#1. Setup
rm (list = ls ())# Sets pseudo random number generator seed to fixed value for reproducibility set.seed (1 ):: restore (project = here:: here ())
- The library is already synchronized with the lockfile.
options (pkgType = "binary" )# Check if package 'here' is installed; if not, install. if (! require ("here" )) install.packages ("here" )library (here)
Loading required package: here
here() starts at C:/Users/Gaming/Desktop/empathy_network
if (! require ("pacman" )) install.packages ("pacman" )library (here)
Loading required package: pacman
# Increase timeout for downloading packages in case of slow download speed options (timeout = 300 )# Load all required packages and update to ensure compatibility :: p_load (#renv::snapshot() options (scipen = 999 ) # Remove scientific notations options (es.use_symbols = TRUE )
#2. Data Load
# load analysis-ready data <- read.csv (here ("data" ,"data_clean" ,"em_net_analysis_data.csv" ))<- data %>% select (id, age_group_t1)<- data %>% select (id, age_group_t2)<- data %>% pivot_longer (cols = matches ("_t[12]$" ),names_to = c (".value" , "time" ),names_pattern = "(.*)_t([12])" %>% arrange (id, time) %>% mutate (time = factor (time, levels = c ("1" , "2" ), labels = c ("T1" , "T2" )))<- c ("id" , "site" , "language" , "gender" , "time" , "age_group" )<- setdiff (names (data_long), id_vars)<- data_long %>% mutate (across (all_of (vars_to_transform),~ as.numeric (huge.npn (matrix (.x, ncol = 1 ), verbose = FALSE ))<- data %>% :: select (ends_with ("t1" ), - c (age_group_t1)) %>% :: huge.npn () %>% as.data.frame () %>% rename_with (~ str_remove (., "_t1$" ))
Conducting the nonparanormal (npn) transformation via shrunkun ECDF....done.
<- data %>% select (ends_with ("t2" ), - c (age_group_t2)) %>% :: huge.npn () %>% as.data.frame () %>% rename_with (~ str_remove (., "_t2$" ))
Conducting the nonparanormal (npn) transformation via shrunkun ECDF....done.
#3. Network Model Estimation
<- estimateNetwork (data_t1,corMethod = "cor_auto" ,corArgs = list (detectOrdinal = FALSE ),default = "EBICglasso" , tuning = 0.5 ,missing = "fiml"
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
<- estimateNetwork (data_t2,corMethod = "cor_auto" ,corArgs = list (detectOrdinal = FALSE ),default = "EBICglasso" , tuning = 0.5 ,missing = "fiml"
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
#4. Post-Modeling Assumption Checks
???
#5. Network Description
print (network_ebicglasso_t1)
=== Estimated network ===
Number of nodes: 17
Number of non-zero edges: 43 / 136
Mean weight: 0.01928795
Network stored in object$graph
Default set used: EBICglasso
Use plot(object) to plot estimated network
Use bootnet(object) to bootstrap edge weights and centrality indices
Relevant references:
Friedman, J. H., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9 (3), 432-441.
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models.
Friedman, J. H., Hastie, T., & Tibshirani, R. (2014). glasso: Graphical lasso estimation of gaussian graphical models. Retrieved from https://CRAN.R-project.org/package=glasso
Epskamp, S., Cramer, A., Waldorp, L., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48 (1), 1-18.
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: a tutorial paper. Multivariate Behavioral Research, 50(1), 195-212.
print (network_ebicglasso_t2)
=== Estimated network ===
Number of nodes: 17
Number of non-zero edges: 34 / 136
Mean weight: 0.0180876
Network stored in object$graph
Default set used: EBICglasso
Use plot(object) to plot estimated network
Use bootnet(object) to bootstrap edge weights and centrality indices
Relevant references:
Friedman, J. H., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9 (3), 432-441.
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models.
Friedman, J. H., Hastie, T., & Tibshirani, R. (2014). glasso: Graphical lasso estimation of gaussian graphical models. Retrieved from https://CRAN.R-project.org/package=glasso
Epskamp, S., Cramer, A., Waldorp, L., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48 (1), 1-18.
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: a tutorial paper. Multivariate Behavioral Research, 50(1), 195-212.
<- qgraph:: centralityTable (network_ebicglasso_t1) %>% filter (measure == "ExpectedInfluence" )#what scale is expected influence on?
graph type node measure value
1 graph 1 NA ht ExpectedInfluence 0.36488269
2 graph 1 NA ec ExpectedInfluence 0.62674678
3 graph 1 NA pa ExpectedInfluence -0.05301127
4 graph 1 NA sd ExpectedInfluence -1.90847666
5 graph 1 NA a ExpectedInfluence -1.12829547
6 graph 1 NA tom ExpectedInfluence 1.10566140
7 graph 1 NA flanker ExpectedInfluence 0.22127117
8 graph 1 NA tec ExpectedInfluence 0.68335732
9 graph 1 NA wm ExpectedInfluence 0.97001505
10 graph 1 NA dccs ExpectedInfluence -0.12202181
11 graph 1 NA cbq_na ExpectedInfluence -1.86619954
12 graph 1 NA cbq_att ExpectedInfluence -0.59539243
13 graph 1 NA cbq_shy ExpectedInfluence -1.27002424
14 graph 1 NA emque_ec ExpectedInfluence 0.42878068
15 graph 1 NA emque_af ExpectedInfluence 0.66378850
16 graph 1 NA emque_pa ExpectedInfluence 0.83488181
17 graph 1 NA csus_pt ExpectedInfluence 1.04403601
:: centralityPlot (network_ebicglasso_t1,include = "ExpectedInfluence" )
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_path()`).
Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
:: centralityPlot (network_ebicglasso_t2,include = "ExpectedInfluence" )
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_path()`).
Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
<- qgraph:: centralityTable (network_ebicglasso_t2) %>% filter (measure == "ExpectedInfluence" )
graph type node measure value
1 graph 1 NA ht ExpectedInfluence 0.55234513
2 graph 1 NA ec ExpectedInfluence 0.73855876
3 graph 1 NA pa ExpectedInfluence -0.02689061
4 graph 1 NA sd ExpectedInfluence -2.06919741
5 graph 1 NA a ExpectedInfluence -1.34068770
6 graph 1 NA tom ExpectedInfluence 0.70307530
7 graph 1 NA flanker ExpectedInfluence 0.17433138
8 graph 1 NA tec ExpectedInfluence 0.62986172
9 graph 1 NA wm ExpectedInfluence 1.44827525
10 graph 1 NA dccs ExpectedInfluence 0.03534386
11 graph 1 NA cbq_na ExpectedInfluence -1.68538427
12 graph 1 NA cbq_att ExpectedInfluence -0.56954032
13 graph 1 NA cbq_shy ExpectedInfluence -1.12018275
14 graph 1 NA emque_ec ExpectedInfluence 0.69784023
15 graph 1 NA emque_af ExpectedInfluence 0.55222052
16 graph 1 NA emque_pa ExpectedInfluence 0.73656857
17 graph 1 NA csus_pt ExpectedInfluence 0.54346236
<- qgraph:: centrality (network_ebicglasso_t1, R2 = TRUE )<- centrality_t1$ R2<- qgraph:: centrality (network_ebicglasso_t2, R2 = TRUE )<- centrality_t2$ R2
<- mean (network_ebicglasso_t1$ graph[upper.tri (network_ebicglasso_t1$ graph)] != 0 )<- (NetworkToolbox:: conn (getWmat (network_ebicglasso_t1)))$ density<- mean (network_ebicglasso_t2$ graph[upper.tri (network_ebicglasso_t2$ graph)] != 0 )<- (NetworkToolbox:: conn (getWmat (network_ebicglasso_t2)))$ density
<- (NetworkToolbox:: conn (getWmat (network_ebicglasso_t1)))$ total<- sum (abs (network_ebicglasso_t1$ graph[upper.tri (network_ebicglasso_t1$ graph)]))<- (NetworkToolbox:: conn (getWmat (network_ebicglasso_t2)))$ total<- sum (abs (network_ebicglasso_t2$ graph[upper.tri (network_ebicglasso_t2$ graph)]))
<- mean (network_ebicglasso_t1$ graph[upper.tri (network_ebicglasso_t1$ graph)])<- mean (network_ebicglasso_t2$ graph[upper.tri (network_ebicglasso_t2$ graph)])
#6. Network Plotting
<- c ("HT" , "EC" , "PA" , "SD" , "AV" , "ToM" , "Flank" , "TEC" , "WM" , "DCCS" , "NA" , "ATT" , "EXT" , "EC" , "AF" , "PA" , "PT" )<- c ("Hypothesis Testing" ,"Empathic Concern" , "Prosocial Acts" ,"Self-Distress" ,"Avoidance" ,"Theory of Mind" ,"Flanker" ,"Test of Emotion Comprehension" ,"Working Memory" ,"Dimensional Change Card Sort" ,"Negative Affectivity" ,"Effortful Control of Attention" ,"Extraversion" ,"Emotion Contagion" ,"Attention to Others' Feelings" ,"Prosocial Actions" ,"Perspective-Taking" <- structure (list ("Pain Simulation Task" = (1 : 5 ), # Assigning groupings (domains) to node/column numbers "Executive Functioning Battery" = c (7 , 9 , 10 ),"Perspective-Taking Battery" = c (6 , 8 ),"Children's Behavior Questionnaire" = c (11 : 13 ),"Empathy Questionnaire" = c (14 : 16 ),"Children's Social Understanding Scale" = c (17 )<- c ("#f8766d" , #Sociodemo "#c77cff" , #Premorbid "#00bfc4" , #EF "#7cae00" , #VF "#cd9600" ,"#cd3" ) #Visuo ##Customized ordering of ggplot2 palette <- averageLayout (network_ebicglasso_t1, network_ebicglasso_t2, layout = "spring" )
