Topology of Fears and Phobias
Eiko Fried
04/25/2020
1 Introduction
This file summarizes the supplementary analyses and results for the paper “A Topography of Fears and Phobias: Network Analysis of 21 Phobic Fears in an Epidemiological Sample of Adult Twins”, by Kenneth S. Kendler, Steve Aggen, Marlene Werner, and Eiko I. Fried. For questions, feel free to reach out (eikofried@gmail.com).
I omit much of the code here to keep this document clean. You can find the full code in the supplementary materials of this paper.
Last udpate: September 22 2020.
2 Estimation of 3 network models for phobic fears
par(mfrow=c(1,3))
g2 <- plot(n2, layout=g2$layout, legend=FALSE, vsize=9, legend=FALSE, title="GGM")
g3 <- plot(n3, layout=g2$layout, legend=FALSE, vsize=9, legend=FALSE, title="thresholded GGM")
g4 <- plot(n4, layout=g2$layout, legend=FALSE, vsize=9, legend=FALSE, title="ggmModSelect")
N3 (thresholded GGM) stands out as being most different. n2 (regularized GGM) and n4 (ggmModSelect) were very similar:
cor(vechs(n2$graph), vechs(n4$graph)) ## [1] 0.9758195cor(vechs(n2$graph), vechs(n4$graph), method="spearman") ## [1] 0.8976063We choose n2 for the main paper.
Here the network that includes the panic item:
g5 <- plot(n5, layout=layoutPanic, legend=FALSE, vsize=9, legend=FALSE, title="GGM with panic")
3 Predictability
We estimated the predictability using the R package mgm. For that, we needed to re-estimate the network using mgm, which is a different estimation procedure (node-wise regression). We checked the similarity of the resulting networks:
layout(t(1:2))
g2 <- plot(n2, layout=g2$layout, pie = pr$errors$R2,
pieBorder = 0.25, vsize=7, title="GGM")
g2mgm <- plot(n2mgm, layout=g2$layout, pie = pr$errors$R2,
pieBorder = 0.25, vsize=7, title="MGM")
cor(vechs(n2$graph), vechs(n2mgm$graph), method="spearman") ## [1] 0.889582Networks are similar enough to warrant plotting predictability in the GGM network. We prefer GGM over MGM because our data are ordinal, skewed items, which GGM can deal with (via spearman correlations), but MGM (linear regression model) cannot.
4 Community detection
Here are the 3 most common community solutions, based on both GGM and ggmModSelect estimation, along with their frequencies:
par(mfrow=c(2,3))
icdg2 <- qgraph(g2, layout=g2$layout, groups=icdgr2, legend=FALSE, vsize=9, legend=FALSE, title= paste("GGM IComDet ", as.character(icd2A$freqOfResult[1]*100), "%", sep = ""))
icdg2.2 <- qgraph(g2, layout=g2$layout, groups=icdgr2.2, legend=FALSE, vsize=9, legend=FALSE, title = paste("GGM IComDet ", as.character(icd2A$freqOfResult[2]*100), "%", sep = ""))
icdg2.3 <- qgraph(g2, layout=g2$layout, groups=icdgr2.3, legend=FALSE, vsize=9, legend=FALSE, title = paste("GGM IComDet ", as.character(icd2A$freqOfResult[3]*100), "%", sep = ""))
icdg4 <- qgraph(g4, layout=g2$layout, groups=icdgr4, legend=FALSE, vsize=9, legend=FALSE, title = paste("ggmModSelect IComDet ", as.character(icd4A$freqOfResult[1]*100), "%", sep = ""))
icdg4.2 <- qgraph(g4, layout=g2$layout, groups=icdgr4.2, legend=FALSE, vsize=9, legend=FALSE, title = paste("ggmModSelect IComDet ", as.character(icd4A$freqOfResult[2]*100), "%", sep = ""))
icdg4.3 <- qgraph(g4, layout=g2$layout, groups=icdgr4.3, legend=FALSE, vsize=9, legend=FALSE, title = paste("ggmModSelect IComDet ", as.character(icd4A$freqOfResult[3]*100), "%", sep = ""))
Here are the node coappearance matrices:
icd2A$heatmapClustMembership
icd4A$heatmapClustMembership
5 Bootstrapping routines
21-item phobias network:
plot(b1, labels = FALSE, order = 'sample', prop0=TRUE) 
plot(b1, 'edge', plot = 'difference', onlyNonZero = TRUE, order = 'sample', labels=FALSE)## Expected significance level given number of bootstrap samples is approximately: 0.05
22-item phobias network with panic disorder:
plot(b2, labels = FALSE, order = 'sample', prop0=TRUE) 
plot(b2, 'edge', plot = 'difference', onlyNonZero = TRUE, order = 'sample', labels=FALSE)## Expected significance level given number of bootstrap samples is approximately: 0.05
6 Robustness analyses twin data
Comparison of networks:
layout(t(1:2))
n_dep <- estimateNetwork(df_dep , default="EBICglasso", corMethod = "cor", corArgs = list(method="spearman"), threshold=FALSE)## Estimating Network. Using package::function:
## - qgraph::EBICglasso for EBIC model selection
## - using glasso::glassog_dep <- plot(n_dep, title="3022 twins, connectivity 6.57", details=TRUE, maximum=0.25)
n_ind <- estimateNetwork(df_ind , default="EBICglasso", corMethod = "cor", corArgs = list(method="spearman"), threshold=FALSE)## Estimating Network. Using package::function:
## - qgraph::EBICglasso for EBIC model selection
## - using glasso::glassog_ind <- plot(n_ind, title="3022 non twins, connectivity 6.73", layout=g_dep$layout, details=TRUE, maximum=0.25)
Correlation of adjacency matrices:
cor(vechs(n_dep$graph), vechs(n_ind$graph), method="spearman") ## [1] 0.7288627plot(vechs(n_dep$graph), vechs(n_ind$graph))
sum(vechs(abs(n_dep$graph))) # connectivity## [1] 6.572486sum(vechs(abs(n_ind$graph))) # connectivity## [1] 6.734718Twin and non twin networks are quite similar, but also show some differences worth investigating in future work. However, the fact that we include both dependent and independent rows in our main analyses likely does not pose a major threat to inferences.
