Named accessors for the tables inside a net_network_comparison object
(from compare_networks()). Each returns a plain data frame with one row
per unit and no row names. Numeric columns are rounded to digits
decimals; p-value columns are never rounded.
Usage
# S3 method for class 'net_network_comparison'
summary(object, pair = NULL, digits = 2L, ...)
edge_differences(x, pair = NULL, digits = 2L)
node_differences(x, pair = NULL, measure = NULL, digits = 2L)
global_differences(x, pair = NULL, digits = 2L)
network_metrics(x, digits = 2L)
# S3 method for class 'net_table'
print(x, digits = attr(x, "digits") %||% 2L, ...)Arguments
- pair
Optional selection of comparisons. Any of: the pair name(s) as printed (
"A vs B"); the two network names (c("A", "B"), either order); one network name ("A", every pair it takes part in); or index/indices into the pair table (1,c(1, 3)).NULLkeeps all.- digits
Decimals kept in numeric columns. Default
2.- ...
Ignored.
- x, object
A
net_network_comparisonobject (forprint.net_table(), a table returned by one of these verbs).- measure
Optional centrality measure name(s) to keep.
Value
summary(): one row per pair –pair,network_a,network_b,n_cells,n_differing,share_higher_a,share_higher_b,mean_abs_diff,max_abs_diff,pearson,spearman,cosine,jaccard,top_edge,top_edge_higher, and with inferencen_sig_edges,n_sig_nodes,m_stat,m_p,s_stat,s_p. The per-edge, per-node, per-metric and per-network tables are the four verbs below.edge_differences(): one row per pair x transition:pair,network_a,network_b,from,to,weight_a,weight_b,diff,abs_diff,rel_diff,ratio,log_ratio,rank_a,rank_b,rank_diff,percentile_diff,status,higher, and the inference columns whentestwas used.node_differences(): one row per pair x state x measure:pair,network_a,network_b,node,measure,value_a,value_b,diff,abs_diff,rank_a,rank_b,higher, plus inference columns. Errors (classnestimate_compare_no_nodes) whencompare_networks()was called with nomeasures.global_differences(): one row per pair x metric:pair,network_a,network_b,category,metric,key,value, plus inference rows and columns.network_metrics(): one row per network x structural metric:network,metric,value.
Every table is a data frame of class net_table whose print() shows
whole numbers without decimals, exact zeros as 0, other values with
digits decimals, and p-values with three decimals; print() itself
returns the table invisibly.
Examples
achievers <- build_network(group_regulation_long, method = "relative",
actor = "Actor", action = "Action",
time = "Time", group = "Achiever")
cmp <- compare_networks(achievers)
summary(cmp)
#> pair network_a network_b n_cells n_differing share_higher_a
#> High vs Low High Low 81 78 0.51
#> share_higher_b mean_abs_diff max_abs_diff pearson spearman cosine jaccard
#> 0.46 0.03 0.21 0.92 0.92 0.95 0.75
#> top_edge top_edge_higher
#> discuss -> consensus High
edge_differences(cmp)
#> pair network_a network_b from to weight_a weight_b diff
#> High vs Low High Low adapt adapt 0 0 0
#> High vs Low High Low adapt cohesion 0.26 0.28 -0.01
#> High vs Low High Low adapt consensus 0.52 0.46 0.06