# jpeg(file = here("output", "figures", "em_net_ebicglasso_mds_t1.jpg"), # width = 1200, # The width of the plot in inches # height = 1200) # The height of the plot in inches # # L <- layout_with_mds(graph_from_adjacency_matrix(abs(getWmat(network_ebicglasso_t1)), # mode = "undirected", # weighted = TRUE, # diag = FALSE)) # # plot(network_ebicglasso_t1, # layout = L, # minimum = 0.05, # labels = labels, # pie = predictability_t1, # groups = groups, # nodeNames = names, # color = colors, # legend.mode = "style1", # borders = TRUE, # title = "Empathy Network Time 1", # title.cex = 1.5, # theme = "colorblind", # normalize = TRUE, # repulsion = 1.5, curveAll = TRUE, curveDefault = .25) # # # run dev.off() to create the file # dev.off() jpeg (file = here ("output" , "figures" , "em_net_ebicglasso_fr_t1.jpg" ),width = 15 ,height = 9 ,units = "in" ,res = 500 )<- plot (network_ebicglasso_t1,layout = avg_layout,labels = labels,minimum = 0.00 , pie = predictability_t1,groups = groups,nodeNames = names,color = colors,legend.mode = "style1" ,legend.cex = 0.7 ,borders = TRUE ,title = "Empathy Network Time 1" ,title.cex = 1.5 ,theme = "colorblind" ,normalize = TRUE # run dev.off() to create the file dev.off ()
# jpeg(file = here("output", "figures", "em_net_ebicglasso_mds_t2.jpg"), # width = 15, # height = 9, # units = "in", # res = 500) # # L <- layout_with_mds(graph_from_adjacency_matrix(abs(getWmat(network_ebicglasso_t2)), # mode = "undirected", # weighted = TRUE, # diag = FALSE)) # # plot(network_ebicglasso_t2, # layout = L, # minimum = 0.05, # labels = labels, # minimum = 0.05, # pie = predictability_t1, # groups = groups, # nodeNames = names, # color = colors, # legend.mode = "style1", # borders = TRUE, # title = "Empathy Network Time 2", # title.cex = 1.5, # theme = "colorblind", # normalize = TRUE, # repel = 1, # curveAll = TRUE) # # # run dev.off() to create the file # dev.off() jpeg (file = here ("output" , "figures" , "em_net_ebicglasso_fr_t2.jpg" ),width = 15 ,height = 9 ,units = "in" ,res = 500 )<- plot (network_ebicglasso_t2,layout = avg_layout,labels = labels,minimum = 0.00 , pie = predictability_t2,groups = groups,nodeNames = names,color = colors,legend.mode = "style1" ,legend.cex = 0.7 ,borders = TRUE ,title = "Empathy Network Time 2" ,title.cex = 1.5 ,theme = "colorblind" ,normalize = TRUE # run dev.off() to create the file dev.off ()
#7. Network Stability
if (file.exists (here ("output" , "models" , "networks" , "boot_edge_t1.rds" ))) {# load saved file <- readRDS (here ("output" , "models" , "networks" , "boot_edge_t1.rds" ))# summary results print (summary (boot_edge_t1))# Plot bootstrapped edge CIs: plot (boot_edge_t1, order = "sample" , labels = FALSE )else {# bootstrap 100 values, using 8 cores: <- bootnet (network_ebicglasso_t1,type = "nonparametric" ,nCores = 6 ,nBoots = 2500 )# save results saveRDS (boot_edge_t1, file = here ("output" , "models" , "networks" , "boot_edge_t1.rds" )) # summary results print (summary (boot_edge_t1))# Plot bootstrapped edge CIs: plot (boot_edge_t1, order = "sample" , labels = FALSE )
# A tibble: 153 × 17
# Groups: type, node1, node2 [153]
type id node1 node2 sample mean sd CIlower CIupper q2.5
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 edge a--cbq_att a cbq_… 0 -8.41e-4 0.00601 -0.0120 0.0120 -0.0134
2 edge a--cbq_na a cbq_… 0 -3.24e-3 0.0119 -0.0238 0.0238 -0.0461
3 edge a--cbq_shy a cbq_… 0 1.62e-3 0.00855 -0.0171 0.0171 0
4 edge a--csus_pt a csus… 0 5.27e-4 0.00455 -0.00909 0.00909 0
5 edge a--dccs a dccs 0.0406 3.04e-2 0.0385 -0.0364 0.118 0
6 edge a--emque_… a emqu… 0 -3.12e-4 0.00394 -0.00787 0.00787 0
7 edge a--emque_… a emqu… 0 -2.46e-4 0.00299 -0.00597 0.00597 0
8 edge a--emque_… a emqu… 0 1.25e-3 0.00678 -0.0136 0.0136 0
9 edge a--flanker a flan… 0 5.90e-4 0.00510 -0.0102 0.0102 0
10 edge a--tec a tec 0 -2.36e-4 0.00307 -0.00615 0.00615 0
# ℹ 143 more rows
# ℹ 7 more variables: q97.5 <dbl>, q2.5_non0 <dbl>, mean_non0 <dbl>,
# q97.5_non0 <dbl>, var_non0 <dbl>, sd_non0 <dbl>, prop0 <dbl>
if (file.exists (here ("output" , "models" , "networks" , "boot_edge_t2.rds" ))) {# load saved file <- readRDS (here ("output" , "models" , "networks" , "boot_edge_t2.rds" ))# summary results print (summary (boot_edge_t2))# Plot bootstrapped edge CIs: plot (boot_edge_t2, order = "sample" , labels = FALSE )else {# bootstrap 100 values, using 8 cores: <- bootnet (network_ebicglasso_t2,type = "nonparametric" ,nCores = 6 ,nBoots = 2500 )# save results saveRDS (boot_edge_t2, file = here ("output" , "models" , "networks" , "boot_edge_t2.rds" )) # summary results print (summary (boot_edge_t2))# Plot bootstrapped edge CIs: plot (boot_edge_t2, order = "sample" , labels = FALSE )
# A tibble: 153 × 17
# Groups: type, node1, node2 [153]
type id node1 node2 sample mean sd CIlower CIupper q2.5
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 edge a--cbq_att a cbq_… 0 5.14e-4 0.00639 -0.0128 0.0128 0
2 edge a--cbq_na a cbq_… 0 4.72e-5 0.00506 -0.0101 0.0101 0
3 edge a--cbq_shy a cbq_… 0 6.52e-4 0.00539 -0.0108 0.0108 0
4 edge a--csus_pt a csus… 0 -1.01e-3 0.00609 -0.0122 0.0122 -0.0180
5 edge a--dccs a dccs 0 1.62e-3 0.00873 -0.0175 0.0175 0
6 edge a--emque_… a emqu… 0 -1.26e-3 0.00711 -0.0142 0.0142 -0.0219
7 edge a--emque_… a emqu… 0 -7.16e-4 0.00555 -0.0111 0.0111 -0.0101
8 edge a--emque_… a emqu… 0 -9.61e-5 0.00270 -0.00539 0.00539 0
9 edge a--flanker a flan… 0 -5.71e-3 0.0162 -0.0323 0.0323 -0.0624
10 edge a--tec a tec 0 -2.86e-4 0.00407 -0.00814 0.00814 0
# ℹ 143 more rows
# ℹ 7 more variables: q97.5 <dbl>, q2.5_non0 <dbl>, mean_non0 <dbl>,
# q97.5_non0 <dbl>, var_non0 <dbl>, sd_non0 <dbl>, prop0 <dbl>
if (file.exists (here ("output" , "models" , "networks" , "boot_ei_t1.rds" ))) {# load saved file <- readRDS (here ("output" , "models" , "networks" , "boot_ei_t1.rds" ))# Compute CS-coefficient print (corStability (boot_ei_t1))# Plot centrality stability: plot (boot_ei_t1, statistics = "expectedInfluence" )else {# Bootstrap 1000 values, using 8 cores: <- bootnet (network_ebicglasso_t1, nBoots = 2500 , nCores = 6 ,type = "case" , statistics = "expectedInfluence" )# save results saveRDS (boot_ei_t1, file = here ("output" , "models" , "networks" , "boot_ei_t1.rds" ))# Compute CS-coefficient print (corStability (boot_ei_t1))# Plot centrality stability: plot (boot_ei_t1, statistics = "expectedInfluence" )
=== Correlation Stability Analysis ===
Sampling levels tested:
nPerson Drop% n
1 120 75.0 245
2 157 67.3 248
3 195 59.4 214
4 232 51.7 253
5 269 44.0 261
6 307 36.0 251
7 344 28.3 246
8 381 20.6 267
9 419 12.7 265
10 456 5.0 250
Maximum drop proportions to retain correlation of 0.7 in at least 95% of the samples:
expectedInfluence: 0.594
- For more accuracy, run bootnet(..., caseMin = 0.517, caseMax = 0.673)
Accuracy can also be increased by increasing both 'nBoots' and 'caseN'.expectedInfluence
0.59375
if (file.exists (here ("output" , "models" , "networks" , "boot_ei_t2.rds" ))) {# load saved file <- readRDS (here ("output" , "models" , "networks" , "boot_ei_t2.rds" ))# Compute CS-coefficient print (corStability (boot_ei_t2))# Plot centrality stability: plot (boot_ei_t2, statistics = "expectedInfluence" )else {# Bootstrap 1000 values, using 8 cores: <- bootnet (network_ebicglasso_t2, nBoots = 2500 , nCores = 6 ,type = "case" , statistics = "expectedInfluence" )# save results saveRDS (boot_ei_t2, file = here ("output" , "models" , "networks" , "boot_ei_t2.rds" ))# Compute CS-coefficient print (corStability (boot_ei_t2))# Plot centrality stability: plot (boot_ei_t2, statistics = "expectedInfluence" )
=== Correlation Stability Analysis ===
Sampling levels tested:
nPerson Drop% n
1 120 75.0 231
2 157 67.3 245
3 195 59.4 261
4 232 51.7 247
5 269 44.0 260
6 307 36.0 263
7 344 28.3 248
8 381 20.6 238
9 419 12.7 259
10 456 5.0 248
Maximum drop proportions to retain correlation of 0.7 in at least 95% of the samples:
expectedInfluence: 0.594
- For more accuracy, run bootnet(..., caseMin = 0.517, caseMax = 0.673)
Accuracy can also be increased by increasing both 'nBoots' and 'caseN'.expectedInfluence
0.59375
#8. Within-Time Network Comparison
<- data %>% filter (age_group_t1 == 4 ) %>% select (ends_with ("_t1" ), - c (id, site, language, gender, age_group_t1, age_group_t2))<- data %>% filter (age_group_t1 == 5 ) %>% select (ends_with ("_t1" ), - c (id, site, language, gender, age_group_t1, age_group_t2))<- data %>% filter (age_group_t1 == 6 ) %>% select (ends_with ("_t1" ), - c (id, site, language, gender, age_group_t1, age_group_t2))
<- data %>% filter (age_group_t2 == 5 ) %>% select (ends_with ("_t2" ), - c (id, site, language, gender, age_group_t1, age_group_t2))<- data %>% filter (age_group_t2 == 6 ) %>% select (ends_with ("_t2" ), - c (id, site, language, gender, age_group_t1, age_group_t2))<- data %>% filter (age_group_t2 == 7 ) %>% select (ends_with ("_t2" ), - c (id, site, language, gender, age_group_t1, age_group_t2))
