 1 1 3 4 4 BDI15 BDI16 BDI17 BDI18 BDI19 BDI20 BDI21 1 3 4 4 5 5 4 2 2 4 1 1 1 1 1 3 $modularity [1] 0.22139 0.23119 > > # Estimate communities using tefi > louvain(net, method = "tefi", corr = corr) $wc BDI1 BDI2 BDI3 BDI4 BDI5 BDI6 BDI7 BDI8 BDI9 BDI10 BDI11 BDI12 BDI13 BDI14 [1,] 1 1 1 1 1 1 1 1 1 1 2 1 1 1 BDI15 BDI16 BDI17 BDI18 BDI19 BDI20 BDI21 [1,] 1 1 1 1 1 1 3 $tefi [1] -19.53785 > > > > > cleanEx() > nameEx("mctest.ergoInfo") > ### * mctest.ergoInfo > > flush(stderr()); flush(stdout()) > > ### Name: mctest.ergoInfo > ### Title: Monte-Carlo Test for the Ergodicity Information Index > ### Aliases: mctest.ergoInfo > > ### ** Examples > > > ## Not run: > ##D \donttest{ > ##D dyn1 <- dynEGA.ind.pop(data = sim.dynEGA, n.embed = 5, tau = 1, > ##D delta = 1, id = 21, group = 22, use.derivatives = 1, > ##D model = "glasso", ncores = 2, corr = "pearson") > ##D > ##D eii1 <- ergoInfo(data = dyn1)$EII > ##D > ##D dist.ergoinfo <- mctest.ergoInfo(iter = 10, N = 10, EII = eii1, > ##D variab = 4, > ##D timep = 100, nfact = 2, error = 0.05, dfm = "DAFS", loadings = 0.55, autoreg = 0.8, > ##D crossreg = 0.1, var.shock = 0.18, cov.shock = 0.36, embed = 5, tau=1, delta=1, derivatives=1, > ##D model = "glasso", ncores = 2, corr = "pearson") > ##D } > ## End(Not run) > > > > > cleanEx() > nameEx("methods.section") > ### * methods.section > > flush(stderr()); flush(stdout()) > > ### Name: methods.section > ### Title: Automated Methods Section for 'EGAnet' Objects > ### Aliases: methods.section > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA(data = wmt) > ##D > ##D # EGA Methods section > ##D methods.section(ega.wmt) > ##D > ##D # Estimate standardized network loadings > ##D wmt.loads <- net.loads(ega.wmt)$std > ##D > ##D # EGA Methods section with network loadings > ##D methods.section(ega.wmt, stats = "net.loads") > ##D > ##D # bootEGA example > ##D ## plot.type = "qqraph" used for CRAN checks > ##D ## plot.type = "GGally" is the default > ##D boot.wmt <- bootEGA( > ##D data = wmt, iter = 500, > ##D type = "parametric", ncores = 2 > ##D ) > ##D > ##D # EGA and bootEGA Methods section > ##D methods.section(ega.wmt, boot.wmt) > ##D > ##D # Estimate structural consistency > ##D sc.wmt <- dimensionStability(boot.wmt) > ##D > ##D # EGA and bootEGA Methods section with structural consistency and item stability > ##D methods.section(boot.wmt, stats = c("dimensionStability", "itemStability")) > ##D > ##D # EGA with network loadings and > ##D # bootEGA Methods section with structural consistency and item stability > ##D methods.section( > ##D ega.wmt, > ##D boot.wmt, > ##D stats = c( > ##D "net.loads", > ##D "dimensionStability", > ##D "itemStability" > ##D ) > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("net.loads") > ### * net.loads > > flush(stderr()); flush(stdout()) > > ### Name: net.loads > ### Title: Network Loadings > ### Aliases: net.loads > > ### ** Examples > > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA( > ##D data = wmt, > ##D plot.EGA = FALSE # No plot for CRAN checks > ##D ) > ## End(Not run) > > # Network loadings > net.loads(ega.wmt) 2 1 wmt2 0.383 0.088 wmt1 0.254 0.064 wmt3 0.217 0.131 wmt5 0.201 0.142 wmt4 0.188 0.140 wmt9 0.034 0.293 wmt7 0.074 0.258 wmt15 0.034 0.247 wmt14 0.030 0.243 wmt6 0.140 0.241 wmt16 0.054 0.220 wmt8 0.065 0.219 wmt10 0.166 0.206 wmt12 0.067 0.187 wmt18 -0.006 0.183 wmt13 0.050 0.170 wmt17 0.097 0.168 wmt11 0.075 0.155 Loadings <= |0| are blank > > ## Not run: > ##D # Produce Methods section > ##D methods.section( > ##D ega.wmt, > ##D stats = "net.loads" > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("net.scores") > ### * net.scores > > flush(stderr()); flush(stdout()) > > ### Name: net.scores > ### Title: Network Scores > ### Aliases: net.scores > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA > ##D ega.wmt <- EGA( > ##D data = wmt, > ##D plot.EGA = FALSE # No plot for CRAN checks > ##D ) > ## End(Not run) > > # Network scores > net.scores(data = wmt, A = ega.wmt) Warning in net.scores(data = wmt, A = ega.wmt) : Argument 'impute' is missing. No imputation will be used. $unstd.scores 1 2 1 0.506 0.516 2 0.895 0.969 