7 More detailed robustness analyses twin data
We separate out 3 datasets: n1=twin1 (3023), n2=twin2 (3023), and n3=singletons (1453). Note that we randomly separate out twins into the 2 twin datasets, i.e. we do not always take the twin first appearing in the dataset and put that twin into twin 1. We do so to avoid potential confounds, e.g. in case the oldest twin was entered first into the database originally.
We then estimate partial correlation networks, not regularized because samples have different sizes, which confounds estimates when the lasso is used. This is also why these networks cannot be compared to the networks estimated in the original paper (which are regularized), but that is not the goal here.
layout(t(1:3))
g_twin1 <- plot(n_twin1, title="3023 Singleton Twin 1", details=TRUE, maximum=graphmax)
g_twin2 <- plot(n_twin2, title="3023 Singleton Twin 2", details=TRUE, maximum=graphmax,
layout=g_twin1$layout)
g_singleton <- plot(n_singleton, title="1453 Singletons", details=TRUE, maximum=graphmax,
layout=g_twin1$layout, )
Now we correlate adjacency matrices to get an idea of similarity. We repeat this 5 times to make sure we do not get results driven solely by sampling variability.
Correlations over all 5 runs: Twin 1 vs Singleton: 0.51, 0.39, 0.44, 0.39, 0.40 (average 0.43) Twin 2 vs Singleton: 0.41, 0.50, 0.46, 0.46, 0.49 (average 0.46) Twin 1 vs Twin 2: 0.49, 0.47, 0.55, 0.47, 0.52 (average 0.50)
In conclusion, these correlations are sufficiently close together that we believe that the fact that we focus on twins in the main analyses in the paper does not drive the core results in the paper.
Next, we investigate whether community solutions are different in twins vs singletons. We now estimate regularized networks (so it can be compared to each other properly) in randomly drawn subsamples of Twin1, Twin2, and singletons (1453 each).
par(mfrow=c(1,3))
g_twin1_icd <- plot(n_twin1_icd, title="1453 Random Twin 1", layout=g_twin1$layout, maximum=graphmax2)
g_twin2_icd <- plot(n_twin2_icd, title="1453 Random Twin 2", layout=g_twin1$layout, maximum=graphmax2)
g_singleton_icd <- plot(n_singleton_icd, title="1453 Singletons", layout=g_twin1$layout, maximum=graphmax2)
We find that the most common singleton community solution (third column) is exactly the same as the most common community solution in the full dataset reported in the main manuscript (right column), which addresses the concerns that the presence of twins in the data distorts the results.
Here are the node coappearance matrices for the most common solutions in twin 1, twin 2, singleton, and (as a reminder) the full sample reported in the manuscript:
icd_twin1_random$heatmapClustMembership
plot.new()
icd_twin2_random$heatmapClustMembership
plot.new()
icd_singleton$heatmapClustMembership
plot.new()
icd2A$heatmapClustMembership
8 Sharing all adjacency matrices and predictability coefficients
Overall network and predictability (variables in same order as adjacency matrix):
n2$graph## House Crowds Spaces People Speech
## House 0.000000000 0.160775011 0.118070253 0.051926170 0.000000000
## Crowds 0.160775011 0.000000000 0.054657034 0.149493478 0.070486897
## Spaces 0.118070253 0.054657034 0.000000000 0.000000000 0.000000000
## People 0.051926170 0.149493478 0.000000000 0.000000000 0.154322624
## Speech 0.000000000 0.070486897 0.000000000 0.154322624 0.000000000
## Bathroom 0.026014948 0.039496725 0.023238564 0.014606927 0.043580650
## Eating 0.097926289 0.123269935 0.036693170 0.125577006 0.008433963
## Spiders 0.021703436 0.002924607 0.000000000 0.030424847 0.041892487
## Bugs 0.001524535 0.022560125 0.000000000 0.041365696 0.008776183
## Mice 0.041105491 0.000000000 0.010103939 0.010657439 0.006466086
## Snakes 0.001580675 0.000000000 0.000000000 0.006603857 0.085840247
## Bats 0.013725798 0.014170419 0.000000000 0.002047382 0.011143183
## Tunnels 0.018793048 0.082249803 0.037000998 0.001471205 0.004916721
## Closed 0.050858658 0.065346092 0.004119702 0.000000000 0.017350053
## Bridges 0.013391497 0.000000000 0.050970249 0.029941317 0.000000000
## Airplane 0.033212490 0.028150348 0.001428999 0.000000000 0.046092686
## High 0.000000000 0.026409979 0.019625796 0.020134460 0.057087390
## Blood 0.005216821 0.000000000 0.000000000 0.014409971 0.024695416
## Needles 0.000000000 0.004504186 0.000000000 0.002808349 0.020422472
## Hospitals 0.025095101 0.047146822 0.000000000 0.045457309 