#> High vs Low High Low adapt coregulate 0 0.03 -0.03
#> High vs Low High Low adapt discuss 0.04 0.07 -0.03
#> High vs Low High Low adapt emotion 0.14 0.11 0.03
#> High vs Low High Low adapt monitor 0.03 0.04 -0.01
#> High vs Low High Low adapt plan 0.01 0.02 0
#> High vs Low High Low adapt synthesis 0 0 0
#> High vs Low High Low cohesion adapt 0.01 0 0.01
#> High vs Low High Low cohesion cohesion 0.04 0.01 0.04
#> High vs Low High Low cohesion consensus 0.54 0.45 0.09
#> High vs Low High Low cohesion coregulate 0.08 0.17 -0.09
#> High vs Low High Low cohesion discuss 0.04 0.08 -0.04
#> High vs Low High Low cohesion emotion 0.12 0.11 0.01
#> High vs Low High Low cohesion monitor 0.02 0.05 -0.04
#> High vs Low High Low cohesion plan 0.15 0.13 0.02
#> High vs Low High Low cohesion synthesis 0.01 0 0.01
#> High vs Low High Low consensus adapt 0 0.01 0
#> High vs Low High Low consensus cohesion 0.02 0.01 0.01
#> High vs Low High Low consensus consensus 0.08 0.08 0
#> High vs Low High Low consensus coregulate 0.17 0.21 -0.04
#> High vs Low High Low consensus discuss 0.23 0.14 0.10
#> High vs Low High Low consensus emotion 0.08 0.06 0.02
#> High vs Low High Low consensus monitor 0.04 0.06 -0.02
#> High vs Low High Low consensus plan 0.36 0.43 -0.07
#> High vs Low High Low consensus synthesis 0.01 0.01 0
#> High vs Low High Low coregulate adapt 0.02 0.01 0.01
#> High vs Low High Low coregulate cohesion 0.04 0.04 0
#> High vs Low High Low coregulate consensus 0.11 0.16 -0.05
#> High vs Low High Low coregulate coregulate 0.01 0.03 -0.02
#> High vs Low High Low coregulate discuss 0.23 0.31 -0.07
#> High vs Low High Low coregulate emotion 0.20 0.15 0.06
#> High vs Low High Low coregulate monitor 0.10 0.08 0.02
#> High vs Low High Low coregulate plan 0.27 0.22 0.05
#> High vs Low High Low coregulate synthesis 0.02 0.02 0
#> High vs Low High Low discuss adapt 0.02 0.12 -0.10
#> High vs Low High Low discuss cohesion 0.06 0.03 0.03
#> High vs Low High Low discuss consensus 0.42 0.21 0.21
#> High vs Low High Low discuss coregulate 0.07 0.10 -0.02
#> High vs Low High Low discuss discuss 0.17 0.22 -0.05
#> High vs Low High Low discuss emotion 0.11 0.10 0.01
#> High vs Low High Low discuss monitor 0.02 0.03 -0.01
#> High vs Low High Low discuss plan 0.01 0.01 0
#> High vs Low High Low discuss synthesis 0.11 0.18 -0.07
#> High vs Low High Low emotion adapt 0 0 0
#> High vs Low High Low emotion cohesion 0.33 0.32 0
#> High vs Low High Low emotion consensus 0.34 0.30 0.03
#> High vs Low High Low emotion coregulate 0.02 0.05 -0.02
#> High vs Low High Low emotion discuss 0.12 0.08 0.04
#> High vs Low High Low emotion emotion 0.06 0.09 -0.03
#> High vs Low High Low emotion monitor 0.03 0.04 -0.01
#> High vs Low High Low emotion plan 0.09 0.11 -0.02
#> High vs Low High Low emotion synthesis 0.01 0 0.01
#> High vs Low High Low monitor adapt 0.01 0.01 0
#> High vs Low High Low monitor cohesion 0.05 0.06 -0.01
#> High vs Low High Low monitor consensus 0.16 0.16 0
#> High vs Low High Low monitor coregulate 0.05 0.06 -0.01