3 0.644 0.824 4 0.207 0.419 5 0.374 0.722 6 0.249 0.636 7 0.305 0.275 8 1.000 1.000 9 0.476 0.702 10 0.349 0.630 11 0.550 0.688 12 0.799 0.857 13 0.431 0.665 14 0.378 0.749 15 0.349 0.722 16 0.101 0.103 17 0.366 0.670 18 0.265 0.423 19 0.191 0.357 20 0.885 0.972 21 1.000 1.000 22 0.630 0.673 23 0.709 0.899 24 0.409 0.389 25 0.629 0.806 26 0.266 0.249 27 0.235 0.676 28 0.000 0.000 29 0.429 0.610 30 0.952 0.978 31 0.858 0.988 32 0.323 0.471 33 0.905 0.944 34 0.306 0.739 35 0.652 0.802 36 0.439 0.706 37 0.500 0.770 38 0.169 0.383 39 0.664 0.838 40 0.420 0.219 41 0.768 0.884 42 0.299 0.477 43 0.422 0.539 44 0.576 0.831 45 0.177 0.432 46 0.719 0.895 47 0.319 0.505 48 0.634 0.882 49 0.902 0.922 50 0.348 0.404 51 0.820 0.894 52 0.361 0.713 53 0.147 0.312 54 0.339 0.523 55 0.591 0.750 56 0.448 0.625 57 0.207 0.530 58 0.369 0.444 59 0.673 0.837 60 0.570 0.787 61 1.000 1.000 62 0.921 0.811 63 0.623 0.623 64 0.769 0.811 65 0.646 0.708 66 0.761 0.878 67 0.069 0.013 68 0.838 0.882 69 0.536 0.750 70 0.259 0.451 71 0.762 0.877 72 0.929 0.938 73 0.332 0.593 74 0.494 0.817 75 0.558 0.834 76 0.167 0.314 77 0.144 0.377 78 0.553 0.718 79 0.621 0.836 80 0.476 0.432 81 0.657 0.909 82 0.464 0.716 83 0.479 0.617 84 0.074 0.301 85 0.555 0.686 86 0.395 0.699 87 0.532 0.472 88 0.828 0.903 89 0.797 0.837 90 0.868 0.917 91 0.497 0.642 92 0.618 0.643 93 0.590 0.659 94 0.268 0.434 95 0.643 0.815 96 0.190 0.247 97 0.706 0.872 98 0.394 0.611 99 0.675 0.782 100 0.693 0.705 101 0.823 0.920 102 0.510 0.765 103 0.333 0.583 104 0.681 0.851 105 0.678 0.762 106 0.895 0.969 107 0.445 0.784 108 0.710 0.672 109 0.885 0.918 110 0.876 0.979 111 0.627 0.780 112 0.954 0.966 113 0.917 0.985 114 0.810 0.828 115 0.284 0.540 116 1.000 1.000 117 0.686 0.758 118 0.658 0.739 119 0.332 0.132 120 0.700 0.845 121 0.795 0.930 122 0.693 0.879 123 0.893 0.981 124 0.515 0.483 125 0.719 0.949 126 0.847 0.947 127 0.582 0.650 128 0.885 0.918 129 0.761 0.897 130 0.893 0.927 131 0.326 0.651 132 0.942 0.949 133 0.569 0.791 134 0.405 0.470 135 0.761 0.932 136 0.468 0.715 137 0.533 0.753 138 0.808 0.764 139 0.509 0.819 140 0.601 0.767 141 0.132 0.312 142 0.480 0.686 143 0.533 0.686 144 0.714 0.786 145 0.607 0.769 146 0.581 0.787 147 0.505 0.697 148 0.141 0.452 149 0.569 0.798 150 0.915 0.859 151 0.700 0.845 152 0.895 0.915 153 0.710 0.887 154 0.723 0.872 155 0.867 0.987 156 0.732 0.665 157 0.893 0.981 158 0.249 0.636 159 0.385 0.629 160 0.653 0.835 161 0.636 0.871 162 0.363 0.596 163 0.712 0.874 164 0.885 0.972 165 0.208 0.069 166 0.876 0.925 167 0.417 0.469 168 0.837 0.918 169 0.641 0.886 170 0.537 0.682 171 0.488 0.614 172 0.242 0.433 173 0.538 0.735 174 0.571 0.780 175 0.474 0.710 176 0.601 0.732 177 0.568 0.815 178 0.607 0.799 179 0.176 0.604 180 0.145 0.363 181 0.775 0.858 182 0.625 0.761 183 0.743 0.627 184 0.422 0.682 185 0.824 0.768 186 0.253 0.457 187 0.809 0.936 188 0.825 0.915 189 0.531 0.781 190 0.501 0.631 191 0.296 0.670 192 0.584 0.786 193 0.234 0.503 194 1.000 1.000 195 0.828 0.957 196 0.665 0.820 197 0.942 0.949 198 0.407 0.594 199 0.462 0.449 200 0.290 0.414 201 0.248 0.470 202 0.941 1.003 203 0.572 0.693 204 0.422 0.397 205 0.814 0.939 206 0.585 0.518 207 0.692 0.831 208 0.511 0.650 209 0.212 0.515 210 0.187 0.427 211 0.895 0.915 212 0.741 0.924 213 0.379 0.481 214 0.636 0.834 215 0.754 0.906 216 0.347 0.575 217 0.897 0.935 218 0.069 0.013 219 0.381 0.531 220 0.506 0.823 221 0.513 0.808 222 0.488 0.537 223 0.942 0.949 224 0.350 0.549 225 0.557 0.782 226 0.467 0.719 227 0.320 0.698 228 0.383 0.457 229 0.568 0.815 230 0.818 0.928 231 0.818 0.928 232 0.756 0.929 233 0.649 0.856 234 0.727 0.737 235 0.886 0.904 236 0.315 0.520 237 0.944 0.969 238 0.278 0.465 239 0.262 0.245 240 0.501 0.804 241 0.120 0.066 242 0.596 0.753 243 0.660 0.771 244 0.706 0.872 245 0.358 0.724 246 0.765 0.923 247 0.860 0.729 248 0.873 0.936 249 0.687 0.619 250 1.000 1.000 251 0.785 0.643 252 0.322 0.648 253 0.883 0.952 254 0.252 0.409 255 0.497 0.581 256 0.559 0.830 257 0.818 0.928 258 0.587 0.735 259 0.546 0.513 260 0.645 0.559 261 0.845 0.891 262 0.697 0.433 263 0.164 0.479 264 0.335 0.624 265 0.398 0.676 266 0.653 0.744 267 0.818 0.928 268 0.362 0.609 269 