0.047030512
## Diseases 0.003363398 0.012900497 0.000000000 0.029162470 0.047820166
## Bathroom Eating Spiders Bugs Mice
## House 0.026014948 0.097926289 0.0217034357 0.001524535 0.041105491
## Crowds 0.039496725 0.123269935 0.0029246073 0.022560125 0.000000000
## Spaces 0.023238564 0.036693170 0.0000000000 0.000000000 0.010103939
## People 0.014606927 0.125577006 0.0304248473 0.041365696 0.010657439
## Speech 0.043580650 0.008433963 0.0418924867 0.008776183 0.006466086
## Bathroom 0.000000000 0.071032183 0.0358545033 0.014834779 0.045158769
## Eating 0.071032183 0.000000000 0.0000000000 0.026490700 0.000000000
## Spiders 0.035854503 0.000000000 0.0000000000 0.242973704 0.033893642
## Bugs 0.014834779 0.026490700 0.2429737039 0.000000000 0.083811038
## Mice 0.045158769 0.000000000 0.0338936416 0.083811038 0.000000000
## Snakes 0.041382457 0.000000000 0.0604661092 0.016094851 0.128507798
## Bats 0.018972053 0.007300757 0.0659415074 0.064216446 0.155691345
## Tunnels 0.030025917 0.013334719 0.0001796902 0.011855692 0.000000000
## Closed 0.000000000 0.000000000 0.0341076119 0.000000000 0.043109199
## Bridges 0.007727963 0.017179942 0.0626820557 0.000000000 0.028398999
## Airplane 0.018297262 0.022493774 0.0283884697 0.000000000 0.001042260
## High 0.000000000 0.006404157 0.0238610534 0.015160036 0.020077433
## Blood 0.030465033 0.000000000 0.0030259977 0.001391433 0.000000000
## Needles 0.003112190 0.010148351 0.0123575235 0.030483565 0.005814178
## Hospitals 0.050003093 0.023177163 0.0283613089 0.023165076 0.025534175
## Diseases 0.126404537 0.034029880 0.0386646178 0.005231537 0.029661208
## Snakes Bats Tunnels Closed Bridges
## House 0.001580675 0.013725798 0.0187930475 0.050858658 0.013391497
## Crowds 0.000000000 0.014170419 0.0822498027 0.065346092 0.000000000
## Spaces 0.000000000 0.000000000 0.0370009979 0.004119702 0.050970249
## People 0.006603857 0.002047382 0.0014712046 0.000000000 0.029941317
## Speech 0.085840247 0.011143183 0.0049167210 0.017350053 0.000000000
## Bathroom 0.041382457 0.018972053 0.0300259175 0.000000000 0.007727963
## Eating 0.000000000 0.007300757 0.0133347191 0.000000000 0.017179942
## Spiders 0.060466109 0.065941507 0.0001796902 0.034107612 0.062682056
## Bugs 0.016094851 0.064216446 0.0118556919 0.000000000 0.000000000
## Mice 0.128507798 0.155691345 0.0000000000 0.043109199 0.028398999
## Snakes 0.000000000 0.090740709 0.0179811928 0.013993926 0.019278596
## Bats 0.090740709 0.000000000 0.0098889016 0.000000000 0.013728382
## Tunnels 0.017981193 0.009888902 0.0000000000 0.237020163 0.163501532
## Closed 0.013993926 0.000000000 0.2370201627 0.000000000 0.006242476
## Bridges 0.019278596 0.013728382 0.1635015323 0.006242476 0.000000000
## Airplane 0.051137146 0.017489023 0.0512430766 0.019464354 0.069230409
## High 0.063503530 0.015332144 0.0319941762 0.063946756 0.165341479
## Blood 0.032806590 0.041566461 0.0064223231 0.026415781 0.000215742
## Needles 0.056277281 0.008454339 0.0000000000 0.020650216 0.000000000
## Hospitals 0.015111091 0.003846484 0.0417952754 0.010033171 0.010243011
## Diseases 0.102794665 0.043411511 0.0016434344 0.020922848 0.022472434
## Airplane High Blood Needles Hospitals
## House 0.033212490 0.000000000 0.005216821 0.000000000 0.025095101
## Crowds 0.028150348 0.026409979 0.000000000 0.004504186 0.047146822
## Spaces 0.001428999 0.019625796 0.000000000 0.000000000 0.000000000
## People 0.000000000 0.020134460 0.014409971 0.002808349 0.045457309
## Speech 0.046092686 0.057087390 0.024695416 0.020422472 0.047030512
## Bathroom 0.018297262 0.000000000 0.030465033 0.003112190 0.050003093
## Eating 0.022493774 0.006404157 0.000000000 0.010148351 0.023177163
## Spiders 0.028388470 0.023861053 0.003025998 0.012357524 0.028361309
## Bugs 0.000000000 0.015160036 0.001391433 0.030483565 0.023165076
## Mice 0.001042260 0.020077433 0.000000000 0.005814178 0.025534175
## Snakes 0.051137146 0.063503530 0.032806590 0.056277281 0.015111091
## Bats 0.017489023 0.015332144 0.041566461 0.008454339 0.003846484
## Tunnels 0.051243077 0.031994176 0.006422323 0.000000000 0.041795275
## Closed 0.019464354 0.063946756 0.026415781 0.020650216 0.010033171
## Bridges 0.069230409 0.165341479 0.000215742 0.000000000 0.010243011
## Airplane 0.000000000 0.166799840 0.001134477 0.009233452 0.060492443
## High 0.166799840 0.000000000 0.024100924 0.015329287 0.021790949
## Blood 0.001134477 0.024100924 0.000000000 0.180879472 