summary() gives the tests, as.data.frame() the edges.
Usage
# S3 method for class 'psychnet_nct'
summary(object, alpha = 0.05, ...)
# S3 method for class 'psychnet_nct'
as.data.frame(x, row.names = NULL, optional = FALSE, alpha = 0.05, ...)Arguments
- alpha
Significance level for the
significantcolumn. Default 0.05.- ...
Unused.
- x, object
A
psychnet_nctobject fromnet_compare().- row.names, optional
Ignored (base generic compatibility).
Value
summary(): a data frame with one row per global test, columns
test ("network structure"; "global strength", or "global expected influence" when abs = FALSE), statistic ("M", "S"), observed,
p_value and significant (p_value < alpha).
as.data.frame(): a data frame with one row per node pair, columns
from, to, weight_1, weight_2 (the edge in the first and second
group's network; 0/1 when weighted = FALSE), difference (absolute
difference, statistic E), p_value (permutation p, adjusted by
p_adjust) and significant.
Examples
set.seed(1)
a <- matrix(stats::rnorm(100 * 4), 100, 4)
b <- matrix(stats::rnorm(100 * 4), 100, 4)
colnames(a) <- colnames(b) <- paste0("V", 1:4)
fit <- net_compare(a, b, iter = 10)
summary(fit)
#> test statistic observed p_value significant
#> 1 network structure M 0 1 FALSE
#> 2 global strength S 0 1 FALSE
as.data.frame(fit)
#> from to weight_1 weight_2 difference p_value significant
#> 1 V1 V2 0 0 0 1 FALSE
#> 2 V1 V3 0 0 0 1 FALSE
#> 3 V2 V3 0 0 0 1 FALSE
#> 4 V1 V4 0 0 0 1 FALSE
#> 5 V2 V4 0 0 0 1 FALSE
#> 6 V3 V4 0 0 0 1 FALSE