0.542 0.631 270 0.761 0.915 271 0.811 0.903 272 0.868 0.971 273 0.350 0.618 274 0.488 0.666 275 0.647 0.536 276 0.839 0.884 277 0.565 0.590 278 0.110 0.244 279 0.633 0.780 280 0.669 0.809 281 0.653 0.853 282 0.942 0.949 283 0.287 0.408 284 0.832 0.911 285 0.968 0.791 286 0.832 0.911 287 0.532 0.687 288 0.232 0.634 289 0.594 0.795 290 0.163 0.103 291 0.260 0.480 292 0.339 0.509 293 0.586 0.592 294 0.329 0.346 295 0.843 0.871 296 0.526 0.760 297 0.232 0.634 298 0.360 0.630 299 0.164 0.493 300 0.548 0.806 301 0.742 0.788 302 0.739 0.913 303 0.249 0.636 304 0.465 0.792 305 0.676 0.767 306 0.763 0.902 307 0.280 0.487 308 0.352 0.572 309 0.895 0.969 310 0.339 0.546 311 0.689 0.849 312 0.287 0.421 313 0.942 0.949 314 0.216 0.060 315 0.195 0.596 316 0.349 0.630 317 0.704 0.756 318 0.541 0.763 319 0.437 0.484 320 0.954 0.966 321 0.931 0.987 322 0.431 0.665 323 0.653 0.810 324 0.401 0.551 325 0.559 0.830 326 0.734 0.825 327 0.216 0.027 328 0.354 0.760 329 0.565 0.820 330 0.187 0.283 331 0.900 0.843 332 0.547 0.754 333 0.359 0.579 334 0.744 0.912 335 0.365 0.558 336 0.167 0.446 337 0.103 0.065 338 0.536 0.620 339 0.255 0.568 340 0.171 0.284 341 0.589 0.849 342 0.388 0.630 343 0.593 0.826 344 0.654 0.809 345 0.042 0.106 346 0.213 0.354 347 0.510 0.811 348 0.733 0.903 349 0.121 0.387 350 0.407 0.654 351 0.187 0.411 352 0.200 0.524 353 0.482 0.487 354 0.366 0.713 355 0.392 0.723 356 0.362 0.769 357 0.251 0.258 358 0.634 0.790 359 0.669 0.768 360 0.425 0.368 361 0.617 0.556 362 0.542 0.647 363 0.489 0.659 364 0.199 0.502 365 0.043 0.092 366 0.520 0.691 367 0.456 0.439 368 0.233 0.234 369 0.237 0.632 370 0.362 0.505 371 0.247 0.358 372 0.354 0.728 373 0.099 0.153 374 0.688 0.846 375 0.085 0.198 376 0.061 0.222 377 0.255 0.607 378 0.344 0.499 379 0.738 0.871 380 0.420 0.660 381 0.245 0.210 382 0.386 0.544 383 0.000 0.000 384 0.228 0.092 385 0.524 0.813 386 0.325 0.627 387 0.736 0.741 388 0.171 0.165 389 0.163 0.245 390 0.116 0.138 391 0.133 0.271 392 0.284 0.573 393 0.337 0.662 394 0.636 0.818 395 0.133 0.317 396 0.554 0.654 397 0.046 0.034 398 0.189 0.060 399 0.670 0.905 400 0.256 0.252 401 0.469 0.691 402 0.383 0.587 403 0.265 0.140 404 0.277 0.589 405 0.078 0.113 406 0.212 0.232 407 0.225 0.521 408 0.310 0.667 409 0.362 0.769 410 0.180 0.306 411 0.263 0.112 412 0.032 0.209 413 0.744 0.912 414 0.453 0.751 415 0.219 0.538 416 0.277 0.411 417 0.403 0.713 418 0.042 0.106 419 0.201 0.042 420 0.112 0.058 421 0.177 0.432 422 0.211 0.235 423 0.135 0.060 424 0.280 0.137 425 0.042 0.106 426 0.492 0.784 427 0.101 0.103 428 0.249 0.277 429 0.233 0.145 430 0.162 0.271 431 0.142 0.045 432 0.091 0.281 433 0.694 0.922 434 0.275 0.251 435 0.043 0.092 436 0.147 0.175 437 0.206 0.337 438 0.212 0.262 439 0.090 0.179 440 0.250 0.443 441 0.114 0.382 442 0.042 0.106 443 0.259 0.356 444 0.090 0.206 445 0.324 0.655 446 0.048 0.022 447 0.303 0.357 448 0.120 0.066 449 0.339 0.284 450 0.526 0.728 451 0.130 0.135 452 0.221 0.189 453 0.293 0.504 454 0.105 0.120 455 0.383 0.712 456 0.199 0.358 457 0.665 0.726 458 0.886 0.904 459 0.418 0.639 460 0.086 0.115 461 0.819 0.912 462 0.283 0.245 463 0.144 0.377 464 0.139 0.339 465 0.344 0.533 466 0.491 0.829 467 0.737 0.893 468 0.534 0.805 469 0.163 0.158 470 0.275 0.599 471 0.405 0.714 472 0.119 0.084 473 0.186 0.337 474 0.251 0.232 475 0.123 0.322 476 0.104 0.314 477 0.214 0.496 478 0.091 0.114 479 0.763 0.902 480 0.253 0.619 481 0.215 0.247 482 0.194 0.057 483 0.440 0.728 484 0.367 0.732 485 0.571 0.785 486 0.452 0.674 487 0.428 0.760 488 0.629 0.631 489 0.059 -0.003 490 0.198 0.382 491 0.275 0.598 492 0.339 0.620 493 0.574 0.814 494 0.414 0.744 495 0.231 0.152 496 0.297 0.372 497 0.381 0.637 498 0.406 0.648 499 0.219 0.101 500 0.326 0.204 501 0.182 0.557 502 0.456 0.668 503 0.446 0.642 504 0.691 0.663 505 0.501 0.690 506 0.417 0.660 507 0.774 0.690 508 0.883 0.952 509 0.766 0.875 510 0.215 0.518 511 0.580 0.744 512 0.831 0.953 513 0.696 0.727 514 0.379 0.771 515 0.331 0.655 516 0.343 0.542 517 0.540 0.834 518 0.199 0.076 519 0.000 0.000 520 0.216 0.278 521 0.440 0.723 522 0.296 0.405 523 0.111 0.119 524 0.148 0.123 525 0.487 0.757 526 0.235 0.289 527 0.326 0.232 528 0.487 0.832 529 