#> High vs Low High Low monitor discuss 0.37 0.38 -0.01
#> High vs Low High Low monitor emotion 0.10 0.09 0.01
#> High vs Low High Low monitor monitor 0.02 0.02 0
#> High vs Low High Low monitor plan 0.23 0.21 0.02
#> High vs Low High Low monitor synthesis 0.02 0.01 0.01
#> High vs Low High Low plan adapt 0 0 0
#> High vs Low High Low plan cohesion 0.03 0.02 0.01
#> High vs Low High Low plan consensus 0.29 0.29 0.01
#> High vs Low High Low plan coregulate 0.02 0.01 0.01
#> High vs Low High Low plan discuss 0.06 0.07 -0.01
#> High vs Low High Low plan emotion 0.18 0.12 0.07
#> High vs Low High Low plan monitor 0.08 0.08 0
#> High vs Low High Low plan plan 0.33 0.42 -0.09
#> High vs Low High Low plan synthesis 0 0 0
#> High vs Low High Low synthesis adapt 0.14 0.30 -0.16
#> High vs Low High Low synthesis cohesion 0.03 0.04 -0.01
#> High vs Low High Low synthesis consensus 0.58 0.39 0.19
#> High vs Low High Low synthesis coregulate 0.01 0.07 -0.05
#> High vs Low High Low synthesis discuss 0.03 0.09 -0.06
#> High vs Low High Low synthesis emotion 0.06 0.07 -0.01
#> High vs Low High Low synthesis monitor 0 0.02 -0.02
#> High vs Low High Low synthesis plan 0.14 0.02 0.12
#> High vs Low High Low synthesis synthesis 0 0 0
#> abs_diff rel_diff ratio log_ratio rank_a rank_b rank_diff percentile_diff
#> 0 NA NA 0 3 3.50 -0.50 -0.01
#> 0.01 0.03 0.95 -0.01 70 70 0 0
#> 0.06 0.06 1.12 0.04 79 81 -2 -0.02
#> 0.03 1 0 -0.03 3 26 -23 -0.26
#> 0.03 0.31 0.52 -0.03 35 40 -5 -0.06
#> 0.03 0.12 1.27 0.03 58 54 4 0.05
#> 0.01 0.11 0.80 -0.01 29 29 0 0
#> 0 0.07 0.87 0 17 19 -2 -0.02
#> 0 NA NA 0 3 3.50 -0.50 -0.01
#> 0.01 1 NA 0.01 11 3.50 7.50 0.06
#> 0.04 0.74 6.62 0.04 38 11 27 0.33
#> 0.09 0.09 1.19 0.06 80 80 0 0
#> 0.09 0.35 0.49 -0.08 47 63 -16 -0.20
#> 0.04 0.35 0.49 -0.04 37 47 -10 -0.12
#> 0.01 0.03 1.05 0.01 56 55 1 0.01
#> 0.04 0.51 0.32 -0.03 20 34 -14 -0.17
#> 0.02 0.08 1.18 0.02 61 58 3 0.04
#> 0.01 1 NA 0.01 12 3.50 8.50 0.07
#> 0 0.14 0.76 0 9 10 -1 -0.01
#> 0.01 0.36 2.15 0.01 24 13 11 0.14
#> 0 0.02 1.04 0 49 46 3 0.04
#> 0.04 0.10 0.83 -0.03 64 65 -1 -0.01
#> 0.10 0.26 1.70 0.08 68 59 9 0.11
#> 0.02 0.13 1.30 0.02 48 37 11 0.14
#> 0.02 0.25 0.59 -0.02 34 35 -1 -0.01
#> 0.07 0.08 0.84 -0.05 76 79 -3 -0.04
#> 0 0.05 1.11 0 13 12 1 0.01
#> 0.01 0.33 2.01 0.01 25 15 10 0.12
#> 0 0.01 0.99 0 36 30 6 0.07
#> 0.05 0.18 0.69 -0.04 54 61 -7 -0.09
#> 0.02 0.40 0.42 -0.02 16 27 -11 -0.14
#> 0.07 0.13 0.77 -0.06 69 74 -5 -0.06
#> 0.06 0.17 1.40 0.05 66 60 6 0.07
#> 0.02 0.10 1.23 0.02 51 45 6 0.07
#> 0.05 0.10 1.23 0.04 71 68 3 0.04
#> 0 0.01 1.02 0 23 21 2 0.02
#> 0.10 0.67 0.20 -0.09 28 57 -29 -0.36
#> 0.03 0.31 1.88 0.03 42 28 14 0.17
#> 0.21 0.33 1.98 0.16 78 67 11 0.14
#> 0.02 0.14 0.75 -0.02 45 51 -6 -0.07
#> 0.05 0.13 0.76 -0.04 63 69 -6 -0.07
#> 0.01 0.06 1.13 0.01 55 52 3 0.04
#> 0.01 0.26 0.58 -0.01 19 25 -6 -0.07
#> 0 0.07 1.16 0 15 14 1 0.01
#> 0.07 0.25 0.60 -0.06 53 64 -11 -0.14
#> 0 0.35 2.08 0 7 9 -2 -0.02
#> 0 0 1 0 73 75 -2 -0.02
#> 0.03 0.05 1.11 0.03 75 72 3 0.04
#> 0.02 0.34 0.49 -0.02 26 33 -7 -0.09
