Summaries of results

summary() on a result returns every table of the result as a list of data frames, named after the what values of hg_get(), so summary(x)$validation is the same data frame as hg_get(x, what = "validation"). Results with overall figures add them as further one-row or per-group tables. Tables that a particular result does not hold are left out. The result itself is not changed.

Usage

# S3 method for class 'hypernets_summary'
hg_get(x, what = names(x)[1L], ...)

# S3 method for class 'hypernets_summary'
print(x, n = 5L, ...)

# S3 method for class 'hypernets_result'
summary(object, ...)

# S3 method for class 'hypernets_hypa'
summary(object, ...)

# S3 method for class 'hypernets_community_comparison'
summary(object, ...)

# S3 method for class 'hg_communities'
summary(object, ...)

# S3 method for class 'net_hg_mmsbm'
summary(object, ...)

# S3 method for class 'hypernets_motifs'
summary(object, ...)

# S3 method for class 'net_hon_communities'
summary(object, ...)

# S3 method for class 'net_hon_boot_group'
summary(object, ...)

# S3 method for class 'net_hon_group'
summary(object, ...)

# S3 method for class 'net_hon_boot'
summary(object, ...)

# S3 method for class 'net_hon_compare'
summary(object, ...)

# S3 method for class 'hypernets_simplicial'
summary(object, ...)

# S3 method for class 'net_hg_topics'
summary(object, ...)

Arguments

x

A hypernets_summary.

what

For hg_get() on a summary: the name of one of its tables. Default: the first.

...

Unused.

n

For print(): number of rows of each table to show. Default 5.

object

A result: from hon(), honem(), mogen(), markov_order(), memory(), hg_markov_stability(), hypa() on sequences, hg_bootstrap(), hg_compare(), hg_communities(), hg_compare_communities(), hg_mmsbm(), hg_motifs(), simplicial(), hg_homology(), hg_landscape() or hg_qanalysis().

Value

A hypernets_summary: a named list of data frames. Its print method shows the first rows of each.

Examples

seqs <- list(c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"),
             c("a", "b", "c", "a", "b", "c"), c("x", "b", "d", "x", "b", "d"))
fit_summary <- summary(memory(seqs, order = 2, min_count = 1))
fit_summary
#> Memory of order 2: 6 contexts, 1 change the most likely next state
#> Tables: contexts, overall
#> 
#> contexts (6 rows)
#>  context count entropy_first_order entropy_order_k entropy_drop kl
#>   a -> b     4                   1               0            1  1
#>   x -> b     4                   1               0            1  1
#>   b -> c     2                   0               0            0  0
#>   b -> d     2                   0               0            0  0
#>   c -> a     2                   0               0            0  0
#>  top_first_order top_order_k flips
#>                c           c FALSE
#>                c           d  TRUE
#>                a           a FALSE
#>                x           x FALSE
#>                b           b FALSE
#> 
#> overall (1 row)
#>  n_contexts n_flips kl_weighted entropy_drop_weighted
#>           6       1         0.5                   0.5
fit_summary$contexts
#>   context count entropy_first_order entropy_order_k entropy_drop kl
#> 1  a -> b     4                   1               0            1  1
#> 2  x -> b     4                   1               0            1  1
#> 3  b -> c     2                   0               0            0  0
#> 4  b -> d     2                   0               0            0  0
#> 5  c -> a     2                   0               0            0  0
#> 6  d -> x     2                   0               0            0  0
#>   top_first_order top_order_k flips
#> 1               c           c FALSE
#> 2               c           d  TRUE
#> 3               a           a FALSE
#> 4               x           x FALSE
#> 5               b           b FALSE
#> 6               b           b FALSE