<- estimateNetwork (data_t1_4,corMethod = "cor_auto" , default = "EBICglasso" , tuning = 0.5 ,missing = "fiml" ,corArgs = list (detectOrdinal = FALSE )
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
<- estimateNetwork (data_t1_5,corMethod = "cor_auto" , default = "EBICglasso" , tuning = 0.5 ,missing = "fiml" ,corArgs = list (detectOrdinal = FALSE )
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
<- estimateNetwork (data_t1_6,corMethod = "cor_auto" , default = "EBICglasso" , tuning = 0.5 ,missing = "fiml" ,corArgs = list (detectOrdinal = FALSE )
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
<- estimateNetwork (data_t2_5,corMethod = "cor_auto" , default = "EBICglasso" , tuning = 0.5 ,missing = "fiml" ,corArgs = list (detectOrdinal = FALSE )
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
<- estimateNetwork (data_t2_6,corMethod = "cor_auto" , default = "EBICglasso" , tuning = 0.5 ,missing = "fiml" ,corArgs = list (detectOrdinal = FALSE )
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
<- estimateNetwork (data_t2_7,corMethod = "cor_auto" , default = "EBICglasso" , tuning = 0.5 ,missing = "fiml" ,corArgs = list (detectOrdinal = FALSE )
Estimating Network. Using package::function:
- qgraph::EBICglasso for EBIC model selection
- using glasso::glasso
- qgraph::cor_auto for correlation computation
- using lavaan::lavCor
Network Comparison T1
if (file.exists (here ("output" , "models" , "networks" , "nct_4vs5_t1.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "nct_4vs5_t1.rds" ))summary (nct_4vs5_t1)plot (nct_4vs5_t1, what = "strength" )plot (nct_4vs5_t1, what = "network" )#plot(nct_4vs5_t1, what = "centrality") #plot(nct_4vs5_t1, what = "edge") else {nct_4vs5_t1 <- NetworkComparisonTest:: NCT (network_ebicglasso_t1_4,gamma = 0.5 ,it = 5000 ,abs = TRUE , edges = "yes" ,test.edges = TRUE ,test.centrality = TRUE ,centrality = "expectedInfluence" ,p.adjust.methods = "BH" )saveRDS (nct_4vs5_t1,file = here ("output" , "models" , "networks" , "nct_4vs5_t1.rds" ))summary (nct_4vs5_t1)plot (nct_4vs5_t1, what = "strength" )plot (nct_4vs5_t1, what = "network" )#plot(nct_4vs5_t1, what = "centrality") #plot(nct_4vs5_t1, what = "edge")
INDEPENDENT GROUPS GAUSSIAN NETWORK COMPARISON TEST
P-VALUE CORRECTION: BH
NETWORK INVARIANCE TEST
Test statistic M: 0.1678017
p-value 0.6314737
GLOBAL STRENGTH INVARIANCE TEST
Global strength per group: 1.627663 0.8833639
Test statistic S: 0.7442992
p-value 0.4613077
EDGE INVARIANCE TEST
Var1 Var2 p-value Test statistic E
18 ht_t1 ec_t1 1 0.07223540
35 ht_t1 pa_t1 1 0.07614103
36 ec_t1 pa_t1 1 0.09192608
52 ht_t1 sd_t1 1 0.00000000
53 ec_t1 sd_t1 1 0.02392299
54 pa_t1 sd_t1 1 0.02856591
69 ht_t1 a_t1 1 0.00000000
70 ec_t1 a_t1 1 0.00000000
71 pa_t1 a_t1 1 0.00000000
72 sd_t1 a_t1 1 0.00000000
86 ht_t1 tom_t1 1 0.00000000
87 ec_t1 tom_t1 1 0.00000000
88 pa_t1 tom_t1 1 0.00000000
89 sd_t1 tom_t1 1 0.00000000
90 a_t1 tom_t1 1 0.00000000
103 ht_t1 flanker_t1 1 0.00000000
104 ec_t1 flanker_t1 1 0.00000000
105 pa_t1 flanker_t1 1 0.00000000
106 sd_t1 flanker_t1 1 0.00000000
107 a_t1 flanker_t1 1 0.00000000
108 tom_t1 flanker_t1 1 0.00000000
120 ht_t1 tec_t1 1 0.00000000
121 ec_t1 tec_t1 1 0.00000000
122 pa_t1 tec_t1 1 0.00000000
123 sd_t1 tec_t1 1 0.00000000
124 a_t1 tec_t1 1 0.00000000
125 tom_t1 tec_t1 1 0.04285243
126 flanker_t1 tec_t1 1 0.00000000
137 ht_t1 wm_t1 1 0.00000000
138 ec_t1 wm_t1 1 0.00000000
139 pa_t1 wm_t1 1 0.00000000
140 sd_t1 wm_t1 1 0.00000000
141 a_t1 wm_t1 1 0.00000000
142 tom_t1 wm_t1 1 0.00000000
143 flanker_t1 wm_t1 1 0.00000000
144 tec_t1 wm_t1 1 0.00000000
154 ht_t1 dccs_t1 1 0.00000000
155 ec_t1 dccs_t1 1 0.00000000
156 pa_t1 dccs_t1 1 0.00000000
157 sd_t1 dccs_t1 1 0.00000000
158 a_t1 dccs_t1 1 0.00000000
159 tom_t1 dccs_t1 1 0.00000000
160 flanker_t1 dccs_t1 1 0.00000000
161 tec_t1 dccs_t1 1 0.00000000
162 wm_t1 dccs_t1 1 0.00000000
171 ht_t1 cbq_na_t1 1 0.00000000
172 ec_t1 cbq_na_t1 1 0.00000000
173 pa_t1 cbq_na_t1 1 0.00000000
174 sd_t1 cbq_na_t1 1 0.00000000
175 a_t1 cbq_na_t1 1 0.00000000
176 tom_t1 cbq_na_t1 1 0.00000000
177 flanker_t1 cbq_na_t1 1 0.00000000
178 tec_t1 cbq_na_t1 1 0.00000000
179 wm_t1 cbq_na_t1 1 0.00000000
180 dccs_t1 cbq_na_t1 1 0.00000000
188 ht_t1 cbq_att_t1 1 0.00000000
189 ec_t1 cbq_att_t1 1 0.00000000
190 pa_t1 cbq_att_t1 1 0.00000000
191 sd_t1 cbq_att_t1 1 0.00000000
192 a_t1 cbq_att_t1 1 0.00000000
193 tom_t1 cbq_att_t1 1 0.00000000
194 flanker_t1 cbq_att_t1 1 0.00000000
195 tec_t1 cbq_att_t1 1 0.00000000
196 wm_t1 cbq_att_t1 1 0.00000000
197 dccs_t1 cbq_att_t1 1 0.00000000
198 cbq_na_t1 cbq_att_t1 1 0.00000000
205 ht_t1 cbq_shy_t1 1 0.00000000
206 ec_t1 cbq_shy_t1 1 0.00000000
207 pa_t1 cbq_shy_t1 1 0.00000000
208 sd_t1 cbq_shy_t1 1 0.00000000
209 a_t1 cbq_shy_t1 1 0.00000000
210 tom_t1 cbq_shy_t1 1 0.00000000
211 flanker_t1 cbq_shy_t1 1 0.00000000
212 tec_t1 cbq_shy_t1 1 0.00000000
213 wm_t1 cbq_shy_t1 1 0.00000000
214 dccs_t1 cbq_shy_t1 1 0.00000000
215 cbq_na_t1 cbq_shy_t1 1 0.12855704
216 cbq_att_t1 cbq_shy_t1 1 0.00000000
222 ht_t1 emque_ec_t1 1 0.00000000
223 ec_t1 emque_ec_t1 1 0.00000000
224 pa_t1 emque_ec_t1 1 0.00000000
225 sd_t1 emque_ec_t1 1 0.00000000
226 a_t1 emque_ec_t1 1 0.00000000
227 tom_t1 emque_ec_t1 1 0.00000000
228 flanker_t1 emque_ec_t1 1 0.00000000
229 tec_t1 emque_ec_t1 1 0.00000000
230 wm_t1 emque_ec_t1 1 0.00000000
231 dccs_t1 emque_ec_t1 1 0.00000000
232 cbq_na_t1 emque_ec_t1 1 0.00000000
233 cbq_att_t1 emque_ec_t1 1 0.00000000
234 cbq_shy_t1 emque_ec_t1 1 0.00000000
239 ht_t1 emque_af_t1 1 0.00000000
240 ec_t1 emque_af_t1 1 0.00000000
241 pa_t1 emque_af_t1 1 0.00000000
242 sd_t1 emque_af_t1 1 0.00000000
243 a_t1 emque_af_t1 1 0.00000000
244 tom_t1 emque_af_t1 1 0.00000000
245 flanker_t1 emque_af_t1 1 0.00000000
246 tec_t1 emque_af_t1 1 0.00000000
247 wm_t1 emque_af_t1 1 0.00000000
248 dccs_t1 emque_af_t1 1 0.00000000
249 cbq_na_t1 emque_af_t1 1 0.00000000
250 cbq_att_t1 emque_af_t1 1 0.00000000
251 cbq_shy_t1 emque_af_t1 1 0.00000000
252 emque_ec_t1 emque_af_t1 1 0.05317371
256 ht_t1 emque_pa_t1 1 0.00000000
257 ec_t1 emque_pa_t1 1 0.00000000
258 pa_t1 emque_pa_t1 1 0.00000000
259 sd_t1 emque_pa_t1 1 0.00000000
260 a_t1 emque_pa_t1 1 0.00000000
261 tom_t1 emque_pa_t1 1 0.00000000
262 flanker_t1 emque_pa_t1 1 0.00000000
263 tec_t1 emque_pa_t1 1 0.00000000
264 wm_t1 emque_pa_t1 1 0.00000000
265 dccs_t1 emque_pa_t1 1 0.00000000
266 cbq_na_t1 emque_pa_t1 1 0.00000000
267 cbq_att_t1 emque_pa_t1 1 0.00000000
268 cbq_shy_t1 emque_pa_t1 1 0.00000000
269 emque_ec_t1 emque_pa_t1 1 0.16780171
270 emque_af_t1 emque_pa_t1 1 0.10887133
273 ht_t1 csus_pt_t1 1 0.00000000
274 ec_t1 csus_pt_t1 1 0.00000000
275 pa_t1 csus_pt_t1 1 0.00000000
276 sd_t1 csus_pt_t1 1 0.00000000
277 a_t1 csus_pt_t1 1 0.00000000
278 tom_t1 csus_pt_t1 1 0.00000000
279 flanker_t1 csus_pt_t1 1 0.00000000
280 tec_t1 csus_pt_t1 1 0.00000000
281 wm_t1 csus_pt_t1 1 0.04029740
282 dccs_t1 csus_pt_t1 1 0.00000000
283 cbq_na_t1 csus_pt_t1 1 0.00000000
284 cbq_att_t1 csus_pt_t1 1 0.00000000
285 cbq_shy_t1 csus_pt_t1 1 0.00000000
286 emque_ec_t1 csus_pt_t1 1 0.00000000
287 emque_af_t1 csus_pt_t1 1 0.00000000
288 emque_pa_t1 csus_pt_t1 1 0.01493197
CENTRALITY INVARIANCE TEST
Nodes tested: ht_t1 ec_t1 pa_t1 sd_t1 a_t1 tom_t1 flanker_t1 tec_t1 wm_t1 dccs_t1 cbq_na_t1 cbq_att_t1 cbq_shy_t1 emque_ec_t1 emque_af_t1 emque_pa_t1 csus_pt_t1
Centralities tested: expectedInfluence
Test statistics C:
expectedInfluence
ht_t1 0.14837643
ec_t1 0.18808448
pa_t1 0.19663302
sd_t1 0.05248890
a_t1 0.00000000
tom_t1 0.04285243
flanker_t1 0.00000000
tec_t1 0.04285243
wm_t1 0.04029740
dccs_t1 0.00000000
cbq_na_t1 -0.12855704
cbq_att_t1 0.00000000
cbq_shy_t1 -0.12855704
emque_ec_t1 0.22097542
emque_af_t1 0.16204504
emque_pa_t1 0.29160501
csus_pt_t1 0.05522937
p-values:
expectedInfluence
ht_t1 0.7225444
ec_t1 0.7225444
pa_t1 0.7225444
sd_t1 1.0000000
a_t1 1.0000000
tom_t1 0.7676798
flanker_t1 1.0000000
tec_t1 0.7225444
wm_t1 0.7561942
dccs_t1 1.0000000
cbq_na_t1 0.7225444
cbq_att_t1 1.0000000
cbq_shy_t1 0.7225444
emque_ec_t1 0.7225444
emque_af_t1 0.7561942
emque_pa_t1 0.7225444
csus_pt_t1 0.7225444
if (file.exists (here ("output" , "models" , "networks" , "nct_5vs6_t1.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "nct_5vs6_t1.rds" ))summary (nct_5vs6_t1)plot (nct_5vs6_t1, what = "strength" )plot (nct_5vs6_t1, what = "network" )#plot(nct_4vs6, what = "centrality") #plot(nct_4vs6, what = "edge") else {nct_5vs6_t1 <- NetworkComparisonTest:: NCT (network_ebicglasso_t1_5,gamma = 0.5 ,it = 5000 ,abs = TRUE , edges = "yes" ,test.edges = TRUE ,test.centrality = TRUE ,centrality = "expectedInfluence" ,p.adjust.methods = "BH" )saveRDS (nct_5vs6_t1,file = here ("output" , "models" , "networks" , "nct_5vs6_t1.rds" ))summary (nct_5vs6_t1)plot (nct_5vs6_t1, what = "strength" )plot (nct_5vs6_t1, what = "network" )#plot(nct_5vs6, what = "centrality") #plot(nct_5vs6, what = "edge")