#> 0.04 0.22 1.57 0.04 57 44 13 0.16
#> 0.03 0.20 0.67 -0.03 43 50 -7 -0.09
#> 0.01 0.14 0.75 -0.01 33 32 1 0.01
#> 0.02 0.10 0.81 -0.02 50 53 -3 -0.04
#> 0.01 1 NA 0.01 10 3.50 6.50 0.05
#> 0 0.01 0.98 0 14 16 -2 -0.02
#> 0.01 0.11 0.80 -0.01 39 36 3 0.04
#> 0 0 1.01 0 62 62 0 0
#> 0.01 0.12 0.79 -0.01 40 38 2 0.02
#> 0.01 0.01 0.97 -0.01 77 76 1 0.01
#> 0.01 0.06 1.12 0.01 52 48 4 0.05
#> 0 0.04 1.08 0 21.50 20 1.50 0.02
#> 0.02 0.04 1.09 0.02 67 66 1 0.01
#> 0.01 0.16 1.38 0.01 21.50 18 3.50 0.05
#> 0 0.39 2.26 0 6 8 -2 -0.02
#> 0.01 0.23 1.61 0.01 32 22 10 0.12
#> 0.01 0.01 1.02 0.01 72 71 1 0.01
#> 0.01 0.36 2.11 0.01 27 17 10 0.12
#> 0.01 0.11 0.81 -0.01 41 41 0 0
#> 0.07 0.22 1.58 0.06 65 56 9 0.11
#> 0 0 1.01 0 46 43 3 0.04
#> 0.09 0.12 0.79 -0.06 74 78 -4 -0.05
#> 0 0.84 11.29 0 8 7 1 0.01
#> 0.16 0.35 0.48 -0.13 59.50 73 -13.50 -0.16
#> 0.01 0.13 0.77 -0.01 30.50 31 -0.50 0
#> 0.19 0.20 1.49 0.13 81 77 4 0.05
#> 0.05 0.65 0.22 -0.05 18 39 -21 -0.26
#> 0.06 0.51 0.33 -0.06 30.50 49 -18.50 -0.22
#> 0.01 0.07 0.86 -0.01 44 42 2 0.02
#> 0.02 1 0 -0.02 3 23 -20 -0.22
#> 0.12 0.71 5.98 0.11 59.50 24 35.50 0.44
#> 0 NA NA 0 3 3.50 -0.50 -0.01
#> status higher
#> neither equal
#> both Low
#> both High
#> only_b Low
#> both Low
#> both High
#> both Low
#> both Low
#> neither equal
#> only_a High
#> both High
#> both High
#> both Low
#> both Low
#> both High
#> both Low
#> both High
#> only_a High
#> both Low
#> both High
#> both High
#> both Low
#> both High
#> both High
#> both Low
#> both Low
#> both High
#> both High
#> both Low
#> both Low
#> both Low
#> both Low
#> both High
#> both High
#> both High
#> both High
#> both Low
#> both High
#> both High
#> both Low
#> both Low
#> both High
#> both Low
#> both High
#> both Low
#> both High
#> both High
#> both High
#> both Low
#> both High
#> both Low
#> both Low
#> both Low
#> only_a High
#> both Low
#> both Low
#> both High
#> both Low
#> both Low
#> both High
#> both High
#> both High
#> both High
#> both High
#> both High
#> both High
#> both High
#> both Low
#> both High
#> both High
#> both Low
#> both High
#> both Low
#> both Low
#> both High
#> both Low
#> both Low
#> both Low
#> only_b Low
#> both High
#> neither equal
edge_differences(cmp, digits = 4)
#> pair network_a network_b from to weight_a weight_b
#> High vs Low High Low adapt adapt 0 0
#> High vs Low High Low adapt cohesion 0.2624 0.2772
#> High vs Low High Low adapt consensus 0.5177 0.4620
#> High vs Low High Low adapt coregulate 0 0.0299
#> High vs Low High Low adapt discuss 0.0355 0.0679
#> High vs Low High Low adapt emotion 0.1418 0.1114
#> High vs Low High Low adapt monitor 0.0284 0.0353
#> High vs Low High Low adapt plan 0.0142 0.0163
#> High vs Low High Low adapt synthesis 0 0
#> High vs Low High Low cohesion adapt 0.0053 0
#> High vs Low High Low cohesion cohesion 0.0437 0.0066
#> High vs Low High Low cohesion consensus 0.5362 0.4505
#> High vs Low High Low cohesion coregulate 0.0810 0.1664
#> High vs Low High Low cohesion discuss 0.0405 0.0832
#> High vs Low High Low cohesion emotion 0.1183 0.1123