0.381 0.237 530 0.749 0.715 531 0.555 0.805 532 0.324 0.395 533 0.355 0.214 534 0.257 0.341 535 0.814 0.926 536 0.573 0.697 537 0.942 0.949 538 0.245 0.356 539 0.677 0.866 540 0.868 0.934 541 0.545 0.829 542 0.804 0.930 543 0.316 0.338 544 0.654 0.761 545 0.517 0.281 546 0.308 0.694 547 0.094 0.417 548 0.449 0.685 549 0.589 0.736 550 0.176 0.604 551 0.629 0.602 552 0.258 0.367 553 0.278 0.465 554 0.352 0.773 555 0.385 0.495 556 0.237 0.525 557 0.388 0.529 558 0.165 0.428 559 0.117 0.407 560 0.409 0.517 561 0.256 0.587 562 0.546 0.642 563 0.250 0.619 564 0.403 0.554 565 0.125 0.097 566 0.569 0.824 567 0.438 0.733 568 0.570 0.628 569 0.143 0.028 570 0.444 0.762 571 0.546 0.642 572 0.773 0.902 573 0.435 0.591 574 0.667 0.896 575 0.608 0.820 576 0.558 0.734 577 0.577 0.850 578 0.145 0.237 579 0.163 0.430 580 0.586 0.721 581 0.639 0.860 582 0.306 0.644 583 0.478 0.771 584 0.836 0.842 585 0.824 0.968 586 0.341 0.308 587 0.235 0.676 588 0.428 0.656 589 0.164 0.493 590 0.280 0.617 591 0.296 0.522 592 0.605 0.813 593 0.240 0.374 594 0.595 0.678 595 0.895 0.969 596 0.000 0.000 597 0.610 0.819 598 0.214 0.377 599 0.505 0.630 600 0.497 0.815 601 0.541 0.626 602 0.157 0.329 603 0.461 0.533 604 0.586 0.790 605 0.091 0.281 606 0.892 0.906 607 0.217 0.388 608 0.604 0.637 609 0.502 0.727 610 0.184 0.263 611 0.325 0.448 612 0.716 0.824 613 0.684 0.683 614 0.398 0.693 615 0.148 0.213 616 0.205 0.536 617 0.065 0.024 618 0.390 0.342 619 0.390 0.342 620 0.328 0.639 621 0.811 0.957 622 0.117 0.407 623 0.265 0.572 624 0.775 0.800 625 0.641 0.847 626 0.649 0.798 627 0.465 0.717 628 0.455 0.544 629 0.667 0.841 630 0.579 0.820 631 0.664 0.846 632 0.453 0.646 633 0.065 0.024 634 0.081 0.144 635 0.840 0.771 636 0.195 0.540 637 0.489 0.620 638 0.195 0.326 639 0.942 0.949 640 0.759 0.926 641 0.417 0.797 642 0.216 0.119 643 0.763 0.916 644 0.737 0.595 645 0.133 0.162 646 0.344 0.581 647 0.656 0.654 648 0.549 0.826 649 0.866 0.963 650 0.061 0.222 651 0.811 0.957 652 0.222 0.248 653 0.139 0.427 654 0.293 0.380 655 0.482 0.835 656 0.557 0.780 657 0.225 0.342 658 0.289 0.120 659 0.094 0.417 660 0.274 0.542 661 0.278 0.592 662 0.843 0.871 663 0.635 0.663 664 0.821 0.924 665 0.835 0.930 666 0.164 0.143 667 0.000 0.000 668 0.074 0.015 669 0.701 0.893 670 0.553 0.745 671 0.826 0.721 672 0.356 0.480 673 0.534 0.557 674 0.426 0.536 675 0.263 0.646 676 0.099 0.123 677 0.271 0.111 678 0.173 0.193 679 0.269 0.628 680 0.190 0.218 681 0.942 0.949 682 0.329 0.287 683 0.277 0.361 684 0.338 0.621 685 0.604 0.867 686 0.393 0.620 687 0.735 0.808 688 0.605 0.730 689 0.217 0.492 690 0.157 0.329 691 0.172 0.171 692 0.828 0.853 693 0.467 0.511 694 0.513 0.637 695 0.071 0.062 696 0.661 0.803 697 0.357 0.415 698 0.366 0.594 699 0.565 0.706 700 0.760 0.715 701 0.506 0.756 702 0.981 0.884 703 0.653 0.835 704 0.541 0.667 705 0.182 0.557 706 0.167 0.446 707 0.222 0.339 708 0.658 0.761 709 0.580 0.519 710 0.321 0.456 711 0.602 0.820 712 0.613 0.871 713 0.121 0.206 714 0.085 0.198 715 0.213 0.180 716 0.176 0.445 717 0.465 0.616 718 0.751 0.899 719 0.376 0.688 720 0.617 0.854 721 0.443 0.579 722 0.203 0.521 723 0.312 0.650 724 0.827 0.921 725 0.656 0.651 726 0.690 0.749 727 0.273 0.522 728 0.291 0.478 729 0.265 0.423 730 0.198 0.551 731 0.382 0.524 732 0.511 0.650 733 0.209 0.374 734 0.711 0.886 735 1.000 1.000 736 0.687 0.755 737 0.237 0.632 738 0.436 0.753 739 0.245 0.492 740 0.531 0.781 741 0.773 0.902 742 0.231 0.579 743 0.093 0.431 744 0.587 0.788 745 0.311 0.629 746 0.019 0.116 747 0.599 0.779 748 0.184 0.545 749 0.074 0.314 750 0.062 0.208 751 0.019 0.116 752 0.242 0.467 753 0.523 0.697 754 0.111 0.119 755 0.083 0.173 756 0.564 0.678 757 0.638 0.842 758 0.341 0.329 759 0.470 0.669 760 0.312 0.543 761 0.821 0.924 762 0.888 0.942 763 0.431 0.587 764 0.363 0.346 765 0.323 0.505 766 0.238 0.233 767 0.750 0.926 768 0.391 0.634 769 0.957 0.908 770 0.610 0.869 771 0.811 0.903 772 0.427 0.571 773 0.414 0.514 774 0.684 0.893 775 0.321 0.697 776 0.265 0.416 777 0.182 0.272 778 0.684 0.905 779 0.159 0.230 780 0.543 0.541 781 0.339 0.690 782 0.560 0.683 783 0.425 0.625 784 0.398 0.741 785 0.403 0.598 786 0.043 0.092 787 0.296 0.268 788 0.415 0.643 