INDEPENDENT GROUPS GAUSSIAN NETWORK COMPARISON TEST
P-VALUE CORRECTION: BH
NETWORK INVARIANCE TEST
Test statistic M: 0.2757188
p-value 0.1119776
GLOBAL STRENGTH INVARIANCE TEST
Global strength per group: 0.8833639 2.756422
Test statistic S: 1.873059
p-value 0.06178764
EDGE INVARIANCE TEST
Var1 Var2 p-value Test statistic E
18 ht_t1 ec_t1 1.00000000 0.05152787
35 ht_t1 pa_t1 0.70705859 0.08418545
36 ec_t1 pa_t1 0.72820991 0.23646091
52 ht_t1 sd_t1 1.00000000 0.00000000
53 ec_t1 sd_t1 1.00000000 0.11408036
54 pa_t1 sd_t1 1.00000000 0.08142363
69 ht_t1 a_t1 1.00000000 0.00000000
70 ec_t1 a_t1 0.71482846 0.13591175
71 pa_t1 a_t1 1.00000000 0.00000000
72 sd_t1 a_t1 1.00000000 0.00000000
86 ht_t1 tom_t1 0.17676465 0.03342778
87 ec_t1 tom_t1 1.00000000 0.00000000
88 pa_t1 tom_t1 1.00000000 0.00000000
89 sd_t1 tom_t1 1.00000000 0.00000000
90 a_t1 tom_t1 1.00000000 0.00000000
103 ht_t1 flanker_t1 1.00000000 0.00000000
104 ec_t1 flanker_t1 1.00000000 0.00000000
105 pa_t1 flanker_t1 1.00000000 0.00000000
106 sd_t1 flanker_t1 1.00000000 0.00000000
107 a_t1 flanker_t1 1.00000000 0.00000000
108 tom_t1 flanker_t1 1.00000000 0.00000000
120 ht_t1 tec_t1 1.00000000 0.00000000
121 ec_t1 tec_t1 1.00000000 0.00000000
122 pa_t1 tec_t1 1.00000000 0.00000000
123 sd_t1 tec_t1 1.00000000 0.00000000
124 a_t1 tec_t1 1.00000000 0.00000000
125 tom_t1 tec_t1 1.00000000 0.00000000
126 flanker_t1 tec_t1 1.00000000 0.00000000
137 ht_t1 wm_t1 1.00000000 0.00000000
138 ec_t1 wm_t1 1.00000000 0.00000000
139 pa_t1 wm_t1 1.00000000 0.00000000
140 sd_t1 wm_t1 1.00000000 0.00000000
141 a_t1 wm_t1 1.00000000 0.00000000
142 tom_t1 wm_t1 1.00000000 0.14910245
143 flanker_t1 wm_t1 1.00000000 0.00000000
144 tec_t1 wm_t1 1.00000000 0.05448537
154 ht_t1 dccs_t1 1.00000000 0.00000000
155 ec_t1 dccs_t1 1.00000000 0.00000000
156 pa_t1 dccs_t1 1.00000000 0.00000000
157 sd_t1 dccs_t1 1.00000000 0.00000000
158 a_t1 dccs_t1 1.00000000 0.00000000
159 tom_t1 dccs_t1 1.00000000 0.00000000
160 flanker_t1 dccs_t1 1.00000000 0.00000000
161 tec_t1 dccs_t1 1.00000000 0.00000000
162 wm_t1 dccs_t1 1.00000000 0.00000000
171 ht_t1 cbq_na_t1 1.00000000 0.00000000
172 ec_t1 cbq_na_t1 1.00000000 0.00000000
173 pa_t1 cbq_na_t1 1.00000000 0.00000000
174 sd_t1 cbq_na_t1 1.00000000 0.00000000
175 a_t1 cbq_na_t1 1.00000000 0.00000000
176 tom_t1 cbq_na_t1 0.08158368 0.03597011
177 flanker_t1 cbq_na_t1 1.00000000 0.00000000
178 tec_t1 cbq_na_t1 1.00000000 0.00000000
179 wm_t1 cbq_na_t1 1.00000000 0.00000000
180 dccs_t1 cbq_na_t1 1.00000000 0.00000000
188 ht_t1 cbq_att_t1 1.00000000 0.00000000
189 ec_t1 cbq_att_t1 1.00000000 0.00000000
190 pa_t1 cbq_att_t1 1.00000000 0.00000000
191 sd_t1 cbq_att_t1 1.00000000 0.00000000
192 a_t1 cbq_att_t1 1.00000000 0.00000000
193 tom_t1 cbq_att_t1 1.00000000 0.00000000
194 flanker_t1 cbq_att_t1 1.00000000 0.00000000
195 tec_t1 cbq_att_t1 1.00000000 0.00000000
196 wm_t1 cbq_att_t1 1.00000000 0.00000000
197 dccs_t1 cbq_att_t1 1.00000000 0.00000000
198 cbq_na_t1 cbq_att_t1 1.00000000 0.00000000
205 ht_t1 cbq_shy_t1 1.00000000 0.00000000
206 ec_t1 cbq_shy_t1 1.00000000 0.09906200
207 pa_t1 cbq_shy_t1 1.00000000 0.03466039
208 sd_t1 cbq_shy_t1 1.00000000 0.00000000
209 a_t1 cbq_shy_t1 1.00000000 0.00000000
210 tom_t1 cbq_shy_t1 1.00000000 0.00000000
211 flanker_t1 cbq_shy_t1 1.00000000 0.00000000
212 tec_t1 cbq_shy_t1 1.00000000 0.00000000
213 wm_t1 cbq_shy_t1 1.00000000 0.00000000
214 dccs_t1 cbq_shy_t1 1.00000000 0.00000000
215 cbq_na_t1 cbq_shy_t1 0.71725655 0.27571878
216 cbq_att_t1 cbq_shy_t1 1.00000000 0.00000000
222 ht_t1 emque_ec_t1 1.00000000 0.00000000
223 ec_t1 emque_ec_t1 1.00000000 0.00000000
224 pa_t1 emque_ec_t1 1.00000000 0.00000000
225 sd_t1 emque_ec_t1 1.00000000 0.00000000
226 a_t1 emque_ec_t1 1.00000000 0.00000000
227 tom_t1 emque_ec_t1 1.00000000 0.00000000
228 flanker_t1 emque_ec_t1 1.00000000 0.00000000
229 tec_t1 emque_ec_t1 1.00000000 0.00000000
230 wm_t1 emque_ec_t1 1.00000000 0.00000000
231 dccs_t1 emque_ec_t1 1.00000000 0.00000000
232 cbq_na_t1 emque_ec_t1 1.00000000 0.00000000
233 cbq_att_t1 emque_ec_t1 1.00000000 0.00000000
234 cbq_shy_t1 emque_ec_t1 1.00000000 0.00000000
239 ht_t1 emque_af_t1 1.00000000 0.00000000
240 ec_t1 emque_af_t1 1.00000000 0.00000000
241 pa_t1 emque_af_t1 1.00000000 0.00000000
242 sd_t1 emque_af_t1 1.00000000 0.00000000
243 a_t1 emque_af_t1 1.00000000 0.00000000
244 tom_t1 emque_af_t1 1.00000000 0.00000000
245 flanker_t1 emque_af_t1 1.00000000 0.00000000
246 tec_t1 emque_af_t1 1.00000000 0.00000000
247 wm_t1 emque_af_t1 1.00000000 0.00000000
248 dccs_t1 emque_af_t1 1.00000000 0.00000000
249 cbq_na_t1 emque_af_t1 1.00000000 0.00000000
250 cbq_att_t1 emque_af_t1 1.00000000 0.00000000
251 cbq_shy_t1 emque_af_t1 1.00000000 0.00000000
252 emque_ec_t1 emque_af_t1 1.00000000 0.07376341
256 ht_t1 emque_pa_t1 1.00000000 0.00000000
257 ec_t1 emque_pa_t1 1.00000000 0.00000000
258 pa_t1 emque_pa_t1 1.00000000 0.00000000
259 sd_t1 emque_pa_t1 1.00000000 0.00000000
260 a_t1 emque_pa_t1 1.00000000 0.00000000
261 tom_t1 emque_pa_t1 1.00000000 0.00000000
262 flanker_t1 emque_pa_t1 1.00000000 0.00000000
263 tec_t1 emque_pa_t1 1.00000000 0.00000000
264 wm_t1 emque_pa_t1 1.00000000 0.00000000
265 dccs_t1 emque_pa_t1 1.00000000 0.00000000
266 cbq_na_t1 emque_pa_t1 1.00000000 0.00000000
267 cbq_att_t1 emque_pa_t1 1.00000000 0.00000000
268 cbq_shy_t1 emque_pa_t1 1.00000000 0.00000000
269 emque_ec_t1 emque_pa_t1 0.39160168 0.12431940
270 emque_af_t1 emque_pa_t1 1.00000000 0.14042100
273 ht_t1 csus_pt_t1 1.00000000 0.00000000
274 ec_t1 csus_pt_t1 1.00000000 0.00000000
275 pa_t1 csus_pt_t1 1.00000000 0.00000000
276 sd_t1 csus_pt_t1 1.00000000 0.00000000
277 a_t1 csus_pt_t1 1.00000000 0.00000000
278 tom_t1 csus_pt_t1 1.00000000 0.00000000
279 flanker_t1 csus_pt_t1 1.00000000 0.00000000
280 tec_t1 csus_pt_t1 1.00000000 0.00000000
281 wm_t1 csus_pt_t1 1.00000000 0.00000000
282 dccs_t1 csus_pt_t1 1.00000000 0.00000000
283 cbq_na_t1 csus_pt_t1 0.39160168 0.10539442
284 cbq_att_t1 csus_pt_t1 1.00000000 0.00000000
285 cbq_shy_t1 csus_pt_t1 0.39160168 0.11155080
286 emque_ec_t1 csus_pt_t1 1.00000000 0.00000000
287 emque_af_t1 csus_pt_t1 1.00000000 0.05410490
288 emque_pa_t1 csus_pt_t1 1.00000000 0.10564847
CENTRALITY INVARIANCE TEST
Nodes tested: ht_t1 ec_t1 pa_t1 sd_t1 a_t1 tom_t1 flanker_t1 tec_t1 wm_t1 dccs_t1 cbq_na_t1 cbq_att_t1 cbq_shy_t1 emque_ec_t1 emque_af_t1 emque_pa_t1 csus_pt_t1
Centralities tested: expectedInfluence
Test statistics C:
expectedInfluence
ht_t1 -0.10228553
ec_t1 -0.36521938
pa_t1 -0.27388311
sd_t1 -0.03265673
a_t1 0.13591175
tom_t1 -0.07970455
flanker_t1 0.00000000
tec_t1 -0.05448537
wm_t1 -0.20358782
dccs_t1 0.00000000
cbq_na_t1 0.41708332
cbq_att_t1 0.00000000
cbq_shy_t1 0.03044560
emque_ec_t1 -0.19808281
emque_af_t1 -0.26828931
emque_pa_t1 -0.37038887
csus_pt_t1 -0.16590974
p-values:
expectedInfluence
ht_t1 0.67578484
ec_t1 0.28282344
pa_t1 0.22888756
sd_t1 1.00000000
a_t1 0.22888756
tom_t1 0.98693595
flanker_t1 1.00000000
tec_t1 0.98693595
wm_t1 0.67578484
dccs_t1 1.00000000
cbq_na_t1 0.03059388
cbq_att_t1 1.00000000
cbq_shy_t1 1.00000000
emque_ec_t1 0.55125642
emque_af_t1 0.59105679
emque_pa_t1 0.24135173
csus_pt_t1 0.59105679
if (file.exists (here ("output" , "models" , "networks" , "nct_4vs6_t1.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "nct_4vs6_t1.rds" ))summary (nct_4vs6_t1)plot (nct_4vs6_t1, what = "strength" )plot (nct_4vs6_t1, what = "network" )#plot(nct_4vs6, what = "centrality") #plot(nct_4vs6, what = "edge") else {<- NetworkComparisonTest:: NCT (network_ebicglasso_t1_4,gamma = 0.5 ,it = 5000 ,abs = TRUE , edges = "yes" ,test.edges = TRUE ,test.centrality = TRUE ,centrality = "expectedInfluence" ,p.adjust.methods = "none" )saveRDS (nct_4vs6_t1,file = here ("output" , "models" , "networks" , "nct_4vs6_t1.rds" ))summary (nct_4vs6_t1)plot (nct_4vs6_t1, what = "strength" )plot (nct_4vs6_t1, what = "network" )#plot(nct_4vs6, what = "centrality") # plot(nct_4vs6, what = "edge")
INDEPENDENT GROUPS GAUSSIAN NETWORK COMPARISON TEST
P-VALUE CORRECTION: none
NETWORK INVARIANCE TEST
Test statistic M: 0.1491025
p-value 0.914817
GLOBAL STRENGTH INVARIANCE TEST
Global strength per group: 1.627663 2.756422
Test statistic S: 1.128759
p-value 0.3983203
EDGE INVARIANCE TEST
Var1 Var2 p-value Test statistic E
18 ht_t1 ec_t1 0.82583483 0.02070753