#> High vs Low High Low cohesion monitor 0.0171 0.0528
#> High vs Low High Low cohesion plan 0.1514 0.1281
#> High vs Low High Low cohesion synthesis 0.0064 0
#> High vs Low High Low consensus adapt 0.0041 0.0054
#> High vs Low High Low consensus cohesion 0.0198 0.0092
#> High vs Low High Low consensus consensus 0.0834 0.0804
#> High vs Low High Low consensus coregulate 0.1710 0.2070
#> High vs Low High Low consensus discuss 0.2326 0.1365
#> High vs Low High Low consensus emotion 0.0814 0.0626
#> High vs Low High Low consensus monitor 0.0354 0.0596
#> High vs Low High Low consensus plan 0.3644 0.4321
#> High vs Low High Low consensus synthesis 0.0080 0.0072
#> High vs Low High Low coregulate adapt 0.0224 0.0112
#> High vs Low High Low coregulate cohesion 0.0358 0.0362
#> High vs Low High Low coregulate consensus 0.1085 0.1561
#> High vs Low High Low coregulate coregulate 0.0134 0.0316
#> High vs Low High Low coregulate discuss 0.2349 0.3058
#> High vs Low High Low coregulate emotion 0.2036 0.1459
#> High vs Low High Low coregulate monitor 0.0962 0.0781
#> High vs Low High Low coregulate plan 0.2662 0.2165
#> High vs Low High Low coregulate synthesis 0.0190 0.0186
#> High vs Low High Low discuss adapt 0.0240 0.1201
#> High vs Low High Low discuss cohesion 0.0619 0.0329
#> High vs Low High Low discuss consensus 0.4249 0.2146
#> High vs Low High Low discuss coregulate 0.0724 0.0965
#> High vs Low High Low discuss discuss 0.1692 0.2213
#> High vs Low High Low discuss emotion 0.1123 0.0991
#> High vs Low High Low discuss monitor 0.0165 0.0282
#> High vs Low High Low discuss plan 0.0125 0.0108
#> High vs Low High Low discuss synthesis 0.1063 0.1766
#> High vs Low High Low emotion adapt 0.0032 0.0016
#> High vs Low High Low emotion cohesion 0.3258 0.3248
#> High vs Low High Low emotion consensus 0.3361 0.3015
#> High vs Low High Low emotion coregulate 0.0232 0.0474
#> High vs Low High Low emotion discuss 0.1219 0.0777
#> High vs Low High Low emotion emotion 0.0626 0.0940
#> High vs Low High Low emotion monitor 0.0316 0.0420
#> High vs Low High Low emotion plan 0.0903 0.1111
#> High vs Low High Low emotion synthesis 0.0052 0
#> High vs Low High Low monitor adapt 0.0111 0.0112
#> High vs Low High Low monitor cohesion 0.0490 0.0612
#> High vs Low High Low monitor consensus 0.1596 0.1588
#> High vs Low High Low monitor coregulate 0.0506 0.0638
#> High vs Low High Low monitor discuss 0.3697 0.3800
#> High vs Low High Low monitor emotion 0.0964 0.0862
#> High vs Low High Low monitor monitor 0.0190 0.0175
#> High vs Low High Low monitor plan 0.2259 0.2075
#> High vs Low High Low monitor synthesis 0.0190 0.0138
#> High vs Low High Low plan adapt 0.0014 0.0006
#> High vs Low High Low plan cohesion 0.0315 0.0196
#> High vs Low High Low plan consensus 0.2939 0.2873
#> High vs Low High Low plan coregulate 0.0239 0.0113
#> High vs Low High Low plan discuss 0.0602 0.0747
#> High vs Low High Low plan emotion 0.1822 0.1155
#> High vs Low High Low plan monitor 0.0757 0.0753
#> High vs Low High Low plan plan 0.3278 0.4153
#> High vs Low High Low plan synthesis 0.0035 0.0003