0.079613492
## Needles 0.009233452 0.015329287 0.180879472 0.000000000 0.220210718
## Hospitals 0.060492443 0.021790949 0.079613492 0.220210718 0.000000000
## Diseases 0.093481505 0.000000000 0.024535109 0.033711674 0.025360151
## Diseases
## House 0.003363398
## Crowds 0.012900497
## Spaces 0.000000000
## People 0.029162470
## Speech 0.047820166
## Bathroom 0.126404537
## Eating 0.034029880
## Spiders 0.038664618
## Bugs 0.005231537
## Mice 0.029661208
## Snakes 0.102794665
## Bats 0.043411511
## Tunnels 0.001643434
## Closed 0.020922848
## Bridges 0.022472434
## Airplane 0.093481505
## High 0.000000000
## Blood 0.024535109
## Needles 0.033711674
## Hospitals 0.025360151
## Diseases 0.000000000pr$errors$R2## [1] 0.211 0.227 0.088 0.160 0.119 0.086 0.113 0.135 0.112 0.099 0.113 0.085
## [13] 0.171 0.152 0.128 0.102 0.140 0.077 0.117 0.131 0.098Panic network and predictability (variables in same order as adjacency matrix):
n5$graph## Panic House Crowds Spaces People
## Panic 0.000000000 0.088094867 0.116600620 0.07370150 0.000000000
## House 0.088094867 0.000000000 0.148572739 0.11604422 0.048114665
## Crowds 0.116600620 0.148572739 0.000000000 0.04537027 0.146251807
## Spaces 0.073701499 0.116044219 0.045370272 0.00000000 0.000000000
## People 0.000000000 0.048114665 0.146251807 0.00000000 0.000000000
## Speech 0.000000000 0.000000000 0.070968106 0.00000000 0.152195627
## Bathroom 0.000000000 0.023257778 0.043685650 0.02290086 0.015328615
## Eating 0.037222577 0.095855140 0.113706832 0.03490829 0.122281857
## Spiders 0.000000000 0.022991528 0.003668701 0.00000000 0.029577627
## Bugs 0.000000000 0.000000000 0.016722091 0.00000000 0.040939025
## Mice 0.000000000 0.035535299 0.000000000 0.01339933 0.013764443
## Snakes -0.013438786 0.002589709 0.000000000 0.00000000 0.007678849
## Bats 0.001972215 0.011664759 0.010020560 0.00000000 0.000000000
## Tunnels 0.054672155 0.008046048 0.073937023 0.03350690 0.004506108
## Closed 0.050794664 0.042784795 0.060677538 0.00000000 0.000000000
## Bridges 0.022223432 0.005066622 0.000000000 0.05181926 0.026677656
## Airplane 0.017876991 0.036558258 0.028982684 0.00000000 0.000000000
## High 0.001464776 0.000000000 0.024904802 0.01864049 0.022659900
## Blood 0.000000000 0.000000000 0.000000000 0.00000000 0.011841246
## Needles 0.000000000 0.000000000 0.003408924 0.00000000 0.005381789
## Hospitals 0.012623672 0.028222021 0.047575433 0.00000000 0.046723740
## Diseases 0.014075821 0.000000000 0.010892222 0.00000000 0.027367285
## Speech Bathroom Eating Spiders Bugs
## Panic 0.000000000 0.000000000 0.0372225766 0.000000000 0.000000000
## House 0.000000000 0.023257778 0.0958551403 0.022991528 0.000000000
## Crowds 0.070968106 0.043685650 0.1137068319 0.003668701 0.016722091
## Spaces 0.000000000 0.022900862 0.0349082894 0.000000000 0.000000000
## People 0.152195627 0.015328615 0.1222818571 0.029577627 0.040939025
## Speech 0.000000000 0.043602059 0.0102623892 0.043893911 0.008900001
## Bathroom 0.043602059 0.000000000 0.0693583862 0.031922443 0.015969674
## Eating 0.010262389 0.069358386 0.0000000000 0.000000000 0.008250729
## Spiders 0.043893911 0.031922443 0.0000000000 0.000000000 0.233487621
## Bugs 0.008900001 0.015969674 0.0082507288 0.233487621 0.000000000
## Mice 0.003081020 0.039773194 0.0000000000 0.032324079 0.073294623
## Snakes 0.085255137 0.045702630 0.0000000000 0.054932620 0.014302820
## Bats 0.010331905 0.020405383 0.0000000000 0.066906582 0.057231925
## Tunnels 0.003822850 0.029875804 0.0144252076 0.000000000 0.012822753
## Closed 0.019992953 0.000000000 0.0000000000 0.034471626 0.000000000
## Bridges 0.000000000 0.004353181 0.0108104076 0.062810760 0.000000000
## Airplane 0.041379057 0.018809604 0.0269467671 0.034620660 0.000000000
## High 0.057348572 0.000000000 0.0008945088 0.022461861 0.016137624
## Blood 0.028357506 0.031016693 0.0000000000 0.004466446 0.000000000
## Needles 0.018887696 0.000000000 0.0110954123 0.009267417 0.025181510
## Hospitals 0.046706667 0.046521069 0.0157282696 0.028541435 0.023494880
## Diseases 0.052407178 0.126037843 0.0307428405 0.038395719 0.001773131
## Mice Snakes Bats Tunnels Closed
## Panic 0.00000000 -0.013438786 0.001972215 0.0546721548 0.050794664
## House 0.03553530 0.002589709 0.011664759 0.0080460479 0.042784795
## Crowds 0.00000000 0.000000000 0.010020560 0.0739370234 0.060677538
## Spaces 0.01339933 0.000000000 0.000000000 0.0335069029 0.000000000