35 ht_t1 pa_t1 0.93761248 0.00804442
36 ec_t1 pa_t1 0.09178164 0.14453482
52 ht_t1 sd_t1 1.00000000 0.00000000
53 ec_t1 sd_t1 0.35952809 0.09015738
54 pa_t1 sd_t1 0.24715057 0.10998954
69 ht_t1 a_t1 1.00000000 0.00000000
70 ec_t1 a_t1 0.10937812 0.13591175
71 pa_t1 a_t1 1.00000000 0.00000000
72 sd_t1 a_t1 1.00000000 0.00000000
86 ht_t1 tom_t1 0.00019996 0.03342778
87 ec_t1 tom_t1 1.00000000 0.00000000
88 pa_t1 tom_t1 1.00000000 0.00000000
89 sd_t1 tom_t1 1.00000000 0.00000000
90 a_t1 tom_t1 1.00000000 0.00000000
103 ht_t1 flanker_t1 1.00000000 0.00000000
104 ec_t1 flanker_t1 1.00000000 0.00000000
105 pa_t1 flanker_t1 1.00000000 0.00000000
106 sd_t1 flanker_t1 1.00000000 0.00000000
107 a_t1 flanker_t1 1.00000000 0.00000000
108 tom_t1 flanker_t1 1.00000000 0.00000000
120 ht_t1 tec_t1 1.00000000 0.00000000
121 ec_t1 tec_t1 1.00000000 0.00000000
122 pa_t1 tec_t1 1.00000000 0.00000000
123 sd_t1 tec_t1 1.00000000 0.00000000
124 a_t1 tec_t1 1.00000000 0.00000000
125 tom_t1 tec_t1 0.65926815 0.04285243
126 flanker_t1 tec_t1 1.00000000 0.00000000
137 ht_t1 wm_t1 1.00000000 0.00000000
138 ec_t1 wm_t1 1.00000000 0.00000000
139 pa_t1 wm_t1 1.00000000 0.00000000
140 sd_t1 wm_t1 1.00000000 0.00000000
141 a_t1 wm_t1 1.00000000 0.00000000
142 tom_t1 wm_t1 0.14137173 0.14910245
143 flanker_t1 wm_t1 1.00000000 0.00000000
144 tec_t1 wm_t1 0.57168566 0.05448537
154 ht_t1 dccs_t1 1.00000000 0.00000000
155 ec_t1 dccs_t1 1.00000000 0.00000000
156 pa_t1 dccs_t1 1.00000000 0.00000000
157 sd_t1 dccs_t1 1.00000000 0.00000000
158 a_t1 dccs_t1 1.00000000 0.00000000
159 tom_t1 dccs_t1 1.00000000 0.00000000
160 flanker_t1 dccs_t1 1.00000000 0.00000000
161 tec_t1 dccs_t1 1.00000000 0.00000000
162 wm_t1 dccs_t1 1.00000000 0.00000000
171 ht_t1 cbq_na_t1 1.00000000 0.00000000
172 ec_t1 cbq_na_t1 1.00000000 0.00000000
173 pa_t1 cbq_na_t1 1.00000000 0.00000000
174 sd_t1 cbq_na_t1 1.00000000 0.00000000
175 a_t1 cbq_na_t1 1.00000000 0.00000000
176 tom_t1 cbq_na_t1 0.01479704 0.03597011
177 flanker_t1 cbq_na_t1 1.00000000 0.00000000
178 tec_t1 cbq_na_t1 1.00000000 0.00000000
179 wm_t1 cbq_na_t1 1.00000000 0.00000000
180 dccs_t1 cbq_na_t1 1.00000000 0.00000000
188 ht_t1 cbq_att_t1 1.00000000 0.00000000
189 ec_t1 cbq_att_t1 1.00000000 0.00000000
190 pa_t1 cbq_att_t1 1.00000000 0.00000000
191 sd_t1 cbq_att_t1 1.00000000 0.00000000
192 a_t1 cbq_att_t1 1.00000000 0.00000000
193 tom_t1 cbq_att_t1 1.00000000 0.00000000
194 flanker_t1 cbq_att_t1 1.00000000 0.00000000
195 tec_t1 cbq_att_t1 1.00000000 0.00000000
196 wm_t1 cbq_att_t1 1.00000000 0.00000000
197 dccs_t1 cbq_att_t1 1.00000000 0.00000000
198 cbq_na_t1 cbq_att_t1 1.00000000 0.00000000
205 ht_t1 cbq_shy_t1 1.00000000 0.00000000
206 ec_t1 cbq_shy_t1 0.05418916 0.09906200
207 pa_t1 cbq_shy_t1 0.25374925 0.03466039
208 sd_t1 cbq_shy_t1 1.00000000 0.00000000
209 a_t1 cbq_shy_t1 1.00000000 0.00000000
210 tom_t1 cbq_shy_t1 1.00000000 0.00000000
211 flanker_t1 cbq_shy_t1 1.00000000 0.00000000
212 tec_t1 cbq_shy_t1 1.00000000 0.00000000
213 wm_t1 cbq_shy_t1 1.00000000 0.00000000
214 dccs_t1 cbq_shy_t1 1.00000000 0.00000000
215 cbq_na_t1 cbq_shy_t1 0.19616077 0.14716175
216 cbq_att_t1 cbq_shy_t1 1.00000000 0.00000000
222 ht_t1 emque_ec_t1 1.00000000 0.00000000
223 ec_t1 emque_ec_t1 1.00000000 0.00000000
224 pa_t1 emque_ec_t1 1.00000000 0.00000000
225 sd_t1 emque_ec_t1 1.00000000 0.00000000
226 a_t1 emque_ec_t1 1.00000000 0.00000000
227 tom_t1 emque_ec_t1 1.00000000 0.00000000
228 flanker_t1 emque_ec_t1 1.00000000 0.00000000
229 tec_t1 emque_ec_t1 1.00000000 0.00000000
230 wm_t1 emque_ec_t1 1.00000000 0.00000000
231 dccs_t1 emque_ec_t1 1.00000000 0.00000000
232 cbq_na_t1 emque_ec_t1 1.00000000 0.00000000
233 cbq_att_t1 emque_ec_t1 1.00000000 0.00000000
234 cbq_shy_t1 emque_ec_t1 1.00000000 0.00000000
239 ht_t1 emque_af_t1 1.00000000 0.00000000
240 ec_t1 emque_af_t1 1.00000000 0.00000000
241 pa_t1 emque_af_t1 1.00000000 0.00000000
242 sd_t1 emque_af_t1 1.00000000 0.00000000
243 a_t1 emque_af_t1 1.00000000 0.00000000
244 tom_t1 emque_af_t1 1.00000000 0.00000000
245 flanker_t1 emque_af_t1 1.00000000 0.00000000
246 tec_t1 emque_af_t1 1.00000000 0.00000000
247 wm_t1 emque_af_t1 1.00000000 0.00000000
248 dccs_t1 emque_af_t1 1.00000000 0.00000000
249 cbq_na_t1 emque_af_t1 1.00000000 0.00000000
250 cbq_att_t1 emque_af_t1 1.00000000 0.00000000
251 cbq_shy_t1 emque_af_t1 1.00000000 0.00000000
252 emque_ec_t1 emque_af_t1 0.84863027 0.02058970
256 ht_t1 emque_pa_t1 1.00000000 0.00000000
257 ec_t1 emque_pa_t1 1.00000000 0.00000000
258 pa_t1 emque_pa_t1 1.00000000 0.00000000
259 sd_t1 emque_pa_t1 1.00000000 0.00000000
260 a_t1 emque_pa_t1 1.00000000 0.00000000
261 tom_t1 emque_pa_t1 1.00000000 0.00000000
262 flanker_t1 emque_pa_t1 1.00000000 0.00000000
263 tec_t1 emque_pa_t1 1.00000000 0.00000000
264 wm_t1 emque_pa_t1 1.00000000 0.00000000
265 dccs_t1 emque_pa_t1 1.00000000 0.00000000
266 cbq_na_t1 emque_pa_t1 1.00000000 0.00000000
267 cbq_att_t1 emque_pa_t1 1.00000000 0.00000000
268 cbq_shy_t1 emque_pa_t1 1.00000000 0.00000000
269 emque_ec_t1 emque_pa_t1 0.68546291 0.04348231
270 emque_af_t1 emque_pa_t1 0.76664667 0.03154967
273 ht_t1 csus_pt_t1 1.00000000 0.00000000
274 ec_t1 csus_pt_t1 1.00000000 0.00000000
275 pa_t1 csus_pt_t1 1.00000000 0.00000000
276 sd_t1 csus_pt_t1 1.00000000 0.00000000
277 a_t1 csus_pt_t1 1.00000000 0.00000000
278 tom_t1 csus_pt_t1 1.00000000 0.00000000
279 flanker_t1 csus_pt_t1 1.00000000 0.00000000
280 tec_t1 csus_pt_t1 1.00000000 0.00000000
281 wm_t1 csus_pt_t1 0.70665867 0.04029740
282 dccs_t1 csus_pt_t1 1.00000000 0.00000000
283 cbq_na_t1 csus_pt_t1 0.04959008 0.10539442
284 cbq_att_t1 csus_pt_t1 1.00000000 0.00000000
285 cbq_shy_t1 csus_pt_t1 0.07498500 0.11155080
286 emque_ec_t1 csus_pt_t1 1.00000000 0.00000000
287 emque_af_t1 csus_pt_t1 0.04079184 0.05410490
288 emque_pa_t1 csus_pt_t1 0.44071186 0.09071650
CENTRALITY INVARIANCE TEST
Nodes tested: ht_t1 ec_t1 pa_t1 sd_t1 a_t1 tom_t1 flanker_t1 tec_t1 wm_t1 dccs_t1 cbq_na_t1 cbq_att_t1 cbq_shy_t1 emque_ec_t1 emque_af_t1 emque_pa_t1 csus_pt_t1
Centralities tested: expectedInfluence
Test statistics C:
expectedInfluence
ht_t1 0.04609090
ec_t1 -0.17713491
pa_t1 -0.07725009
sd_t1 0.01983217
a_t1 0.13591175
tom_t1 -0.03685212
flanker_t1 0.00000000
tec_t1 -0.01163294
wm_t1 -0.16329042
dccs_t1 0.00000000
cbq_na_t1 0.28852629
cbq_att_t1 0.00000000
cbq_shy_t1 -0.09811144
emque_ec_t1 0.02289261
emque_af_t1 -0.10624427
emque_pa_t1 -0.07878386
csus_pt_t1 -0.11068038
p-values:
expectedInfluence
ht_t1 0.73365327
ec_t1 0.29154169
pa_t1 0.62567487
sd_t1 0.89402120
a_t1 0.11737652
tom_t1 0.89402120
flanker_t1 1.00000000
tec_t1 0.95800840
wm_t1 0.51869626
dccs_t1 1.00000000
cbq_na_t1 0.04079184
cbq_att_t1 1.00000000
cbq_shy_t1 0.48690262
emque_ec_t1 0.89182164
emque_af_t1 0.55188962
emque_pa_t1 0.77464507
csus_pt_t1 0.75064987
Network Comparison T2
if (file.exists (here ("output" , "models" , "networks" , "nct_5vs6_t2.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "nct_5vs6_t2.rds" ))summary (nct_5vs6_t2)plot (nct_5vs6_t2, what = "strength" )plot (nct_5vs6_t2, what = "network" )#plot(nct_4vs6, what = "centrality") #plot(nct_4vs6, what = "edge") else { nct_5vs6_t2 <- NetworkComparisonTest:: NCT (network_ebicglasso_t2_5,gamma = 0.5 ,it = 5000 ,abs = TRUE , edges = "yes" ,test.edges = TRUE ,test.centrality = TRUE ,centrality = "expectedInfluence" ,p.adjust.methods = "BH" )saveRDS (nct_5vs6_t2,file = here ("output" , "models" , "networks" , "nct_5vs6_t2.rds" ))summary (nct_5vs6_t2)plot (nct_5vs6_t2, what = "strength" )plot (nct_5vs6_t2, what = "network" )#plot(nct_5vs6_t2, what = "centrality") #plot(nct_5vs6_t2, what = "edge")
INDEPENDENT GROUPS GAUSSIAN NETWORK COMPARISON TEST
P-VALUE CORRECTION: BH
NETWORK INVARIANCE TEST
Test statistic M: 0.116441
p-value 0.9580084
GLOBAL STRENGTH INVARIANCE TEST
Global strength per group: 1.234407 1.558107
Test statistic S: 0.3236996
p-value 0.7676465
EDGE INVARIANCE TEST
Var1 Var2 p-value Test statistic E
18 ht_t2 ec_t2 1 0.01864101
35 ht_t2 pa_t2 1 0.01346907
36 ec_t2 pa_t2 1 0.07568362
52 ht_t2 sd_t2 1 0.00000000
53 ec_t2 sd_t2 1 0.02182476
54 pa_t2 sd_t2 1 0.05674066
69 ht_t2 a_t2 1 0.00000000
70 ec_t2 a_t2 1 0.06076482
71 pa_t2 a_t2 1 0.00000000
72 sd_t2 a_t2 1 0.00000000
86 ht_t2 tom_t2 1 0.00000000
87 ec_t2 tom_t2 1 0.00000000
88 pa_t2 tom_t2 1 0.00000000
89 sd_t2 tom_t2 1 0.00000000
90 a_t2 tom_t2 1 0.00000000
103 ht_t2 flanker_t2 1 0.00000000
104 ec_t2 flanker_t2 1 0.00000000
105 pa_t2 flanker_t2 1 0.00000000
106 sd_t2 flanker_t2 1 0.00000000
107 a_t2 flanker_t2 1 0.00000000
108 tom_t2 flanker_t2 1 0.00000000
120 ht_t2 tec_t2 1 0.00000000
121 ec_t2 tec_t2 1 0.00000000
122 pa_t2 tec_t2 1 0.00000000