789 0.370 0.701 790 0.821 0.953 791 0.446 0.553 792 0.828 0.903 793 0.835 0.930 794 0.893 0.981 795 0.665 0.739 796 0.281 0.411 797 0.584 0.786 798 0.491 0.776 799 0.878 0.945 800 0.277 0.188 801 0.269 0.628 802 0.810 0.932 803 0.514 0.797 804 0.776 0.822 805 0.285 0.533 806 0.768 0.885 807 0.309 0.522 808 0.329 0.581 809 0.133 0.387 810 0.482 0.674 811 0.334 0.547 812 0.576 0.737 813 0.093 0.431 814 0.147 0.346 815 0.811 0.903 816 1.000 1.000 817 0.584 0.721 818 0.472 0.651 819 1.000 1.000 820 0.744 0.912 821 0.797 0.837 822 0.952 0.978 823 0.762 0.664 824 0.688 0.733 825 0.526 0.728 826 0.371 0.743 827 0.852 0.856 828 0.941 1.003 829 0.562 0.778 830 0.926 0.985 831 0.739 0.902 832 0.703 0.841 833 0.827 0.921 834 0.721 0.815 835 0.811 0.938 836 0.800 0.937 837 0.556 0.845 838 0.673 0.757 839 0.660 0.799 840 1.000 1.000 841 0.885 0.918 842 0.182 0.557 843 0.354 0.760 844 0.835 0.853 845 0.187 0.469 846 0.769 0.906 847 0.324 0.325 848 0.323 0.478 849 0.691 0.678 850 0.302 0.427 851 0.682 0.737 852 0.437 0.822 853 0.736 0.904 854 0.547 0.709 855 0.500 0.500 856 0.391 0.708 857 0.385 0.532 858 0.406 0.678 859 0.816 0.959 860 0.065 0.024 861 0.701 0.875 862 0.618 0.860 863 0.735 0.918 864 0.636 0.850 865 0.459 0.707 866 0.465 0.616 867 0.574 0.620 868 0.251 0.551 869 0.482 0.684 870 0.406 0.678 871 0.310 0.206 872 0.689 0.773 873 0.358 0.377 874 0.723 0.812 875 0.523 0.726 876 0.463 0.653 877 0.562 0.672 878 0.496 0.563 879 0.523 0.674 880 0.476 0.605 881 0.593 0.467 882 0.139 0.045 883 0.243 0.188 884 0.941 1.003 885 0.827 0.921 886 0.329 0.326 887 0.293 0.503 888 0.491 0.818 889 0.769 0.831 890 0.658 0.811 891 0.809 0.936 892 0.093 0.431 893 0.352 0.642 894 0.000 0.000 895 0.180 0.122 896 0.000 0.000 897 0.429 0.729 898 0.468 0.662 899 0.821 0.932 900 0.207 0.477 901 0.366 0.553 902 0.493 0.559 903 0.712 0.832 904 0.602 0.678 905 0.192 0.384 906 0.570 0.847 907 0.657 0.460 908 0.432 0.645 909 0.323 0.605 910 0.754 0.932 911 0.718 0.859 912 0.334 0.597 913 0.780 0.887 914 0.269 0.584 915 0.257 0.369 916 0.785 0.919 917 0.587 0.797 918 0.515 0.768 919 0.184 0.364 920 0.275 0.388 921 0.032 0.209 922 0.651 0.776 923 0.543 0.555 924 0.483 0.400 925 0.620 0.568 926 0.142 0.012 927 0.275 0.303 928 0.371 0.504 929 0.426 0.587 930 0.821 0.924 931 0.524 0.813 932 0.738 0.909 933 0.574 0.663 934 0.532 0.817 935 0.350 0.598 936 0.465 0.548 937 0.626 0.819 938 0.938 0.972 939 0.421 0.785 940 0.381 0.686 941 0.426 0.628 942 0.560 0.668 943 0.646 0.850 944 0.301 0.423 945 0.265 0.409 946 0.395 0.669 947 0.711 0.887 948 0.734 0.739 949 0.388 0.711 950 0.954 0.966 951 0.558 0.838 952 0.653 0.835 953 0.883 0.952 954 0.473 0.529 955 0.172 0.171 956 0.162 0.204 957 0.108 0.094 958 0.744 0.912 959 0.466 0.688 960 0.243 0.309 961 0.820 0.895 962 0.897 0.935 963 0.837 0.918 964 0.404 0.656 965 0.658 0.801 966 0.941 1.003 967 0.019 0.116 968 0.216 0.171 969 0.386 0.641 970 0.564 0.785 971 0.000 0.000 972 0.492 0.610 973 0.213 0.510 974 0.427 0.700 975 0.419 0.710 976 0.423 0.633 977 0.952 0.978 978 0.270 0.544 979 0.000 0.000 980 0.489 0.820 981 0.700 0.869 982 0.759 0.890 983 0.366 0.559 984 0.393 0.681 985 0.470 0.714 986 0.447 0.639 987 0.700 0.869 988 0.532 0.767 989 0.441 0.798 990 0.775 0.904 991 0.000 0.000 992 0.311 0.668 993 0.457 0.766 994 0.266 0.603 995 0.586 0.647 996 0.122 0.281 997 0.147 0.346 998 0.513 0.502 999 0.895 0.915 1000 0.121 0.387 1001 0.142 0.352 1002 0.645 0.844 1003 0.641 0.847 1004 0.823 0.920 1005 0.283 0.270 1006 0.056 0.031 1007 0.637 0.673 1008 0.729 0.801 1009 0.426 0.667 1010 0.176 0.479 1011 0.473 0.671 1012 0.398 0.671 1013 0.622 0.666 1014 0.815 0.897 1015 0.815 0.897 1016 0.374 0.576 1017 0.954 0.966 1018 0.244 0.617 1019 0.672 0.852 1020 0.765 0.893 1021 0.175 0.182 1022 0.134 0.498 1023 0.941 1.003 1024 0.441 0.588 1025 0.673 0.668 1026 0.562 0.674 1027 0.114 0.046 1028 0.617 0.628 1029 0.499 0.705 1030 0.821 0.953 1031 0.217 0.213 1032 0.524 0.813 1033 0.905 0.944 1034 0.183 0.276 1035 0.827 0.921 1036 0.531 0.798 1037 0.339 0.620 1038 0.136 0.523 1039 0.677 0.766 1040 0.523 0.741 1041 0.583 0.726 1042 0.354 0.571 1043 0.634 0.863 1044 0.787 0.875 1045 0.513 0.433 1046 