#> High vs Low High Low synthesis adapt 0.1439 0.3021
#> High vs Low High Low synthesis cohesion 0.0288 0.0374
#> High vs Low High Low synthesis consensus 0.5755 0.3850
#> High vs Low High Low synthesis coregulate 0.0144 0.0668
#> High vs Low High Low synthesis discuss 0.0288 0.0882
#> High vs Low High Low synthesis emotion 0.0647 0.0749
#> High vs Low High Low synthesis monitor 0 0.0214
#> High vs Low High Low synthesis plan 0.1439 0.0241
#> High vs Low High Low synthesis synthesis 0 0
#> diff abs_diff rel_diff ratio log_ratio rank_a rank_b rank_diff
#> 0 0 NA NA 0 3 3.5000 -0.5000
#> -0.0148 0.0148 0.0274 0.9467 -0.0116 70 70 0
#> 0.0558 0.0558 0.0569 1.1207 0.0374 79 81 -2
#> -0.0299 0.0299 1 0 -0.0295 3 26 -23
#> -0.0325 0.0325 0.3141 0.5220 -0.0309 35 40 -5
#> 0.0304 0.0304 0.1202 1.2731 0.0270 58 54 4
#> -0.0070 0.0070 0.1092 0.8031 -0.0067 29 29 0
#> -0.0021 0.0021 0.0695 0.8700 -0.0021 17 19 -2
#> 0 0 NA NA 0 3 3.5000 -0.5000
#> 0.0053 0.0053 1 NA 0.0053 11 3.5000 7.5000
#> 0.0371 0.0371 0.7375 6.6177 0.0362 38 11 27
#> 0.0858 0.0858 0.0869 1.1904 0.0575 80 80 0
#> -0.0854 0.0854 0.3452 0.4868 -0.0761 47 63 -16
#> -0.0427 0.0427 0.3452 0.4868 -0.0402 37 47 -10
#> 0.0061 0.0061 0.0262 1.0539 0.0054 56 55 1
#> -0.0358 0.0358 0.5119 0.3228 -0.0346 20 34 -14
#> 0.0232 0.0232 0.0832 1.1814 0.0204 61 58 3
#> 0.0064 0.0064 1 NA 0.0064 12 3.5000 8.5000
#> -0.0013 0.0013 0.1379 0.7576 -0.0013 9 10 -1
#> 0.0106 0.0106 0.3648 2.1486 0.0104 24 13 11
#> 0.0031 0.0031 0.0188 1.0383 0.0028 49 46 3
#> -0.0360 0.0360 0.0953 0.8260 -0.0303 64 65 -1
#> 0.0961 0.0961 0.2603 1.7037 0.0811 68 59 9
#> 0.0187 0.0187 0.1300 1.2988 0.0175 48 37 11
#> -0.0242 0.0242 0.2549 0.5937 -0.0231 34 35 -1
#> -0.0677 0.0677 0.0850 0.8433 -0.0484 76 79 -3
#> 0.0008 0.0008 0.0536 1.1132 0.0008 13 12 1
#> 0.0112 0.0112 0.3347 2.0060 0.0110 25 15 10
#> -0.0005 0.0005 0.0063 0.9876 -0.0004 36 30 6
#> -0.0476 0.0476 0.1800 0.6949 -0.0421 54 61 -7
#> -0.0182 0.0182 0.4037 0.4248 -0.0178 16 27 -11
#> -0.0709 0.0709 0.1311 0.7682 -0.0558 69 74 -5
#> 0.0577 0.0577 0.1650 1.3952 0.0491 66 60 6
#> 0.0181 0.0181 0.1040 1.2322 0.0167 51 45 6
#> 0.0497 0.0497 0.1029 1.2294 0.0400 71 68 3
#> 0.0004 0.0004 0.0114 1.0230 0.0004 23 21 2
#> -0.0962 0.0962 0.6674 0.1995 -0.0898 28 57 -29
#> 0.0291 0.0291 0.3066 1.8843 0.0277 42 28 14
#> 0.2103 0.2103 0.3289 1.9800 0.1597 78 67 11
#> -0.0241 0.0241 0.1428 0.7501 -0.0222 45 51 -6
#> -0.0520 0.0520 0.1332 0.7649 -0.0435 63 69 -6
#> 0.0133 0.0133 0.0627 1.1338 0.0120 55 52 3
#> -0.0118 0.0118 0.2630 0.5835 -0.0115 19 25 -6
#> 0.0017 0.0017 0.0731 1.1578 0.0017 15 14 1
#> -0.0703 0.0703 0.2483 0.6022 -0.0616 53 64 -11
#> 0.0017 0.0017 0.3498 2.0758 0.0017 7 9 -2
#> 0.0010 0.0010 0.0016 1.0031 0.0008 73 75 -2
#> 0.0347 0.0347 0.0543 1.1149 0.0263 75 72 3
#> -0.0242 0.0242 0.3423 0.4900 -0.0233 26 33 -7
#> 0.0442 0.0442 0.2216 1.5693 0.0402 57 44 13
#> -0.0314 0.0314 0.2007 0.6656 -0.0292 43 50 -7
#> -0.0103 0.0103 0.1406 0.7534 -0.0100 33 32 1
#> -0.0208 0.0208 0.1032 0.8129 -0.0189 50 53 -3