123 sd_t2 tec_t2 1 0.00000000
124 a_t2 tec_t2 1 0.00000000
125 tom_t2 tec_t2 1 0.00000000
126 flanker_t2 tec_t2 1 0.00000000
137 ht_t2 wm_t2 1 0.00000000
138 ec_t2 wm_t2 1 0.00000000
139 pa_t2 wm_t2 1 0.00000000
140 sd_t2 wm_t2 1 0.00000000
141 a_t2 wm_t2 1 0.00000000
142 tom_t2 wm_t2 1 0.11644105
143 flanker_t2 wm_t2 1 0.00000000
144 tec_t2 wm_t2 1 0.00000000
154 ht_t2 dccs_t2 1 0.00000000
155 ec_t2 dccs_t2 1 0.00000000
156 pa_t2 dccs_t2 1 0.00000000
157 sd_t2 dccs_t2 1 0.00000000
158 a_t2 dccs_t2 1 0.00000000
159 tom_t2 dccs_t2 1 0.00000000
160 flanker_t2 dccs_t2 1 0.00000000
161 tec_t2 dccs_t2 1 0.00000000
162 wm_t2 dccs_t2 1 0.00000000
171 ht_t2 cbq_na_t2 1 0.00000000
172 ec_t2 cbq_na_t2 1 0.00000000
173 pa_t2 cbq_na_t2 1 0.00000000
174 sd_t2 cbq_na_t2 1 0.00000000
175 a_t2 cbq_na_t2 1 0.00000000
176 tom_t2 cbq_na_t2 1 0.00000000
177 flanker_t2 cbq_na_t2 1 0.00000000
178 tec_t2 cbq_na_t2 1 0.00000000
179 wm_t2 cbq_na_t2 1 0.00000000
180 dccs_t2 cbq_na_t2 1 0.00000000
188 ht_t2 cbq_att_t2 1 0.00000000
189 ec_t2 cbq_att_t2 1 0.00000000
190 pa_t2 cbq_att_t2 1 0.00000000
191 sd_t2 cbq_att_t2 1 0.00000000
192 a_t2 cbq_att_t2 1 0.00000000
193 tom_t2 cbq_att_t2 1 0.00000000
194 flanker_t2 cbq_att_t2 1 0.00000000
195 tec_t2 cbq_att_t2 1 0.00000000
196 wm_t2 cbq_att_t2 1 0.00000000
197 dccs_t2 cbq_att_t2 1 0.00000000
198 cbq_na_t2 cbq_att_t2 1 0.00000000
205 ht_t2 cbq_shy_t2 1 0.00000000
206 ec_t2 cbq_shy_t2 1 0.00000000
207 pa_t2 cbq_shy_t2 1 0.00000000
208 sd_t2 cbq_shy_t2 1 0.00000000
209 a_t2 cbq_shy_t2 1 0.00000000
210 tom_t2 cbq_shy_t2 1 0.00000000
211 flanker_t2 cbq_shy_t2 1 0.00000000
212 tec_t2 cbq_shy_t2 1 0.00000000
213 wm_t2 cbq_shy_t2 1 0.00000000
214 dccs_t2 cbq_shy_t2 1 0.00000000
215 cbq_na_t2 cbq_shy_t2 1 0.05347205
216 cbq_att_t2 cbq_shy_t2 1 0.00000000
222 ht_t2 emque_ec_t2 1 0.00000000
223 ec_t2 emque_ec_t2 1 0.00000000
224 pa_t2 emque_ec_t2 1 0.00000000
225 sd_t2 emque_ec_t2 1 0.00000000
226 a_t2 emque_ec_t2 1 0.00000000
227 tom_t2 emque_ec_t2 1 0.00000000
228 flanker_t2 emque_ec_t2 1 0.00000000
229 tec_t2 emque_ec_t2 1 0.00000000
230 wm_t2 emque_ec_t2 1 0.00000000
231 dccs_t2 emque_ec_t2 1 0.00000000
232 cbq_na_t2 emque_ec_t2 1 0.00000000
233 cbq_att_t2 emque_ec_t2 1 0.00000000
234 cbq_shy_t2 emque_ec_t2 1 0.00000000
239 ht_t2 emque_af_t2 1 0.00000000
240 ec_t2 emque_af_t2 1 0.00000000
241 pa_t2 emque_af_t2 1 0.00000000
242 sd_t2 emque_af_t2 1 0.00000000
243 a_t2 emque_af_t2 1 0.00000000
244 tom_t2 emque_af_t2 1 0.00000000
245 flanker_t2 emque_af_t2 1 0.00000000
246 tec_t2 emque_af_t2 1 0.00000000
247 wm_t2 emque_af_t2 1 0.00000000
248 dccs_t2 emque_af_t2 1 0.00000000
249 cbq_na_t2 emque_af_t2 1 0.00000000
250 cbq_att_t2 emque_af_t2 1 0.00000000
251 cbq_shy_t2 emque_af_t2 1 0.00000000
252 emque_ec_t2 emque_af_t2 1 0.05358032
256 ht_t2 emque_pa_t2 1 0.00000000
257 ec_t2 emque_pa_t2 1 0.00000000
258 pa_t2 emque_pa_t2 1 0.00000000
259 sd_t2 emque_pa_t2 1 0.00000000
260 a_t2 emque_pa_t2 1 0.00000000
261 tom_t2 emque_pa_t2 1 0.00000000
262 flanker_t2 emque_pa_t2 1 0.00000000
263 tec_t2 emque_pa_t2 1 0.00000000
264 wm_t2 emque_pa_t2 1 0.00000000
265 dccs_t2 emque_pa_t2 1 0.00000000
266 cbq_na_t2 emque_pa_t2 1 0.00000000
267 cbq_att_t2 emque_pa_t2 1 0.00000000
268 cbq_shy_t2 emque_pa_t2 1 0.00000000
269 emque_ec_t2 emque_pa_t2 1 0.10347454
270 emque_af_t2 emque_pa_t2 1 0.03352101
273 ht_t2 csus_pt_t2 1 0.00000000
274 ec_t2 csus_pt_t2 1 0.00000000
275 pa_t2 csus_pt_t2 1 0.00000000
276 sd_t2 csus_pt_t2 1 0.00000000
277 a_t2 csus_pt_t2 1 0.00000000
278 tom_t2 csus_pt_t2 1 0.00000000
279 flanker_t2 csus_pt_t2 1 0.00000000
280 tec_t2 csus_pt_t2 1 0.00000000
281 wm_t2 csus_pt_t2 1 0.00000000
282 dccs_t2 csus_pt_t2 1 0.00000000
283 cbq_na_t2 csus_pt_t2 1 0.00000000
284 cbq_att_t2 csus_pt_t2 1 0.00000000
285 cbq_shy_t2 csus_pt_t2 1 0.00000000
286 emque_ec_t2 csus_pt_t2 1 0.00000000
287 emque_af_t2 csus_pt_t2 1 0.00000000
288 emque_pa_t2 csus_pt_t2 1 0.05429799
CENTRALITY INVARIANCE TEST
Nodes tested: ht_t2 ec_t2 pa_t2 sd_t2 a_t2 tom_t2 flanker_t2 tec_t2 wm_t2 dccs_t2 cbq_na_t2 cbq_att_t2 cbq_shy_t2 emque_ec_t2 emque_af_t2 emque_pa_t2 csus_pt_t2
Centralities tested: expectedInfluence
Test statistics C:
expectedInfluence
ht_t2 0.03211008
ec_t2 0.02554698
pa_t2 -0.00547388
sd_t2 0.07856543
a_t2 0.06076482
tom_t2 -0.11644105
flanker_t2 0.00000000
tec_t2 0.00000000
wm_t2 -0.11644105
dccs_t2 0.00000000
cbq_na_t2 0.05347205
cbq_att_t2 0.00000000
cbq_shy_t2 0.05347205
emque_ec_t2 0.04989422
emque_af_t2 -0.02005931
emque_pa_t2 0.08269756
csus_pt_t2 -0.05429799
p-values:
expectedInfluence
ht_t2 1
ec_t2 1
pa_t2 1
sd_t2 1
a_t2 1
tom_t2 1
flanker_t2 1
tec_t2 1
wm_t2 1
dccs_t2 1
cbq_na_t2 1
cbq_att_t2 1
cbq_shy_t2 1
emque_ec_t2 1
emque_af_t2 1
emque_pa_t2 1
csus_pt_t2 1
if (file.exists (here ("output" , "models" , "networks" , "nct_6vs7_t2.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "nct_6vs7_t2.rds" ))summary (nct_6vs7_t2)plot (nct_6vs7_t2, what = "strength" )plot (nct_6vs7_t2, what = "network" )#plot(nct_4vs6, what = "centrality") #plot(nct_4vs6, what = "edge") else { nct_6vs7_t2 <- NetworkComparisonTest:: NCT (network_ebicglasso_t2_6,gamma = 0.5 ,it = 5000 ,abs = TRUE , edges = "yes" ,test.edges = TRUE ,test.centrality = TRUE ,centrality = "expectedInfluence" ,p.adjust.methods = "BH" )saveRDS (nct_6vs7_t2,file = here ("output" , "models" , "networks" , "nct_6vs7_t2.rds" ))summary (nct_6vs7_t2)plot (nct_6vs7_t2, what = "strength" )plot (nct_5vs6_t2, what = "network" )#plot(nct_6vs7_t2, what = "centrality") #plot(nct_6vs7_t2, what = "edge")
INDEPENDENT GROUPS GAUSSIAN NETWORK COMPARISON TEST
P-VALUE CORRECTION: BH
NETWORK INVARIANCE TEST
Test statistic M: 0.2034839
p-value 0.3055389
GLOBAL STRENGTH INVARIANCE TEST
Global strength per group: 1.558107 2.924347
Test statistic S: 1.36624
p-value 0.1483703
EDGE INVARIANCE TEST
Var1 Var2 p-value Test statistic E
18 ht_t2 ec_t2 1.0000000 0.12440105
35 ht_t2 pa_t2 1.0000000 0.08236284
36 ec_t2 pa_t2 1.0000000 0.08679458
52 ht_t2 sd_t2 1.0000000 0.00000000
53 ec_t2 sd_t2 1.0000000 0.08299823
54 pa_t2 sd_t2 1.0000000 0.02623878
69 ht_t2 a_t2 1.0000000 0.00000000
70 ec_t2 a_t2 1.0000000 0.06076482
71 pa_t2 a_t2 1.0000000 0.00000000
72 sd_t2 a_t2 0.4759048 0.09836261
86 ht_t2 tom_t2 1.0000000 0.00000000
87 ec_t2 tom_t2 1.0000000 0.00000000
88 pa_t2 tom_t2 0.8469163 0.03904162
89 sd_t2 tom_t2 1.0000000 0.00000000
90 a_t2 tom_t2 1.0000000 0.00000000
103 ht_t2 flanker_t2 1.0000000 0.00000000
104 ec_t2 flanker_t2 1.0000000 0.00000000
105 pa_t2 flanker_t2 1.0000000 0.00000000
106 sd_t2 flanker_t2 1.0000000 0.00000000
107 a_t2 flanker_t2 1.0000000 0.00000000
108 tom_t2 flanker_t2 0.8770246 0.09148413
120 ht_t2 tec_t2 1.0000000 0.00000000
121 ec_t2 tec_t2 1.0000000 0.00000000
122 pa_t2 tec_t2 1.0000000 0.00000000
123 sd_t2 tec_t2 1.0000000 0.00000000
124 a_t2 tec_t2 1.0000000 0.00000000
125 tom_t2 tec_t2 1.0000000 0.00000000
126 flanker_t2 tec_t2 1.0000000 0.00000000
137 ht_t2 wm_t2 1.0000000 0.00000000
138 ec_t2 wm_t2 1.0000000 0.00000000
139 pa_t2 wm_t2 1.0000000 0.00000000
140 sd_t2 wm_t2 1.0000000 0.00000000
141 a_t2 wm_t2 1.0000000 0.00000000
142 tom_t2 wm_t2 1.0000000 0.03281102
143 flanker_t2 wm_t2 1.0000000 0.00000000
144 tec_t2 wm_t2 1.0000000 0.09449018
154 ht_t2 dccs_t2 1.0000000 0.00000000
155 ec_t2 dccs_t2 1.0000000 0.00000000
156 pa_t2 dccs_t2 1.0000000 0.00000000
157 sd_t2 dccs_t2 1.0000000 0.00000000
158 a_t2 dccs_t2 1.0000000 0.00000000
159 tom_t2 dccs_t2 1.0000000 0.00000000
160 flanker_t2 dccs_t2 1.0000000 0.00000000
161 tec_t2 dccs_t2 1.0000000 0.00000000
162 wm_t2 dccs_t2 1.0000000 0.02955106
171 ht_t2 cbq_na_t2 1.0000000 0.00000000
172 ec_t2 cbq_na_t2 1.0000000 0.00000000
173 pa_t2 cbq_na_t2 1.0000000 0.00000000
174 sd_t2 cbq_na_t2 1.0000000 0.00000000
175 a_t2 cbq_na_t2 1.0000000 0.00000000
176 tom_t2 cbq_na_t2 1.0000000 0.00000000
177 flanker_t2 cbq_na_t2 1.0000000 0.00000000
178 tec_t2 cbq_na_t2 1.0000000 0.00000000
179 wm_t2 cbq_na_t2 1.0000000 0.00000000
180 dccs_t2 cbq_na_t2 1.0000000 0.00000000
188 ht_t2 cbq_att_t2 1.0000000 0.00000000
189 ec_t2 cbq_att_t2 1.0000000 0.00000000
190 pa_t2 cbq_att_t2 1.0000000 0.00000000
191 sd_t2 cbq_att_t2 1.0000000 0.00000000
192 a_t2 cbq_att_t2 1.0000000 0.00000000
193 tom_t2 cbq_att_t2 1.0000000 0.00000000
194 flanker_t2 cbq_att_t2 1.0000000 0.00000000
195 tec_t2 cbq_att_t2 0.2447510 0.08536023
196 wm_t2 cbq_att_t2 1.0000000 0.00000000
197 dccs_t2 cbq_att_t2 1.0000000 0.00000000
198 cbq_na_t2 cbq_att_t2 1.0000000 0.00000000
205 ht_t2 cbq_shy_t2 0.6979937 0.01753731
206 ec_t2 cbq_shy_t2 1.0000000 0.00000000
207 pa_t2 cbq_shy_t2 1.0000000 0.00000000
208 sd_t2 cbq_shy_t2 1.0000000 0.00000000
209 a_t2 cbq_shy_t2 1.0000000 0.00000000