## People 0.01376444 0.007678849 0.000000000 0.0045061078 0.000000000
## Speech 0.00308102 0.085255137 0.010331905 0.0038228499 0.019992953
## Bathroom 0.03977319 0.045702630 0.020405383 0.0298758038 0.000000000
## Eating 0.00000000 0.000000000 0.000000000 0.0144252076 0.000000000
## Spiders 0.03232408 0.054932620 0.066906582 0.0000000000 0.034471626
## Bugs 0.07329462 0.014302820 0.057231925 0.0128227531 0.000000000
## Mice 0.00000000 0.134657670 0.151748861 0.0000000000 0.036485461
## Snakes 0.13465767 0.000000000 0.095176129 0.0172990900 0.017029129
## Bats 0.15174886 0.095176129 0.000000000 0.0117041905 0.000000000
## Tunnels 0.00000000 0.017299090 0.011704191 0.0000000000 0.236139688
## Closed 0.03648546 0.017029129 0.000000000 0.2361396882 0.000000000
## Bridges 0.02294510 0.024155660 0.006569527 0.1584318617 0.000000000
## Airplane 0.00000000 0.051485210 0.009305356 0.0428156965 0.020369718
## High 0.01115163 0.065458813 0.014374392 0.0389025112 0.060179524
## Blood 0.00000000 0.028232404 0.040617072 0.0000000000 0.025306924
## Needles 0.00000000 0.060424854 0.009454084 0.0008418955 0.018901814
## Hospitals 0.02767878 0.018176298 0.005102772 0.0438088525 0.005480906
## Diseases 0.03037293 0.099843487 0.046367248 0.0008943999 0.022122981
## Bridges Airplane High Blood Needles
## Panic 0.022223432 0.017876991 0.0014647760 0.000000000 0.0000000000
## House 0.005066622 0.036558258 0.0000000000 0.000000000 0.0000000000
## Crowds 0.000000000 0.028982684 0.0249048024 0.000000000 0.0034089240
## Spaces 0.051819264 0.000000000 0.0186404891 0.000000000 0.0000000000
## People 0.026677656 0.000000000 0.0226598999 0.011841246 0.0053817891
## Speech 0.000000000 0.041379057 0.0573485722 0.028357506 0.0188876960
## Bathroom 0.004353181 0.018809604 0.0000000000 0.031016693 0.0000000000
## Eating 0.010810408 0.026946767 0.0008945088 0.000000000 0.0110954123
## Spiders 0.062810760 0.034620660 0.0224618612 0.004466446 0.0092674166
## Bugs 0.000000000 0.000000000 0.0161376235 0.000000000 0.0251815096
## Mice 0.022945103 0.000000000 0.0111516279 0.000000000 0.0000000000
## Snakes 0.024155660 0.051485210 0.0654588134 0.028232404 0.0604248535
## Bats 0.006569527 0.009305356 0.0143743920 0.040617072 0.0094540839
## Tunnels 0.158431862 0.042815697 0.0389025112 0.000000000 0.0008418955
## Closed 0.000000000 0.020369718 0.0601795238 0.025306924 0.0189018140
## Bridges 0.000000000 0.069595216 0.1623734651 0.001023215 0.0000000000
## Airplane 0.069595216 0.000000000 0.1686026434 0.007961454 0.0117786913
## High 0.162373465 0.168602643 0.0000000000 0.024175313 0.0161737771
## Blood 0.001023215 0.007961454 0.0241753129 0.000000000 0.1801794821
## Needles 0.000000000 0.011778691 0.0161737771 0.180179482 0.0000000000
## Hospitals 0.008160053 0.059578228 0.0227984996 0.079489331 0.2220573033
## Diseases 0.022384759 0.091280717 0.0000000000 0.018407612 0.0367215180
## Hospitals Diseases
## Panic 0.012623672 0.0140758212
## House 0.028222021 0.0000000000
## Crowds 0.047575433 0.0108922218
## Spaces 0.000000000 0.0000000000
## People 0.046723740 0.0273672848
## Speech 0.046706667 0.0524071779
## Bathroom 0.046521069 0.1260378433
## Eating 0.015728270 0.0307428405
## Spiders 0.028541435 0.0383957189
## Bugs 0.023494880 0.0017731314
## Mice 0.027678777 0.0303729256
## Snakes 0.018176298 0.0998434870
## Bats 0.005102772 0.0463672478
## Tunnels 0.043808852 0.0008943999
## Closed 0.005480906 0.0221229809
## Bridges 0.008160053 0.0223847586
## Airplane 0.059578228 0.0912807167
## High 0.022798500 0.0000000000
## Blood 0.079489331 0.0184076119
## Needles 0.222057303 0.0367215180
## Hospitals 0.000000000 0.0231882863
## Diseases 0.023188286 0.0000000000pr5_errors## [1] 0.037 0.219 0.235 0.096 0.155 0.120 0.080 0.105 0.129 0.096 0.092 0.117
## [13] 0.079 0.176 0.158 0.123 0.102 0.136 0.077 0.120 0.133 0.097Male and female networks with adjacency matrices (first male, then female):
plot(n2female, layout=g2$layout, title="Female", legend=FALSE, vsize=9)
plot(n2male, layout=g2$layout, title="Men", legend=FALSE, vsize=9)
n2female$graph## o_house o_crowds o_o_spaces o_people o_speech
