Summary Method for wtna_boot_mixed
Usage
# S3 method for class 'wtna_boot_mixed'
summary(object, ...)Examples
oh <- data.frame(A = c(1,0,1,0), B = c(0,1,0,1), C = c(1,1,0,0))
mixed <- wtna(oh, method = "both")
boot <- bootstrap_network(mixed, iter = 10)
summary(boot)
#> $transition
#> from to weight mean sd p_value sig ci_lower ci_upper cr_lower
#> 1 A B 2 1.3 1.1595018 0.5454545 FALSE 0 2.775 1.50
#> 2 B B 1 0.8 0.9189366 0.5454545 FALSE 0 2.550 0.75
#> 3 C B 2 1.2 0.9189366 0.5454545 FALSE 0 2.000 1.50
#> cr_upper
#> 1 2.50
#> 2 1.25
#> 3 2.50
#>
#> $cooccurrence
#> from to weight mean sd p_value sig ci_lower ci_upper cr_lower
#> 1 A A 4 3.8 2.780887 1.0000000 FALSE 1.000 8.875 3.0
#> 2 A B 2 1.6 0.843274 0.2727273 FALSE 0.000 2.000 1.5
#> 3 A C 4 1.6 1.264911 0.9090909 FALSE 0.000 3.775 3.0
#> 4 B B 2 3.0 2.748737 1.0000000 FALSE 0.225 8.100 1.5
#> 5 B C 2 2.1 2.378141 0.9090909 FALSE 0.000 6.000 1.5
#> 6 C C 4 2.7 2.790858 0.8181818 FALSE 0.225 8.100 3.0
#> cr_upper
#> 1 5.0
#> 2 2.5
#> 3 5.0
#> 4 2.5
#> 5 2.5
#> 6 5.0
#>
# \donttest{
set.seed(1)
oh <- data.frame(
A = c(1,0,1,0,1,0,1,0),
B = c(0,1,0,1,0,1,0,1),
C = c(1,1,0,0,1,1,0,0)
)
mixed <- wtna(oh, method = "both")
boot <- bootstrap_network(mixed, iter = 20)
summary(boot)
#> $transition
#> from to weight mean sd p_value sig ci_lower ci_upper cr_lower
#> 1 A A 3 3.65 2.739093 1.0000000 FALSE 0.000 9.050 2.25
#> 2 A B 5 3.75 1.996708 0.6666667 FALSE 0.950 8.050 3.75
#> 3 A C 4 4.35 2.978431 0.9523810 FALSE 0.000 9.525 3.00
#> 4 B A 3 3.85 2.300458 0.9047619 FALSE 0.000 8.050 2.25
#> 5 B B 4 3.75 3.258592 0.9523810 FALSE 0.000 11.050 3.00
#> 6 B C 2 3.30 2.657660 0.9523810 FALSE 0.000 9.200 1.50
#> 7 C A 4 4.20 3.053902 0.8571429 FALSE 0.000 10.525 3.00
#> 8 C B 6 4.10 3.143916 0.8571429 FALSE 0.475 11.050 4.50
#> 9 C C 4 4.20 3.778053 0.9523810 FALSE 0.000 13.575 3.00
#> cr_upper
#> 1 3.75
#> 2 6.25
#> 3 5.00
#> 4 3.75
#> 5 5.00
#> 6 2.50
#> 7 5.00
#> 8 7.50
#> 9 5.00
#>
#> $cooccurrence
#> from to weight mean sd p_value sig ci_lower ci_upper cr_lower
#> 1 A A 6 7.85 4.356181 0.8095238 FALSE 1.475 16.625 4.50
#> 2 A B 5 3.40 1.429022 0.3809524 FALSE 0.475 5.000 3.75
#> 3 A C 6 6.00 3.308681 0.6190476 FALSE 2.000 13.525 4.50
#> 4 B B 6 7.35 4.498830 0.8571429 FALSE 1.475 15.100 4.50
#> 5 B C 6 5.95 3.677456 0.8571429 FALSE 1.475 13.525 4.50
#> 6 C C 8 8.00 4.142209 0.7619048 FALSE 2.475 14.000 6.00
#> cr_upper
#> 1 7.50
#> 2 6.25
#> 3 7.50
#> 4 7.50
#> 5 7.50
#> 6 10.00
#>
# }