210 tom_t2 cbq_shy_t2 1.0000000 0.00000000
211 flanker_t2 cbq_shy_t2 1.0000000 0.00000000
212 tec_t2 cbq_shy_t2 1.0000000 0.00000000
213 wm_t2 cbq_shy_t2 1.0000000 0.00000000
214 dccs_t2 cbq_shy_t2 1.0000000 0.00000000
215 cbq_na_t2 cbq_shy_t2 1.0000000 0.13340390
216 cbq_att_t2 cbq_shy_t2 1.0000000 0.00000000
222 ht_t2 emque_ec_t2 1.0000000 0.00000000
223 ec_t2 emque_ec_t2 1.0000000 0.00000000
224 pa_t2 emque_ec_t2 1.0000000 0.00000000
225 sd_t2 emque_ec_t2 1.0000000 0.00000000
226 a_t2 emque_ec_t2 1.0000000 0.00000000
227 tom_t2 emque_ec_t2 1.0000000 0.00000000
228 flanker_t2 emque_ec_t2 1.0000000 0.00000000
229 tec_t2 emque_ec_t2 1.0000000 0.00000000
230 wm_t2 emque_ec_t2 1.0000000 0.00000000
231 dccs_t2 emque_ec_t2 1.0000000 0.00000000
232 cbq_na_t2 emque_ec_t2 1.0000000 0.00000000
233 cbq_att_t2 emque_ec_t2 1.0000000 0.00000000
234 cbq_shy_t2 emque_ec_t2 1.0000000 0.00000000
239 ht_t2 emque_af_t2 1.0000000 0.00000000
240 ec_t2 emque_af_t2 1.0000000 0.00000000
241 pa_t2 emque_af_t2 1.0000000 0.00000000
242 sd_t2 emque_af_t2 1.0000000 0.00000000
243 a_t2 emque_af_t2 1.0000000 0.00000000
244 tom_t2 emque_af_t2 1.0000000 0.00000000
245 flanker_t2 emque_af_t2 1.0000000 0.00000000
246 tec_t2 emque_af_t2 1.0000000 0.00000000
247 wm_t2 emque_af_t2 1.0000000 0.00000000
248 dccs_t2 emque_af_t2 1.0000000 0.00000000
249 cbq_na_t2 emque_af_t2 1.0000000 0.00000000
250 cbq_att_t2 emque_af_t2 1.0000000 0.00000000
251 cbq_shy_t2 emque_af_t2 1.0000000 0.00000000
252 emque_ec_t2 emque_af_t2 1.0000000 0.01256945
256 ht_t2 emque_pa_t2 1.0000000 0.00000000
257 ec_t2 emque_pa_t2 1.0000000 0.00000000
258 pa_t2 emque_pa_t2 1.0000000 0.00000000
259 sd_t2 emque_pa_t2 1.0000000 0.00000000
260 a_t2 emque_pa_t2 1.0000000 0.00000000
261 tom_t2 emque_pa_t2 1.0000000 0.00000000
262 flanker_t2 emque_pa_t2 1.0000000 0.00000000
263 tec_t2 emque_pa_t2 1.0000000 0.00000000
264 wm_t2 emque_pa_t2 1.0000000 0.00000000
265 dccs_t2 emque_pa_t2 1.0000000 0.00000000
266 cbq_na_t2 emque_pa_t2 1.0000000 0.00000000
267 cbq_att_t2 emque_pa_t2 1.0000000 0.00000000
268 cbq_shy_t2 emque_pa_t2 1.0000000 0.00000000
269 emque_ec_t2 emque_pa_t2 1.0000000 0.13828573
270 emque_af_t2 emque_pa_t2 0.5302939 0.20348392
273 ht_t2 csus_pt_t2 1.0000000 0.00000000
274 ec_t2 csus_pt_t2 1.0000000 0.00000000
275 pa_t2 csus_pt_t2 1.0000000 0.00000000
276 sd_t2 csus_pt_t2 1.0000000 0.00000000
277 a_t2 csus_pt_t2 1.0000000 0.00000000
278 tom_t2 csus_pt_t2 1.0000000 0.01528408
279 flanker_t2 csus_pt_t2 1.0000000 0.00000000
280 tec_t2 csus_pt_t2 1.0000000 0.05928222
281 wm_t2 csus_pt_t2 0.5874025 0.13926081
282 dccs_t2 csus_pt_t2 1.0000000 0.00000000
283 cbq_na_t2 csus_pt_t2 1.0000000 0.04261542
284 cbq_att_t2 csus_pt_t2 1.0000000 0.00000000
285 cbq_shy_t2 csus_pt_t2 0.5302939 0.12341783
286 emque_ec_t2 csus_pt_t2 1.0000000 0.00000000
287 emque_af_t2 csus_pt_t2 1.0000000 0.00000000
288 emque_pa_t2 csus_pt_t2 1.0000000 0.00628302
CENTRALITY INVARIANCE TEST
Nodes tested: ht_t2 ec_t2 pa_t2 sd_t2 a_t2 tom_t2 flanker_t2 tec_t2 wm_t2 dccs_t2 cbq_na_t2 cbq_att_t2 cbq_shy_t2 emque_ec_t2 emque_af_t2 emque_pa_t2 csus_pt_t2
Centralities tested: expectedInfluence
Test statistics C:
expectedInfluence
ht_t2 -0.059575527
ec_t2 -0.015373068
pa_t2 0.234437805
sd_t2 0.207599615
a_t2 0.037597783
tom_t2 -0.100537610
flanker_t2 -0.091484126
tec_t2 -0.239132637
wm_t2 -0.296113061
dccs_t2 -0.029551059
cbq_na_t2 0.176019324
cbq_att_t2 -0.085360235
cbq_shy_t2 -0.007551238
emque_ec_t2 -0.150855178
emque_af_t2 -0.216053367
emque_pa_t2 -0.348052659
csus_pt_t2 -0.300912538
p-values:
expectedInfluence
ht_t2 0.7328934
ec_t2 0.9556089
pa_t2 0.3365327
sd_t2 0.4254816
a_t2 0.4626166
tom_t2 0.6087398
flanker_t2 0.4293341
tec_t2 0.3365327
wm_t2 0.4293341
dccs_t2 0.7039021
cbq_na_t2 0.4293341
cbq_att_t2 0.3365327
cbq_shy_t2 0.9556089
emque_ec_t2 0.5886489
emque_af_t2 0.4293341
emque_pa_t2 0.4254816
csus_pt_t2 0.3365327
if (file.exists (here ("output" , "models" , "networks" , "nct_5vs7_t2.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "nct_5vs7_t2.rds" ))summary (nct_5vs7_t2)plot (nct_5vs7_t2, what = "strength" )plot (nct_5vs7_t2, what = "network" )#plot(nct_4vs6, what = "centrality") #plot(nct_4vs6, what = "edge") else {<- NetworkComparisonTest:: NCT (network_ebicglasso_t2_5,gamma = 0.5 ,it = 5000 ,abs = TRUE , edges = "yes" ,test.edges = TRUE ,test.centrality = TRUE ,centrality = "expectedInfluence" ,p.adjust.methods = "none" )saveRDS (nct_5vs7_t2,file = here ("output" , "models" , "networks" , "nct_5vs7_t2.rds" ))summary (nct_5vs7_t2)plot (nct_5vs7_t2, what = "strength" )plot (nct_5vs7_t2, what = "network" )# plot(nct_4vs6, what = "centrality") #plot(nct_4vs6, what = "edge")
INDEPENDENT GROUPS GAUSSIAN NETWORK COMPARISON TEST
P-VALUE CORRECTION: none
NETWORK INVARIANCE TEST
Test statistic M: 0.186876
p-value 0.5962807
GLOBAL STRENGTH INVARIANCE TEST
Global strength per group: 1.234407 2.924347
Test statistic S: 1.68994
p-value 0.284943
EDGE INVARIANCE TEST
Var1 Var2 p-value Test statistic E
18 ht_t2 ec_t2 0.30433913 0.10576004
35 ht_t2 pa_t2 0.29394121 0.09583191
36 ec_t2 pa_t2 0.91001800 0.01111096
52 ht_t2 sd_t2 1.00000000 0.00000000
53 ec_t2 sd_t2 0.31033793 0.10482300
54 pa_t2 sd_t2 0.34853029 0.08297944
69 ht_t2 a_t2 1.00000000 0.00000000
70 ec_t2 a_t2 1.00000000 0.00000000
71 pa_t2 a_t2 1.00000000 0.00000000
72 sd_t2 a_t2 0.14397121 0.09836261
86 ht_t2 tom_t2 1.00000000 0.00000000
87 ec_t2 tom_t2 1.00000000 0.00000000
88 pa_t2 tom_t2 0.04659068 0.03904162
89 sd_t2 tom_t2 1.00000000 0.00000000
90 a_t2 tom_t2 1.00000000 0.00000000
103 ht_t2 flanker_t2 1.00000000 0.00000000
104 ec_t2 flanker_t2 1.00000000 0.00000000
105 pa_t2 flanker_t2 1.00000000 0.00000000
106 sd_t2 flanker_t2 1.00000000 0.00000000
107 a_t2 flanker_t2 1.00000000 0.00000000
108 tom_t2 flanker_t2 0.29234153 0.09148413
120 ht_t2 tec_t2 1.00000000 0.00000000
121 ec_t2 tec_t2 1.00000000 0.00000000
122 pa_t2 tec_t2 1.00000000 0.00000000
123 sd_t2 tec_t2 1.00000000 0.00000000
124 a_t2 tec_t2 1.00000000 0.00000000
125 tom_t2 tec_t2 1.00000000 0.00000000
126 flanker_t2 tec_t2 1.00000000 0.00000000
137 ht_t2 wm_t2 1.00000000 0.00000000
138 ec_t2 wm_t2 1.00000000 0.00000000
139 pa_t2 wm_t2 1.00000000 0.00000000
140 sd_t2 wm_t2 1.00000000 0.00000000
141 a_t2 wm_t2 1.00000000 0.00000000
142 tom_t2 wm_t2 0.17536493 0.14925207
143 flanker_t2 wm_t2 1.00000000 0.00000000
144 tec_t2 wm_t2 0.38332334 0.09449018
154 ht_t2 dccs_t2 1.00000000 0.00000000
155 ec_t2 dccs_t2 1.00000000 0.00000000
156 pa_t2 dccs_t2 1.00000000 0.00000000
157 sd_t2 dccs_t2 1.00000000 0.00000000
158 a_t2 dccs_t2 1.00000000 0.00000000
159 tom_t2 dccs_t2 1.00000000 0.00000000
160 flanker_t2 dccs_t2 1.00000000 0.00000000
161 tec_t2 dccs_t2 1.00000000 0.00000000
162 wm_t2 dccs_t2 0.80063987 0.02955106
171 ht_t2 cbq_na_t2 1.00000000 0.00000000
172 ec_t2 cbq_na_t2 1.00000000 0.00000000
173 pa_t2 cbq_na_t2 1.00000000 0.00000000
174 sd_t2 cbq_na_t2 1.00000000 0.00000000
175 a_t2 cbq_na_t2 1.00000000 0.00000000
176 tom_t2 cbq_na_t2 1.00000000 0.00000000
177 flanker_t2 cbq_na_t2 1.00000000 0.00000000
178 tec_t2 cbq_na_t2 1.00000000 0.00000000
179 wm_t2 cbq_na_t2 1.00000000 0.00000000
180 dccs_t2 cbq_na_t2 1.00000000 0.00000000
188 ht_t2 cbq_att_t2 1.00000000 0.00000000
189 ec_t2 cbq_att_t2 1.00000000 0.00000000
190 pa_t2 cbq_att_t2 1.00000000 0.00000000
191 sd_t2 cbq_att_t2 1.00000000 0.00000000
192 a_t2 cbq_att_t2 1.00000000 0.00000000
193 tom_t2 cbq_att_t2 1.00000000 0.00000000
194 flanker_t2 cbq_att_t2 1.00000000 0.00000000
195 tec_t2 cbq_att_t2 0.00079984 0.08536023
196 wm_t2 cbq_att_t2 1.00000000 0.00000000
197 dccs_t2 cbq_att_t2 1.00000000 0.00000000
198 cbq_na_t2 cbq_att_t2 1.00000000 0.00000000
205 ht_t2 cbq_shy_t2 0.06218756 0.01753731
206 ec_t2 cbq_shy_t2 1.00000000 0.00000000
207 pa_t2 cbq_shy_t2 1.00000000 0.00000000
208 sd_t2 cbq_shy_t2 1.00000000 0.00000000
209 a_t2 cbq_shy_t2 1.00000000 0.00000000
210 tom_t2 cbq_shy_t2 1.00000000 0.00000000
211 flanker_t2 cbq_shy_t2 1.00000000 0.00000000
212 tec_t2 cbq_shy_t2 1.00000000 0.00000000
213 wm_t2 cbq_shy_t2 1.00000000 0.00000000
214 dccs_t2 cbq_shy_t2 1.00000000 0.00000000
215 cbq_na_t2 cbq_shy_t2 0.20475905 0.18687596
216 cbq_att_t2 cbq_shy_t2 1.00000000 0.00000000
222 ht_t2 emque_ec_t2 1.00000000 0.00000000
223 ec_t2 emque_ec_t2 1.00000000 0.00000000
224 pa_t2 emque_ec_t2 1.00000000 0.00000000
225 sd_t2 emque_ec_t2 1.00000000 0.00000000
226 a_t2 emque_ec_t2 1.00000000 0.00000000
227 tom_t2 emque_ec_t2 1.00000000 0.00000000
228 flanker_t2 emque_ec_t2 1.00000000 0.00000000
229 tec_t2 emque_ec_t2 1.00000000 0.00000000
230 wm_t2 emque_ec_t2 1.00000000 0.00000000
231 dccs_t2 emque_ec_t2 1.00000000 0.00000000