## o_house 0.0000000000 0.212864088 0.071856703 0.057404345 0.006252168
## o_crowds 0.2128640878 0.000000000 0.068955288 0.099279021 0.037421110
## o_o_spaces 0.0718567025 0.068955288 0.000000000 0.014930816 0.001833921
## o_people 0.0574043451 0.099279021 0.014930816 0.000000000 0.143159390
## o_speech 0.0062521679 0.037421110 0.001833921 0.143159390 0.000000000
## o_bathroom 0.0491449586 0.000000000 0.041505315 0.000000000 0.044120719
## o_public 0.1037419144 0.042134940 0.033866038 0.082586851 0.018093090
## o_spiders 0.0146242198 0.000000000 0.000000000 0.035177068 0.058221538
## o_bugs 0.0007605832 0.000000000 0.000000000 0.035779718 0.012714629
## o_mice 0.0199425199 0.000000000 0.003856918 0.032066428 0.028882219
## o_snakes 0.0000000000 0.000000000 0.000000000 0.023610614 0.076452108
## o_bats 0.0099004713 0.013345361 0.000000000 0.009901109 0.000000000
## o_tunnels 0.0338150622 0.094095174 0.014808226 0.004650834 0.018617372
## o_c_places 0.0328720608 0.099747503 0.000000000 0.003084599 0.000000000
## o_bridges 0.0000000000 0.009177870 0.000000000 0.049383642 0.000000000
## o_airplane 0.0378575038 0.023856722 0.000000000 0.000000000 0.056016612
## o_h_places 0.0000000000 0.037186727 0.037945587 0.011296163 0.049079253
## o_blood 0.0000000000 0.000000000 0.000000000 0.000000000 0.004553295
## o_needles 0.0000000000 0.007599676 0.000000000 0.006143726 0.002666515
## o_hospital 0.0143470657 0.052544363 0.000000000 0.036635649 0.035629656
## o_diseases 0.0366615297 0.001946826 0.000000000 0.000000000 0.006696372
## o_bathroom o_public o_spiders o_bugs o_mice
## o_house 0.0491449586 0.103741914 0.014624220 0.0007605832 0.019942520
## o_crowds 0.0000000000 0.042134940 0.000000000 0.0000000000 0.000000000
## o_o_spaces 0.0415053148 0.033866038 0.000000000 0.0000000000 0.003856918
## o_people 0.0000000000 0.082586851 0.035177068 0.0357797184 0.032066428
## o_speech 0.0441207190 0.018093090 0.058221538 0.0127146288 0.028882219
## o_bathroom 0.0000000000 0.031857749 0.042140884 0.0093664646 0.063678251
## o_public 0.0318577495 0.000000000 0.000000000 0.0000000000 0.007332819
## o_spiders 0.0421408845 0.000000000 0.000000000 0.2423403292 0.030388748
## o_bugs 0.0093664646 0.000000000 0.242340329 0.0000000000 0.075790012
## o_mice 0.0636782513 0.007332819 0.030388748 0.0757900124 0.000000000
## o_snakes 0.0660358680 0.000000000 0.054924559 0.0110568795 0.148744797
## o_bats 0.0226058224 0.012973438 0.048546886 0.0735103878 0.168679374
## o_tunnels 0.0477261575 0.009545486 0.000000000 0.0000000000 0.000000000
## o_c_places 0.0000000000 0.000000000 0.032049090 0.0000000000 0.048502953
## o_bridges 0.0102210153 0.000000000 0.057029814 0.0000000000 0.000000000
## o_airplane 0.0009387855 0.028619162 0.019133685 0.0000000000 0.000000000
## o_h_places 0.0000000000 0.023611972 0.037664933 0.0087758783 0.028757873
## o_blood 0.0490979198 0.000000000 0.000000000 0.0142352325 0.000000000
## o_needles 0.0103008040 0.000000000 0.001619324 0.0291809720 0.018264292
## o_hospital 0.0799900741 0.015380600 0.020672560 0.0163349451 0.004095910
## o_diseases 0.1154985377 0.031085763 0.031114604 0.0162278403 0.018310673
## o_snakes o_bats o_tunnels o_c_places o_bridges
## o_house 0.00000000 0.009900471 0.033815062 0.032872061 0.000000000
## o_crowds 0.00000000 0.013345361 0.094095174 0.099747503 0.009177870
## o_o_spaces 0.00000000 0.000000000 0.014808226 0.000000000 0.000000000
## o_people 0.02361061 0.009901109 0.004650834 0.003084599 0.049383642
## o_speech 0.07645211 0.000000000 0.018617372 0.000000000 0.000000000
## o_bathroom 0.06603587 0.022605822 0.047726157 0.000000000 0.010221015
## o_public 0.00000000 0.012973438 0.009545486 0.000000000 0.000000000
## o_spiders 0.05492456 0.048546886 0.000000000 0.032049090 0.057029814
## o_bugs 0.01105688 0.073510388 0.000000000 0.000000000 0.000000000
## o_mice 0.14874480 0.168679374 0.000000000 0.048502953 0.000000000
## o_snakes 0.00000000 0.084549143 0.023192105 0.000000000 0.018126882
## o_bats 0.08454914 0.000000000 0.000000000 0.004693481 0.000000000
## o_tunnels 0.02319211 0.000000000 0.000000000 0.224517747 0.172524421
## o_c_places 0.00000000 0.004693481 0.224517747 0.000000000 0.005341344
## o_bridges 0.01812688 0.000000000 0.172524421 0.005341344 0.000000000
## o_airplane 0.05105564 0.042969495 0.060356990 0.031924814 0.049920043
## o_h_places 0.03115275 0.023231392 0.039261418 0.076444681 0.177432537
## o_blood 0.01289729 0.032000052 0.000000000 0.013909771 0.015596660
## o_needles 0.03295932 0.000000000 0.000000000 0.037283196 0.000000000
## o_hospital 0.02591254 0.000000000 0.027412646 0.022133976 0.019046751
## o_diseases 0.12468759 0.050439073 0.007093181 0.035536901 0.019750255
## o_airplane o_h_places o_blood