#> 0.0052 0.0052 1 NA 0.0051 10 3.5000 6.5000
#> -0.0002 0.0002 0.0086 0.9830 -0.0002 14 16 -2
#> -0.0123 0.0123 0.1114 0.7996 -0.0116 39 36 3
#> 0.0008 0.0008 0.0025 1.0051 0.0007 62 62 0
#> -0.0132 0.0132 0.1155 0.7930 -0.0125 40 38 2
#> -0.0103 0.0103 0.0138 0.9728 -0.0075 77 76 1
#> 0.0101 0.0101 0.0554 1.1173 0.0093 52 48 4
#> 0.0015 0.0015 0.0400 1.0833 0.0014 21.5000 20 1.5000
#> 0.0184 0.0184 0.0425 1.0887 0.0151 67 66 1
#> 0.0052 0.0052 0.1592 1.3787 0.0051 21.5000 18 3.5000
#> 0.0008 0.0008 0.3861 2.2580 0.0008 6 8 -2
#> 0.0119 0.0119 0.2323 1.6053 0.0116 32 22 10
#> 0.0066 0.0066 0.0114 1.0231 0.0051 72 71 1
#> 0.0125 0.0125 0.3560 2.1054 0.0123 27 17 10
#> -0.0146 0.0146 0.1080 0.8051 -0.0136 41 41 0
#> 0.0668 0.0668 0.2243 1.5782 0.0581 65 56 9
#> 0.0004 0.0004 0.0025 1.0051 0.0004 46 43 3
#> -0.0875 0.0875 0.1178 0.7893 -0.0638 74 78 -4
#> 0.0032 0.0032 0.8373 11.2898 0.0031 8 7 1
#> -0.1583 0.1583 0.3548 0.4762 -0.1296 59.5000 73 -13.5000
#> -0.0087 0.0087 0.1307 0.7688 -0.0084 30.5000 31 -0.5000
#> 0.1905 0.1905 0.1983 1.4948 0.1289 81 77 4
#> -0.0525 0.0525 0.6457 0.2153 -0.0504 18 39 -21
#> -0.0595 0.0595 0.5081 0.3261 -0.0562 30.5000 49 -18.5000
#> -0.0101 0.0101 0.0725 0.8649 -0.0095 44 42 2
#> -0.0214 0.0214 1 0 -0.0212 3 23 -20
#> 0.1198 0.1198 0.7134 5.9792 0.1107 59.5000 24 35.5000
#> 0 0 NA NA 0 3 3.5000 -0.5000
#> percentile_diff status higher
#> -0.0123 neither equal
#> 0 both Low
#> -0.0247 both High
#> -0.2593 only_b Low
#> -0.0617 both Low
#> 0.0494 both High
#> 0 both Low
#> -0.0247 both Low
#> -0.0123 neither equal
#> 0.0617 only_a High
#> 0.3333 both High
#> 0 both High
#> -0.1975 both Low
#> -0.1235 both Low
#> 0.0123 both High
#> -0.1728 both Low
#> 0.0370 both High
#> 0.0741 only_a High
#> -0.0123 both Low
#> 0.1358 both High
#> 0.0370 both High
#> -0.0123 both Low
#> 0.1111 both High
#> 0.1358 both High
#> -0.0123 both Low
#> -0.0370 both Low
#> 0.0123 both High
#> 0.1235 both High
#> 0.0741 both Low
#> -0.0864 both Low
#> -0.1358 both Low
#> -0.0617 both Low
#> 0.0741 both High
#> 0.0741 both High
#> 0.0370 both High
#> 0.0247 both High
#> -0.3580 both Low
#> 0.1728 both High
#> 0.1358 both High
#> -0.0741 both Low
#> -0.0741 both Low
#> 0.0370 both High
#> -0.0741 both Low
#> 0.0123 both High
#> -0.1358 both Low
#> -0.0247 both High
#> -0.0247 both High
#> 0.0370 both High
#> -0.0864 both Low
#> 0.1605 both High
#> -0.0864 both Low
#> 0.0123 both Low
#> -0.0370 both Low
#> 0.0494 only_a High
#> -0.0247 both Low
#> 0.0370 both Low
#> 0 both High
#> 0.0247 both Low
#> 0.0123 both Low
#> 0.0494 both High
#> 0.0247 both High
#> 0.0123 both High
#> 0.0494 both High
#> -0.0247 both High
#> 0.1235 both High
#> 0.0123 both High
#> 0.1235 both High
#> 0 both Low
#> 0.1111 both High
#> 0.0370 both High
#> -0.0494 both Low
#> 0.0123 both High
#> -0.1605 both Low
#> 0 both Low
#> 0.0494 both High
#> -0.2593 both Low
#> -0.2222 both Low
#> 0.0247 both Low
#> -0.2222 only_b Low
#> 0.4444 both High
#> -0.0123 neither equal
node_differences(cmp, measure = "InStrength")
#> pair network_a network_b node measure value_a value_b diff