0.590 0.728 1047 0.728 0.768 1048 0.897 0.935 1049 0.247 0.469 1050 0.770 0.925 1051 0.195 0.132 1052 0.211 0.193 1053 0.187 0.071 1054 0.773 0.791 1055 0.398 0.527 1056 0.753 0.870 1057 0.895 0.948 1058 0.661 0.773 1059 0.121 0.387 1060 0.473 0.727 1061 0.676 0.682 1062 0.668 0.765 1063 0.452 0.675 1064 0.804 0.777 1065 0.238 0.564 1066 0.246 0.455 1067 0.427 0.792 1068 0.499 0.803 1069 0.897 0.935 1070 0.187 0.071 1071 0.523 0.741 1072 0.246 0.203 1073 0.941 1.003 1074 0.529 0.742 1075 0.465 0.663 1076 0.885 0.972 1077 0.152 0.503 1078 0.256 0.548 1079 0.727 0.814 1080 0.667 0.841 1081 0.431 0.583 1082 0.708 0.837 1083 0.168 0.433 1084 0.321 0.584 1085 0.342 0.610 1086 0.737 0.795 1087 0.812 0.939 1088 0.754 0.744 1089 0.411 0.685 1090 0.646 0.850 1091 0.819 0.966 1092 0.593 0.826 1093 0.511 0.602 1094 0.269 0.611 1095 0.366 0.553 1096 0.440 0.745 1097 0.293 0.287 1098 0.690 0.749 1099 0.200 0.421 1100 0.691 0.859 1101 0.635 0.692 1102 0.059 -0.003 1103 0.237 0.238 1104 0.542 0.628 1105 0.118 0.194 1106 0.243 0.261 1107 0.269 0.447 1108 0.667 0.768 1109 0.000 0.000 1110 0.821 0.924 1111 0.245 0.358 1112 0.032 0.209 1113 0.511 0.807 1114 0.275 0.417 1115 0.564 0.874 1116 1.000 1.000 1117 0.897 0.935 1118 0.630 0.861 1119 0.480 0.679 1120 0.676 0.767 1121 0.604 0.834 1122 0.839 0.884 1123 0.804 0.930 1124 0.813 0.795 1125 0.882 0.747 1126 0.605 0.691 1127 0.492 0.634 1128 0.462 0.694 1129 0.721 0.762 1130 0.491 0.788 1131 0.553 0.577 1132 0.236 0.072 1133 0.444 0.728 1134 0.058 0.051 1135 0.289 0.419 1136 1.000 1.000 1137 0.263 0.053 1138 0.706 0.665 1139 0.811 0.903 1140 1.000 1.000 1141 0.051 0.325 1142 0.600 0.776 1143 0.585 0.700 1144 0.207 0.477 1145 0.122 0.281 1146 0.122 0.281 1147 0.305 0.559 1148 0.272 0.434 1149 0.341 0.312 1150 0.198 0.129 1151 0.568 0.622 1152 0.580 0.746 1153 0.819 0.966 1154 0.305 0.559 1155 0.122 0.281 1156 0.207 0.477 1157 0.198 0.129 1158 0.568 0.622 1159 0.268 0.377 1160 0.538 0.640 1161 0.366 0.553 1162 0.122 0.281 1163 0.708 0.843 1164 0.768 0.905 1165 0.497 0.633 1166 0.598 0.740 1167 0.460 0.736 1168 0.340 0.638 1169 0.484 0.732 1170 0.697 0.873 1171 0.577 0.612 1172 0.548 0.721 1173 0.615 0.792 1174 0.247 0.236 1175 0.824 0.903 1176 0.663 0.762 1177 0.308 0.616 1178 0.461 0.533 1179 0.673 0.657 1180 0.539 0.521 1181 0.539 0.521 1182 0.074 0.032 1183 0.133 0.512 1184 0.618 0.742 1185 0.574 0.643 $std.scores 1 2 1 0.108 -0.396 2 1.659 1.359 3 0.659 0.797 4 -1.086 -0.771 5 -0.417 0.401 6 -0.917 0.068 7 -0.694 -1.328 8 2.078 1.480 9 -0.011 0.324 10 -0.520 0.046 11 0.284 0.272 12 1.276 0.927 13 -0.191 0.180 14 -0.403 0.507 15 -0.520 0.404 16 -1.509 -1.996 17 -0.450 0.202 18 -0.853 -0.757 19 -1.147 -1.012 20 1.619 1.372 21 2.078 1.480 22 0.603 0.213 23 0.919 1.086 24 -0.278 -0.889 25 0.598 0.729 26 -0.851 -1.432 27 -0.973 0.226 28 -1.911 -2.395 29 -0.199 -0.031 30 1.885 1.396 31 1.513 1.434 32 -0.623 -0.572 33 1.700 1.264 34 -0.692 0.467 35 0.691 0.711 36 -0.160 0.342 37 0.082 0.587 38 -1.236 -0.909 39 0.739 0.852 40 -0.233 -1.548 41 1.154 1.032 42 -0.718 -0.549 43 -0.226 -0.306 44 0.386 0.825 45 -1.205 -0.722 46 0.959 1.073 47 -0.640 -0.439 48 0.619 1.023 49 1.686 1.178 50 -0.523 -0.831 51 1.360 1.069 52 -0.472 0.369 53 -1.323 -1.187 54 -0.557 -0.367 55 0.445 0.512 56 -0.123 0.027 57 -1.085 -0.342 58 -0.439 -0.676 59 0.773 0.848 60 0.364 0.653 61 2.078 1.480 62 1.764 0.747 63 0.574 0.020 64 1.155 0.747 65 0.666 0.349 66 1.126 1.006 67 -1.637 -2.345 68 1.432 1.023 69 0.228 0.512 70 -0.878 -0.646 71 1.130 1.002 72 1.797 1.239 73 -0.586 -0.096 74 0.061 0.772 75 0.314 0.837 76 -1.244 -1.180 77 -1.335 -0.936 78 0.295 0.388 79 0.566 0.846 80 -0.011 -0.723 81 0.712 1.127 82 -0.058 0.381 83 -0.001 -0.002 84 -1.614 -1.230 85 0.303 0.263 86 -0.336 0.312 87 0.213 -0.568 88 1.392 1.104 89 1.269 0.848 90 1.553 1.158 91 0.072 0.093 92 0.555 0.097 93 0.443 0.160 94 -0.840 -0.713 95 0.654 0.764 96 -1.151 -1.436 97 0.907 0.983 98 -0.339 -0.029 99 0.783 0.635 100 0.852 0.335 101 1.373 1.169 102 0.123 0.568 103 -0.584 -0.134 104 0.806 0.902 105 0.794 0.556 106 1.659 1.359 107 -0.136 0.642 108 0.921 0.209 109 1.621 1.163 110 1.584 1.397 111 0.589 0.627 112 1.893 