o_needles o_hospital
## o_house 0.0378575038 0.000000000 0.000000000 0.000000000 0.01434707
## o_crowds 0.0238567222 0.037186727 0.000000000 0.007599676 0.05254436
## o_o_spaces 0.0000000000 0.037945587 0.000000000 0.000000000 0.00000000
## o_people 0.0000000000 0.011296163 0.000000000 0.006143726 0.03663565
## o_speech 0.0560166122 0.049079253 0.004553295 0.002666515 0.03562966
## o_bathroom 0.0009387855 0.000000000 0.049097920 0.010300804 0.07999007
## o_public 0.0286191618 0.023611972 0.000000000 0.000000000 0.01538060
## o_spiders 0.0191336853 0.037664933 0.000000000 0.001619324 0.02067256
## o_bugs 0.0000000000 0.008775878 0.014235233 0.029180972 0.01633495
## o_mice 0.0000000000 0.028757873 0.000000000 0.018264292 0.00409591
## o_snakes 0.0510556441 0.031152746 0.012897289 0.032959321 0.02591254
## o_bats 0.0429694947 0.023231392 0.032000052 0.000000000 0.00000000
## o_tunnels 0.0603569897 0.039261418 0.000000000 0.000000000 0.02741265
## o_c_places 0.0319248141 0.076444681 0.013909771 0.037283196 0.02213398
## o_bridges 0.0499200428 0.177432537 0.015596660 0.000000000 0.01904675
## o_airplane 0.0000000000 0.171507815 0.016171836 0.000000000 0.06919011
## o_h_places 0.1715078148 0.000000000 0.000000000 0.000000000 0.02353793
## o_blood 0.0161718362 0.000000000 0.000000000 0.167398312 0.10832027
## o_needles 0.0000000000 0.000000000 0.167398312 0.000000000 0.21240369
## o_hospital 0.0691901108 0.023537931 0.108320269 0.212403689 0.00000000
## o_diseases 0.0759151068 0.009532694 0.018237084 0.025378926 0.01560005
## o_diseases
## o_house 0.036661530
## o_crowds 0.001946826
## o_o_spaces 0.000000000
## o_people 0.000000000
## o_speech 0.006696372
## o_bathroom 0.115498538
## o_public 0.031085763
## o_spiders 0.031114604
## o_bugs 0.016227840
## o_mice 0.018310673
## o_snakes 0.124687594
## o_bats 0.050439073
## o_tunnels 0.007093181
## o_c_places 0.035536901
## o_bridges 0.019750255
## o_airplane 0.075915107
## o_h_places 0.009532694
## o_blood 0.018237084
## o_needles 0.025378926
## o_hospital 0.015600051
## o_diseases 0.000000000n2male$graph## o_house o_crowds o_o_spaces o_people o_speech
## o_house 0.000000000 0.115928512 0.152193521 0.06993342 0.00000000
## o_crowds 0.115928512 0.000000000 0.032214893 0.14207347 0.08644946
## o_o_spaces 0.152193521 0.032214893 0.000000000 0.00000000 0.00000000
## o_people 0.069933424 0.142073474 0.000000000 0.00000000 0.15724867
## o_speech 0.000000000 0.086449459 0.000000000 0.15724867 0.00000000
## o_bathroom 0.001557205 0.075219423 0.000000000 0.01385072 0.03273998
## o_public 0.096190569 0.177605081 0.019072662 0.17618178 0.00000000
## o_spiders 0.003385829 0.039180690 0.006510889 0.02561384 0.02668763
## o_bugs 0.000000000 0.052315868 0.000000000 0.01962040 0.01058847
## o_mice 0.004887789 0.013204833 0.012462431 0.01039376 0.00000000
## o_snakes 0.000000000 0.000000000 0.000000000 0.00000000 0.07584999
## o_bats 0.000000000 0.001979449 0.000000000 0.00000000 0.03975454
## o_tunnels 0.000000000 0.074180311 0.045186367 0.01583489 0.00000000
## o_c_places 0.045925177 0.019399075 0.025024300 0.00000000 0.04066481
## o_bridges 0.000000000 0.000000000 0.102204499 0.01824049 0.00000000
## o_airplane 0.010369713 0.028877051 0.038663202 0.00000000 0.04625781
## o_h_places 0.000000000 0.008492689 0.000000000 0.01915785 0.06278661
## o_blood 0.000000000 0.011259678 0.000000000 0.01123047 0.04582363
## o_needles 0.000000000 0.000000000 0.000000000 0.00000000 0.02394302
## o_hospital 0.045894129 0.035701583 0.000000000 0.05269229 0.06157333
## o_diseases 0.000000000 0.005847120 0.000000000 0.02317482 0.07842151
## o_bathroom o_public o_spiders o_bugs o_mice
## o_house 0.001557205 0.096190569 0.003385829 0.000000000 0.004887789
## o_crowds 0.075219423 0.177605081 0.039180690 0.052315868 0.013204833
## o_o_spaces 0.000000000 0.019072662 0.006510889 0.000000000 0.012462431
## o_people 0.013850725 0.176181781 0.025613838 0.019620403 0.010393760
## o_speech 0.032739982 0.000000000 0.026687630 0.010588465 0.000000000
## o_bathroom 0.000000000 0.088015787 0.045678731 0.000000000 0.009545328
## o_public 0.088015787 0.000000000 0.000000000 0.055901308 0.000000000
## o_spiders 0.045678731 0.000000000 0.000000000 0.196034274 0.006015912
## o_bugs 0.000000000 0.055901308 0.196034274 0.000000000 0.027899182
## o_mice 0.009545328 0.000000000 0.006015912 0.027899182 0.000000000
## o_snakes 0.003571168 0.013108133 0.084010789 