Tidiers for cluster permutation test objects

Usage

# S3 method for timewise_statistics
tidy(x, ...)

# S3 method for empirical_clusters
tidy(x, ...)

# S3 method for null_cluster_dists
tidy(x, ...)

Arguments

x: An object of class <timewise_statistics>, <empirical_clusters>, or <null_cluster_dists>
...: Unused

Value

A data frame

Examples

# \donttest{
# \dontshow{
options("jlmerclusterperm.nthreads" = 2)
jlmerclusterperm_setup(cache_dir = tempdir(), verbose = FALSE)
julia_progress(show = FALSE)
# }

library(dplyr, warn.conflicts = FALSE)

# Specification object
spec <- make_jlmer_spec(
  weight ~ 1 + Diet, filter(ChickWeight, Time <= 20),
  subject = "Chick", time = "Time"
)
spec
#> ── jlmer specification ───────────────────────────────────────── <jlmer_spec> ──
#> Formula: weight ~ 1 + Diet2 + Diet3 + Diet4
#> Predictors:
#>   Diet: Diet2, Diet3, Diet4
#> Groupings:
#>   Subject: Chick
#>   Trial:
#>   Time: Time
#> Data:
#> # A tibble: 533 × 6
#>   weight Diet2 Diet3 Diet4 Chick  Time
#>    <dbl> <dbl> <dbl> <dbl> <ord> <dbl>
#> 1     42     0     0     0 1         0
#> 2     51     0     0     0 1         2
#> 3     59     0     0     0 1         4
#> # ℹ 530 more rows
#> ────────────────────────────────────────────────────────────────────────────────

# Method for `<timewise_statistics>`
empirical_statistics <- compute_timewise_statistics(spec)
class(empirical_statistics)
#> [1] "timewise_statistics"
tidy(empirical_statistics)
#> # A tibble: 33 × 3
#>    predictor  time statistic
#>    <chr>     <dbl>     <dbl>
#>  1 Diet2         0    -1.60 
#>  2 Diet3         0    -1.37 
#>  3 Diet4         0    -0.916
#>  4 Diet2         2     1.67 
#>  5 Diet3         2     2.45 
#>  6 Diet4         2     3.53 
#>  7 Diet2         4     2.60 
#>  8 Diet3         4     4.48 
#>  9 Diet4         4     6.27 
#> 10 Diet2         6     3.52 
#> # ℹ 23 more rows

reset_rng_state()
null_statistics <- permute_timewise_statistics(spec, nsim = 100)
class(null_statistics)
#> [1] "timewise_statistics"
tidy(null_statistics)
#> # A tibble: 3,300 × 4
#>    predictor  time statistic sim  
#>    <chr>     <dbl>     <dbl> <fct>
#>  1 Diet2         0    -0.898 001  
#>  2 Diet2         0     1.40  002  
#>  3 Diet2         0    -1.44  003  
#>  4 Diet2         0    -0.449 004  
#>  5 Diet2         0     1.38  005  
#>  6 Diet2         0    -0.692 006  
#>  7 Diet2         0     0.113 007  
#>  8 Diet2         0     0.334 008  
#>  9 Diet2         0     0.558 009  
#> 10 Diet2         0    -0.449 010  
#> # ℹ 3,290 more rows

# Method for `<empirical_clusters>`
empirical_clusters <- extract_empirical_clusters(empirical_statistics, threshold = 2)
class(empirical_clusters)
#> [1] "empirical_clusters"
tidy(empirical_clusters)
#> # A tibble: 3 × 6
#>   predictor id    start   end length sum_statistic
#>   <chr>     <fct> <dbl> <dbl>  <dbl>         <dbl>
#> 1 Diet2     1         4     8      3          8.50
#> 2 Diet3     1         2    20     10         34.6 
#> 3 Diet4     1         2    20     10         39.4 

# Method for `<null_cluster_dists>`
null_cluster_dists <- extract_null_cluster_dists(null_statistics, threshold = 2)
class(null_cluster_dists)
#> [1] "null_cluster_dists"
tidy(null_cluster_dists)
#> # A tibble: 300 × 6
#>    predictor start   end length sum_statistic sim  
#>    <chr>     <dbl> <dbl>  <dbl>         <dbl> <fct>
#>  1 Diet2        10    20      6          14.8 001  
#>  2 Diet2        NA    NA     NA           0   002  
#>  3 Diet2        NA    NA     NA           0   003  
#>  4 Diet2        NA    NA     NA           0   004  
#>  5 Diet2        NA    NA     NA           0   005  
#>  6 Diet2        NA    NA     NA           0   006  
#>  7 Diet2        NA    NA     NA           0   007  
#>  8 Diet2        NA    NA     NA           0   008  
#>  9 Diet2        NA    NA     NA           0   009  
#> 10 Diet2        NA    NA     NA           0   010  
#> # ℹ 290 more rows

# \dontshow{
JuliaConnectoR::stopJulia()
# }
# }