1.348 113 1.747 1.423 114 1.322 0.814 115 -0.778 -0.304 116 2.078 1.480 117 0.824 0.540 118 0.712 0.467 119 -0.587 -1.883 120 0.880 0.877 121 1.261 1.210 122 0.852 1.011 123 1.651 1.407 124 0.144 -0.523 125 0.958 1.280 126 1.466 1.275 127 0.411 0.124 128 1.621 1.163 129 1.127 1.080 130 1.653 1.198 131 -0.608 0.128 132 1.846 1.282 133 0.361 0.669 134 -0.293 -0.574 135 1.125 1.215 136 -0.045 0.374 137 0.217 0.524 138 1.312 0.565 139 0.118 0.779 140 0.488 0.575 141 -1.383 -1.188 142 0.002 0.264 143 0.217 0.261 144 0.938 0.651 145 0.509 0.585 146 0.408 0.656 147 0.102 0.304 148 -1.347 -0.642 149 0.357 0.696 150 1.741 0.935 151 0.880 0.877 152 1.661 1.150 153 0.920 1.041 154 0.973 0.983 155 1.549 1.431 156 1.007 0.183 157 1.651 1.407 158 -0.917 0.068 159 -0.375 0.041 160 0.695 0.840 161 0.625 0.981 162 -0.462 -0.085 163 0.928 0.993 164 1.619 1.372 165 -1.083 -2.127 166 1.585 1.188 167 -0.248 -0.577 168 1.427 1.161 169 0.645 1.037 170 0.231 0.247 171 0.035 -0.017 172 -0.944 -0.716 173 0.235 0.453 174 0.366 0.628 175 -0.021 0.357 176 0.488 0.440 177 0.353 0.764 178 0.509 0.699 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1113 0.126 0.731 1114 -0.815 -0.778 1115 0.341 0.990 1116 2.078 1.480 1117 1.668 1.229 1118 0.602 0.940 1119 0.005 0.236 1120 0.784 0.578 1121 0.498 0.835 1122 1.436 1.031 1123 1.298 1.207 1124 1.330 0.684 1125 1.608 0.500 1126 0.502 0.282 1127 0.053 0.062 1128 -0.067 0.296 1129 0.966 0.557 1130 0.049 0.657 1131 0.293 -0.160 1132 -0.971 -2.116 1133 -0.141 0.426 1134 -1.679 -2.197 1135 -0.757 -0.771 1136 2.078 1.480 1137 -0.861 -2.189 1138 0.904 0.181 1139 1.326 1.103 1140 2.078 1.480 1141 -1.708 -1.136 1142 0.481 0.610 1143 0.423 0.318 1144 -1.086 -0.548 1145 -1.426 -1.306 1146 -1.426 -1.306 1147 -0.695 -0.230 1148 -0.825 -0.713 1149 -0.551 -1.187 1150 -1.120 -1.895 1151 0.355 0.014 1152 0.404 0.494 1153 1.356 1.347 1154 -0.695 -0.230 1155 -1.426 -1.306 1156 -1.086 -0.548 1157 -1.120 -1.895 1158 0.355 0.014 1159 -0.842 -0.935 1160 0.236 0.084 1161 -0.453 -0.252 1162 -1.426 -1.306 1163 0.914 0.871 1164 1.153 1.111 1165 0.073 0.057 1166 0.476 0.471 1167 -0.075 0.456 1168 -0.554 0.077 1169 0.021 0.442 1170 0.871 0.986 1171 0.390 -0.022 1172 0.274 0.399 1173 0.541 0.676 1174 -0.925 -1.481 1175 1.375 1.105 1176 0.732 0.559 1177 -0.681 -0.007 1178 -0.071 -0.329 1179 0.776 0.151 1180 0.239 -0.378 1181 0.239 -0.378 1182 -1.617 -2.271 1183 -1.381 -0.412 1184 0.556 0.482 1185 0.378 0.095 $loads 1 2 wmt9 0.293 0.034 wmt7 0.258 0.074 wmt15 0.247 0.034 wmt14 0.243 0.030 wmt6 0.241 0.140 wmt16 0.220 0.054 wmt8 0.219 0.065 wmt10 0.206 0.166 wmt12 0.187 0.067 wmt18 0.183 -0.006 wmt13 0.170 0.050 wmt17 0.168 0.097 wmt11 0.155 0.075 wmt2 0.088 0.383 wmt1 0.064 0.254 wmt3 0.131 0.217 wmt5 0.142 0.201 wmt4 0.140 0.188 attr(,"class") [1] "NetScores" > > ## Not run: > ##D # Produce Methods section > ##D methods.section( > ##D ega.wmt, > ##D stats = "net.scores" > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("network.descriptives") > ### * network.descriptives > > flush(stderr()); flush(stdout()) > > ### Name: network.descriptives > ### Title: Descriptive Statistics for Networks > ### Aliases: network.descriptives > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # EGA example > ##D ega.wmt <- EGA(data = wmt) > ## End(Not run) > > # Compute descriptives > network.descriptives(ega.wmt) Weights: Mean = 0.082 Standard Deviation = 0.06 Range = -0.013 to 0.363 Density = 0.627 Global Properties: Average Shortest Path Length (ASPL) = 1.373 Clustering Coefficient (CC) = 0.604 Small-world (Telesford et al., 2011) = 0.256 R-squared Fit for Scale-free Network = 0.005 --- Small-world (Telesford et al., 2011): -1 'lattice' 0 'random' 1; near 0 = small-world> > > > > cleanEx() > nameEx("optimism") > ### * optimism > > flush(stderr()); flush(stdout()) > > ### Name: optimism > ### Title: Optimism Data > ### Aliases: optimism > ### Keywords: datasets > > ### ** Examples > > data("optimism") > > > > > cleanEx() > nameEx("prime.num") > ### * prime.num > > flush(stderr()); flush(stdout()) > > ### Name: prime.num > ### Title: Prime Numbers through 100,000 > ### Aliases: prime.num > ### Keywords: datasets > > ### ** Examples > > data("prime.num") > > > > > cleanEx() > nameEx("residualEGA") > ### * residualEGA > > flush(stderr()); flush(stdout()) > > ### Name: residualEGA > ### Title: Residualized 'EGA' > ### Aliases: residualEGA > > ### ** Examples > > # Load data > data <- optimism > > ## Not run: > ##D # Residual EGA example > ##D opt.res <- residualEGA(data = data, manifests = colnames(optimism), > ##D lat = 1, negative.items = c(3,7,9)) > ##D > ##D # Fit: > ##D opt.res$Fit > ## End(Not run) > > > > > cleanEx() > nameEx("riEGA") > ### * riEGA > > flush(stderr()); flush(stdout()) > > ### Name: riEGA > ### Title: Random-Intercept 'EGA' > ### Aliases: riEGA > > ### ** Examples > > # Obtain example data > data <- optimism > > ## Not run: > ##D # riEGA example > ##D opt.res <- riEGA(data = optimism) > ## End(Not run) > > > > > cleanEx() > nameEx("sim.dynEGA") > ### * sim.dynEGA > > flush(stderr()); flush(stdout()) > > ### Name: sim.dynEGA > ### Title: sim.dynEGA Data > ### Aliases: sim.dynEGA > ### Keywords: datasets > > ### ** Examples > > data("sim.dynEGA") > > > > > cleanEx() > nameEx("simDFM") > ### * simDFM > > flush(stderr()); flush(stdout()) > > ### Name: simDFM > ### Title: Simulate data following a Dynamic Factor Model > ### Aliases: simDFM > > ### ** Examples > > ## Not run: > ##D # Estimate EGA network > ##D data1 <- simDFM(variab = 5, timep = 50, nfact = 3, error = 0.05, > ##D dfm = "DAFS", loadings = 0.7, autoreg = 0.8, > ##D crossreg = 0.1, var.shock = 0.18, > ##D cov.shock = 0.36, burnin = 1000) > ## End(Not run) > > > > > cleanEx() > nameEx("tefi") > ### * tefi > > flush(stderr()); flush(stdout()) > > ### Name: tefi > ### Title: Total Entropy Fit Index using Von Neumman's entropy (Quantum > ### Information Theory) for correlation matrices > ### Aliases: tefi > > ### ** Examples > > # Load data > wmt <- wmt2[,7:24] > > ## Not run: > ##D # Estimate EGA model > ##D ega.wmt <- EGA(data = wmt, model = "glasso") > ## End(Not run) > > # Compute entropy indices > tefi(data = ega.wmt$correlation, structure = ega.wmt$wc) VN.Entropy.Fit Total.Correlation Average.Entropy 1 -11.17103 -2.695963 -14.9837 > > > > > cleanEx() > nameEx("totalCor") > ### * totalCor > > flush(stderr()); flush(stdout()) > > ### Name: totalCor > ### Title: Total Correlation > ### Aliases: totalCor > > ### ** Examples > > # Compute total correlation > totalCor(wmt2[,7:24]) $Ind.Entropies [1] 0.6248172 0.4848127 0.5703028 0.6852211 0.6248172 0.6540949 0.6795459 [8] 0.6889107 0.6852211 0.6686914 0.6323409 0.6261038 0.6811750 0.6879249 [15] 0.6329467 0.6410914 0.5107527 0.5738498 $Joint.Entropy [1] 6.818678 $Total.Cor [1] 4.533942 > > > > > cleanEx() > nameEx("totalCorMat") > ### * totalCorMat > > flush(stderr()); flush(stdout()) > > ### Name: totalCorMat > ### Title: Total Correlation Matrix > ### Aliases: totalCorMat > > ### ** Examples > > ## Not run: > ##D # Compute total correlation > ##D totalCorMat(wmt2[,7:24]) > ## End(Not run) > > > > > cleanEx() > nameEx("toy.example") > ### * toy.example > > flush(stderr()); flush(stdout()) > > ### Name: toy.example > ### Title: Toy Example Data > ### Aliases: toy.example > ### Keywords: datasets > > ### ** Examples > > data("toy.example") > > > > > cleanEx() > nameEx("vn.entropy") > ### * vn.entropy > > flush(stderr()); flush(stdout()) > > ### Name: vn.entropy > ### Title: Entropy Fit Index using Von Neumman's entropy (Quantum > ### Information Theory) for correlation matrices > ### Aliases: vn.entropy > > ### ** Examples > > # Select Five Factor Model personality items only > idx <- na.omit(match(gsub("-", "", unlist(psychTools::spi.keys[1:5])), colnames(psychTools::spi))) > items <- psychTools::spi[,idx] > > ## Not run: > ##D # Estimate EGA > ##D ega.spi <- EGA(data = items, model = "glasso") > ##D > ##D # Compute entropy indices > ##D vn.entropy( > ##D data = ega.spi$correlation, > ##D structure = ega.spi$wc > ##D ) > ## End(Not run) > > > > > cleanEx() > nameEx("wmt2") > ### * wmt2 > > flush(stderr()); flush(stdout()) > > ### Name: wmt2 > ### Title: WMT-2 Data > ### Aliases: wmt2 > ### Keywords: datasets > > ### ** Examples > > data("wmt2") > > > > > ### *