232 cbq_na_t2 emque_ec_t2 1.00000000 0.00000000
233 cbq_att_t2 emque_ec_t2 1.00000000 0.00000000
234 cbq_shy_t2 emque_ec_t2 1.00000000 0.00000000
239 ht_t2 emque_af_t2 1.00000000 0.00000000
240 ec_t2 emque_af_t2 1.00000000 0.00000000
241 pa_t2 emque_af_t2 1.00000000 0.00000000
242 sd_t2 emque_af_t2 1.00000000 0.00000000
243 a_t2 emque_af_t2 1.00000000 0.00000000
244 tom_t2 emque_af_t2 1.00000000 0.00000000
245 flanker_t2 emque_af_t2 1.00000000 0.00000000
246 tec_t2 emque_af_t2 1.00000000 0.00000000
247 wm_t2 emque_af_t2 1.00000000 0.00000000
248 dccs_t2 emque_af_t2 1.00000000 0.00000000
249 cbq_na_t2 emque_af_t2 1.00000000 0.00000000
250 cbq_att_t2 emque_af_t2 1.00000000 0.00000000
251 cbq_shy_t2 emque_af_t2 1.00000000 0.00000000
252 emque_ec_t2 emque_af_t2 0.60067986 0.06614977
256 ht_t2 emque_pa_t2 1.00000000 0.00000000
257 ec_t2 emque_pa_t2 1.00000000 0.00000000
258 pa_t2 emque_pa_t2 1.00000000 0.00000000
259 sd_t2 emque_pa_t2 1.00000000 0.00000000
260 a_t2 emque_pa_t2 1.00000000 0.00000000
261 tom_t2 emque_pa_t2 1.00000000 0.00000000
262 flanker_t2 emque_pa_t2 1.00000000 0.00000000
263 tec_t2 emque_pa_t2 1.00000000 0.00000000
264 wm_t2 emque_pa_t2 1.00000000 0.00000000
265 dccs_t2 emque_pa_t2 1.00000000 0.00000000
266 cbq_na_t2 emque_pa_t2 1.00000000 0.00000000
267 cbq_att_t2 emque_pa_t2 1.00000000 0.00000000
268 cbq_shy_t2 emque_pa_t2 1.00000000 0.00000000
269 emque_ec_t2 emque_pa_t2 0.76044791 0.03481119
270 emque_af_t2 emque_pa_t2 0.13517297 0.16996290
273 ht_t2 csus_pt_t2 1.00000000 0.00000000
274 ec_t2 csus_pt_t2 1.00000000 0.00000000
275 pa_t2 csus_pt_t2 1.00000000 0.00000000
276 sd_t2 csus_pt_t2 1.00000000 0.00000000
277 a_t2 csus_pt_t2 1.00000000 0.00000000
278 tom_t2 csus_pt_t2 0.92081584 0.01528408
279 flanker_t2 csus_pt_t2 1.00000000 0.00000000
280 tec_t2 csus_pt_t2 0.64247151 0.05928222
281 wm_t2 csus_pt_t2 0.21555689 0.13926081
282 dccs_t2 csus_pt_t2 1.00000000 0.00000000
283 cbq_na_t2 csus_pt_t2 0.14097181 0.04261542
284 cbq_att_t2 csus_pt_t2 1.00000000 0.00000000
285 cbq_shy_t2 csus_pt_t2 0.19176165 0.12341783
286 emque_ec_t2 csus_pt_t2 1.00000000 0.00000000
287 emque_af_t2 csus_pt_t2 1.00000000 0.00000000
288 emque_pa_t2 csus_pt_t2 0.51289742 0.06058101
CENTRALITY INVARIANCE TEST
Nodes tested: ht_t2 ec_t2 pa_t2 sd_t2 a_t2 tom_t2 flanker_t2 tec_t2 wm_t2 dccs_t2 cbq_na_t2 cbq_att_t2 cbq_shy_t2 emque_ec_t2 emque_af_t2 emque_pa_t2 csus_pt_t2
Centralities tested: expectedInfluence
Test statistics C:
expectedInfluence
ht_t2 -0.02746544
ec_t2 0.01017391
pa_t2 0.22896392
sd_t2 0.28616504
a_t2 0.09836261
tom_t2 -0.21697866
flanker_t2 -0.09148413
tec_t2 -0.23913264
wm_t2 -0.41255411
dccs_t2 -0.02955106
cbq_na_t2 0.22949138
cbq_att_t2 -0.08536023
cbq_shy_t2 0.04592082
emque_ec_t2 -0.10096096
emque_af_t2 -0.23611267
emque_pa_t2 -0.26535510
csus_pt_t2 -0.35521053
p-values:
expectedInfluence
ht_t2 0.88982204
ec_t2 0.94101180
pa_t2 0.09958008
sd_t2 0.13977205
a_t2 0.27774445
tom_t2 0.49870026
flanker_t2 0.72885423
tec_t2 0.45690862
wm_t2 0.24195161
dccs_t2 0.85522895
cbq_na_t2 0.14037193
cbq_att_t2 0.12857429
cbq_shy_t2 0.71985603
emque_ec_t2 0.59548090
emque_af_t2 0.26014797
emque_pa_t2 0.36312737
csus_pt_t2 0.33953209
#9. Across-Time Network Comparison
# if (file.exists(here("output", "models", "networks", "nct_t1_vs_t2.rds"))) { # # nct_t1_vs_t2 <- readRDS(here("output", "models", "networks", "nct_t1_vs_t2.rds")) # # summary(nct_t1_vs_t2) # # plot(nct_t1_vs_t2, what = "strength" ) # # plot(nct_t1_vs_t2, what = "network") # # #plot(nct_4vs6, what = "centrality") # # #plot(nct_4vs6, what = "edge") # } else {nct_t1_vs_t2 <- NetworkComparisonTest::NCT(network_ebicglasso_t1, # network_ebicglasso_t2, # gamma = 0.5, # it = 5000, # abs = FALSE, # edges = "yes", # test.edges = TRUE, # test.centrality = TRUE, # centrality = "expectedInfluence", # p.adjust.methods = "BH", # paired = TRUE) # # saveRDS(nct_t1_vs_t2, # file = here("output", "models", "networks", "nct_t1_vs_t2.rds")) # # summary(nct_t1_vs_t2) # # plot(nct_t1_vs_t2, what = "strength" ) # # plot(nct_t1_vs_t2, what = "network") # }
#10. Exploratory Graph Analysis
if (file.exists (here ("output" , "models" , "networks" , "ega_boot_t1.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "ega_boot_t1.rds" ))<- EGAnet:: EGA (data = data_t1,cor = "cor_auto" ,plot.EGA = FALSE print (summary (ega_boot_t1))print (EGAnet:: dimensionStability (ega_boot_t1, IS.plot = FALSE ))<- EGAnet:: compare.EGA.plots (labels = c ("Empirical" , "Bootstrap" ),plot.all = FALSE else {<- EGAnet:: EGA (data = data_t1,cor = "cor_auto" ,plot.EGA = FALSE <- EGAnet:: bootEGA (data = data_t1,cor = "cor_auto" ,iter = 5000 ,plot.EGA = FALSE saveRDS (ega_boot_t1,here ("output" , "models" , "networks" , "ega_boot_t1.rds" ))print (summary (ega_boot_t1))print (EGAnet:: dimensionStability (ega_boot_t1, IS.plot = FALSE ))<- EGAnet:: compare.EGA.plots (labels = c ("Empirical" , "Bootstrap" ),plot.all = FALSE
Registered S3 method overwritten by 'car':
method from
na.action.merMod lme4
Model: GLASSO (EBIC)
Correlations: cor_auto
Algorithm: Walktrap
Unidimensional Method: Louvain
----
EGA Type: EGA
Bootstrap Samples: 5000 (Parametric)
3 4 5 6
Frequency: 0.0578 0.8134 0.1246 0.0042
Median dimensions: 4 [3.14, 4.86] 95% CINULL
EGA Type: EGA
Bootstrap Samples: 5000 (Parametric)
Proportion Replicated in Dimensions:
ht ec pa sd a tom flanker tec
1.0000 1.0000 1.0000 1.0000 0.9382 1.0000 0.9984 1.0000
wm dccs cbq_na cbq_att cbq_shy emque_ec emque_af emque_pa
1.0000 0.7650 0.8382 0.5520 0.8382 0.9886 0.9886 0.8592
csus_pt
0.6494
----
Structural Consistency:
1 2 3 4
0.9404 0.5260 0.8382 0.8596
if (file.exists (here ("output" , "models" , "networks" , "ega_boot_t2.rds" ))) {<- readRDS (here ("output" , "models" , "networks" , "ega_boot_t2.rds" ))<- EGAnet:: EGA (data = data_t2,cor = "cor_auto" ,plot.EGA = FALSE print (summary (ega_boot_t2))print (EGAnet:: dimensionStability (ega_boot_t2, IS.plot = FALSE ))<- EGAnet:: compare.EGA.plots (labels = c ("Empirical" , "Bootstrap" ),plot.all = FALSE else {<- EGAnet:: EGA (data = data_t2,cor = "cor_auto" ,plot.EGA = FALSE <- EGAnet:: bootEGA (data = data_t2,cor = "cor_auto" ,iter = 5000 ,plot.EGA = FALSE saveRDS (ega_boot_t2,here ("output" , "models" , "networks" , "ega_boot_t2.rds" ))print (summary (ega_boot_t2, ... = )):: summary ()print (EGAnet:: dimensionStability (ega_boot_t2, IS.plot = FALSE ))<- EGAnet:: compare.EGA.plots (labels = c ("Empirical" , "Bootstrap" ),plot.all = FALSE
Model: GLASSO (EBIC)
Correlations: cor_auto
Algorithm: Walktrap
Unidimensional Method: Louvain
----
EGA Type: EGA
Bootstrap Samples: 5000 (Parametric)
3 4 5
Frequency: 0.1856 0.776 0.0384
Median dimensions: 4 [3.12, 4.88] 95% CINULL
EGA Type: EGA
Bootstrap Samples: 5000 (Parametric)
Proportion Replicated in Dimensions:
ht ec pa sd a tom flanker tec
1.0000 1.0000 1.0000 1.0000 1.0000 0.9986 0.9876 0.9980
wm dccs cbq_na cbq_att cbq_shy emque_ec emque_af emque_pa
0.9986 0.9912 0.9642 0.6764 0.9642 0.8276 0.8276 0.7250
csus_pt
0.6480
----
Structural Consistency:
1 2 3 4
1.0000 0.9858 0.6142 0.7272
compare.EGA.plots (ega_boot_t1,labels = c ("T1" , "T2" ))
$individual
$individual[[1]]
<- as.numeric (data_long_npn$ time)<- EGAnet:: invariance (data = data_long_npn[,7 : 23 ],groups = time
Testing configural invariance...
Configural invariance was found with 15 variables
Testing metric invariance...
The default 'loading.method' has changed to "revised" in {EGAnet} version >= 2.0.7.
For the previous default (version <= 2.0.6), use `loading.method = "original"`
The default 'loading.method' has changed to "revised" in {EGAnet} version >= 2.0.7.
For the previous default (version <= 2.0.6), use `loading.method = "original"`
plot (time_invariance$ configural.results$ item_stability)
Ignoring unknown labels:
• fill : "Communities"
• linetype : "Communities"
• shape : "Communities"
[1;mSummary of Invariance Results[0m
Number of groups: 2
Number of pairwise comparisons: 1
[4;m
Comparison: 1 vs 2[0m
Number of noninvariant items (p < 0.05): 1 (6.7%)
Number of noninvariant items (p_BH < 0.05): 1 (6.7%)
Use `print()` for more detailed results.
[1;mInvariance Results[0m
[4;m
Comparison: 1 vs 2[0m
Membership Difference p p_BH sig Direction
ht 1 -0.028 0.576 0.720
ec 1 -0.128 0.186 0.399
pa 1 -0.188 0.060 0.300 .
sd 1 -0.136 0.100 0.300 .
a 1 -0.097 0.094 0.300 .
tom 2 0.024 0.738 0.852
flanker 2 -0.088 0.294 0.551
tec 2 -0.016 0.834 0.894
wm 2 -0.051 0.536 0.720
dccs 2 -0.224 0.002 0.030 ** 1 < 2
cbq_na 3 0.021 0.416 0.624
cbq_shy 3 -0.021 0.416 0.624
emque_ec 4 -0.101 0.058 0.300 .
emque_af 4 0.003 0.956 0.956
emque_pa 4 -0.074 0.156 0.390
----
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 'n.s.' 1