0.013678809 0.082122018
## o_bats 0.013356128 0.000000000 0.077244904 0.029417042 0.068041659
## o_tunnels 0.012695695 0.004098697 0.000000000 0.019791601 0.000000000
## o_c_places 0.000000000 0.014115080 0.011271069 0.000000000 0.000000000
## o_bridges 0.004106954 0.007727515 0.023544563 0.000000000 0.014131267
## o_airplane 0.023552796 0.000357294 0.031138479 0.000000000 0.010693369
## o_h_places 0.000000000 0.000000000 0.005994408 0.037158345 0.032729164
## o_blood 0.000000000 0.000000000 0.021143619 0.000000000 0.000000000
## o_needles 0.000000000 0.010854712 0.007904312 0.022790215 0.018681977
## o_hospital 0.031671030 0.012193295 0.030731546 0.009606367 0.072122579
## o_diseases 0.127428016 0.007354002 0.052344733 0.000000000 0.057515565
## o_snakes o_bats o_tunnels o_c_places o_bridges
## o_house 0.000000000 0.000000000 0.000000000 0.045925177 0.000000000
## o_crowds 0.000000000 0.001979449 0.074180311 0.019399075 0.000000000
## o_o_spaces 0.000000000 0.000000000 0.045186367 0.025024300 0.102204499
## o_people 0.000000000 0.000000000 0.015834890 0.000000000 0.018240492
## o_speech 0.075849988 0.039754538 0.000000000 0.040664805 0.000000000
## o_bathroom 0.003571168 0.013356128 0.012695695 0.000000000 0.004106954
## o_public 0.013108133 0.000000000 0.004098697 0.014115080 0.007727515
## o_spiders 0.084010789 0.077244904 0.000000000 0.011271069 0.023544563
## o_bugs 0.013678809 0.029417042 0.019791601 0.000000000 0.000000000
## o_mice 0.082122018 0.068041659 0.000000000 0.000000000 0.014131267
## o_snakes 0.000000000 0.087364595 0.009506938 0.030289648 0.024616447
## o_bats 0.087364595 0.000000000 0.000000000 0.000000000 0.060720055
## o_tunnels 0.009506938 0.000000000 0.000000000 0.251199864 0.146310433
## o_c_places 0.030289648 0.000000000 0.251199864 0.000000000 0.000000000
## o_bridges 0.024616447 0.060720055 0.146310433 0.000000000 0.000000000
## o_airplane 0.040160070 0.000000000 0.036929748 0.007874582 0.079293701
## o_h_places 0.076682304 0.000000000 0.025820117 0.055439320 0.153476734
## o_blood 0.054517820 0.023760432 0.002936429 0.047127173 0.000000000
## o_needles 0.064853119 0.030586658 0.000000000 0.002590411 0.000000000
## o_hospital 0.017738416 0.017498325 0.038039219 0.000000000 0.000000000
## o_diseases 0.096933072 0.039451001 0.005552318 0.001255334 0.018413837
## o_airplane o_h_places o_blood o_needles o_hospital
## o_house 0.010369713 0.000000000 0.000000000 0.000000000 0.045894129
## o_crowds 0.028877051 0.008492689 0.011259678 0.000000000 0.035701583
## o_o_spaces 0.038663202 0.000000000 0.000000000 0.000000000 0.000000000
## o_people 0.000000000 0.019157851 0.011230467 0.000000000 0.052692293
## o_speech 0.046257811 0.062786613 0.045823634 0.023943024 0.061573326
## o_bathroom 0.023552796 0.000000000 0.000000000 0.000000000 0.031671030
## o_public 0.000357294 0.000000000 0.000000000 0.010854712 0.012193295
## o_spiders 0.031138479 0.005994408 0.021143619 0.007904312 0.030731546
## o_bugs 0.000000000 0.037158345 0.000000000 0.022790215 0.009606367
## o_mice 0.010693369 0.032729164 0.000000000 0.018681977 0.072122579
## o_snakes 0.040160070 0.076682304 0.054517820 0.064853119 0.017738416
## o_bats 0.000000000 0.000000000 0.023760432 0.030586658 0.017498325
## o_tunnels 0.036929748 0.025820117 0.002936429 0.000000000 0.038039219
## o_c_places 0.007874582 0.055439320 0.047127173 0.002590411 0.000000000
## o_bridges 0.079293701 0.153476734 0.000000000 0.000000000 0.000000000
## o_airplane 0.000000000 0.145972578 0.006185784 0.018251059 0.045329295
## o_h_places 0.145972578 0.000000000 0.027599406 0.045640526 0.017943277
## o_blood 0.006185784 0.027599406 0.000000000 0.176997167 0.035009063
## o_needles 0.018251059 0.045640526 0.176997167 0.000000000 0.222598186
## o_hospital 0.045329295 0.017943277 0.035009063 0.222598186 0.000000000
## o_diseases 0.089229065 0.000000000 0.035679986 0.032991328 0.015580592
## o_diseases
## o_house 0.000000000
## o_crowds 0.005847120
## o_o_spaces 0.000000000
## o_people 0.023174821
## o_speech 0.078421514
## o_bathroom 0.127428016
## o_public 0.007354002
## o_spiders 0.052344733
## o_bugs 0.000000000
## o_mice 0.057515565
## o_snakes 0.096933072
## o_bats 0.039451001
## o_tunnels 0.005552318
## o_c_places 0.001255334
## o_bridges 0.018413837
## o_airplane 0.089229065
## o_h_places 0.000000000
## o_blood 0.035679986
## o_needles 0.032991328
## o_hospital 0.015580592
## o_diseases 0.000000000