#> High vs Low High Low adapt InStrength 0.22 0.45 -0.24
#> High vs Low High Low cohesion InStrength 0.81 0.80 0.02
#> High vs Low High Low consensus InStrength 2.95 2.42 0.54
#> High vs Low High Low coregulate InStrength 0.44 0.69 -0.25
#> High vs Low High Low discuss InStrength 1.12 1.21 -0.09
#> High vs Low High Low emotion InStrength 1 0.81 0.19
#> High vs Low High Low monitor InStrength 0.30 0.39 -0.09
#> High vs Low High Low plan InStrength 1.27 1.15 0.12
#> High vs Low High Low synthesis InStrength 0.17 0.22 -0.05
#> abs_diff rank_a rank_b higher
#> 0.24 2 3 Low
#> 0.02 5 5 High
#> 0.54 9 9 High
#> 0.25 4 4 Low
#> 0.09 7 8 Low
#> 0.19 6 6 High
#> 0.09 3 2 Low
#> 0.12 8 7 High
#> 0.05 1 1 Low
global_differences(cmp)
#> pair network_a network_b category metric
#> High vs Low High Low Weight Deviations Mean Abs. Diff.
#> High vs Low High Low Weight Deviations Median Abs. Diff.
#> High vs Low High Low Weight Deviations RMS Diff.
#> High vs Low High Low Weight Deviations Max Abs. Diff.
#> High vs Low High Low Weight Deviations Rel. Mean Abs. Diff.
#> High vs Low High Low Weight Deviations CV Ratio
#> High vs Low High Low Correlations Pearson
#> High vs Low High Low Correlations Spearman
#> High vs Low High Low Correlations Kendall
#> High vs Low High Low Correlations Distance
#> High vs Low High Low Dissimilarities Euclidean
#> High vs Low High Low Dissimilarities Manhattan
#> High vs Low High Low Dissimilarities Canberra
#> High vs Low High Low Dissimilarities Bray-Curtis
#> High vs Low High Low Dissimilarities Frobenius
#> High vs Low High Low Similarities Cosine
#> High vs Low High Low Similarities Jaccard
#> High vs Low High Low Similarities Dice
#> High vs Low High Low Similarities Overlap
#> High vs Low High Low Similarities RV
#> High vs Low High Low Pattern Similarities Rank Agreement
#> High vs Low High Low Pattern Similarities Sign Agreement
#> key value
#> mean_abs_diff 0.03
#> median_abs_diff 0.02
#> rms_diff 0.05
#> max_abs_diff 0.21
#> rel_mean_abs 0.29
#> cv_ratio 1.10
#> pearson 0.92
#> spearman 0.92
#> kendall 0.77
#> distance_cor 0.84
#> euclidean 0.47
#> manhattan 2.61
#> canberra 14.76
#> bray_curtis 0.15
#> frobenius 0.22
#> cosine 0.95
#> jaccard 0.75
#> dice 0.85
#> overlap 0.85
#> rv 0.90
#> rank_agreement 0.82
#> sign_agreement 0.94
network_metrics(cmp)
#> network metric value
#> High Node Count 9
#> High Edge Count 76
#> High Network Density 1
#> High Mean Distance 0.04
#> High Mean Out-Strength 1
#> High SD Out-Strength 0.91
#> High Mean In-Strength 1
#> High SD In-Strength 0
#> High Mean Out-Degree 8.44
#> High SD Out-Degree 1.13
#> High Centralization (Out-Degree) 0.05
#> High Centralization (In-Degree) 0.05
#> High Reciprocity 0.96
#> Low Node Count 9
#> Low Edge Count 75
#> Low Network Density 1
#> Low Mean Distance 0.06
#> Low Mean Out-Strength 1
#> Low SD Out-Strength 0.72
#> Low Mean In-Strength 1
#> Low SD In-Strength 0
#> Low Mean Out-Degree 8.33
#> Low SD Out-Degree 0.87
#> Low Centralization (Out-Degree) 0.06
#> Low Centralization (In-Degree) 0.06
#> Low Reciprocity 0.94