Summary of tt_test() results.

Summary of tt_test() results.

# S3 method for tt_df
summary(object, ...)

# S3 method for tt_lst
summary(object, ...)

Arguments

object	An object of class "tt_df" or "tt_lst".
...	Not used (included only for compatibility with summary).

Value

A tibble.

See also

tt_test(), base::summary().

Author

Adapted from code contributed by Daniel Zuleta.

Examples

assert_is_installed("fgeo.x")

tt_result <- tt_test(fgeo.x::tree6_3species, fgeo.x::habitat)
#> Using `plotdim = c(320, 500)`. To change this value see `?tt_test()`.
#> Using `gridsize = 20`. To change this value see `?tt_test()`.

summary(tt_result)
#> # A tibble: 12 x 3
#>    sp     habitat association
#>    <chr>  <chr>   <chr>      
#>  1 CASARB 1       neutral    
#>  2 CASARB 2       neutral    
#>  3 CASARB 3       neutral    
#>  4 CASARB 4       neutral    
#>  5 PREMON 1       neutral    
#>  6 PREMON 2       neutral    
#>  7 PREMON 3       neutral    
#>  8 PREMON 4       neutral    
#>  9 SLOBER 1       neutral    
#> 10 SLOBER 2       neutral    
#> 11 SLOBER 3       neutral    
#> 12 SLOBER 4       neutral    

# Same
summary(as_tibble(tt_result))
#> # A tibble: 12 x 3
#>    sp     habitat association
#>    <chr>  <chr>   <chr>      
#>  1 CASARB 1       neutral    
#>  2 CASARB 2       neutral    
#>  3 CASARB 3       neutral    
#>  4 CASARB 4       neutral    
#>  5 PREMON 1       neutral    
#>  6 PREMON 2       neutral    
#>  7 PREMON 3       neutral    
#>  8 PREMON 4       neutral    
#>  9 SLOBER 1       neutral    
#> 10 SLOBER 2       neutral    
#> 11 SLOBER 3       neutral    
#> 12 SLOBER 4       neutral    

# You may want to add the explanation to the result of `tt_test()`

dplyr::left_join(as_tibble(tt_result), summary(tt_result))
#> Joining, by = c("habitat", "sp")
#> # A tibble: 12 x 9
#>    habitat sp    N.Hab Gr.Hab Ls.Hab Eq.Hab Rep.Agg.Neut Obs.Quantile
#>    <chr>   <chr> <dbl>  <dbl>  <dbl>  <dbl>        <dbl>        <dbl>
#>  1 1       CASA…    29   1242    356      2            0        0.776
#>  2 2       CASA…    20    390   1206      4            0        0.244
#>  3 3       CASA…    12    778    817      5            0        0.486
#>  4 4       CASA…     5    932    658     10            0        0.582
#>  5 1       PREM…    91   1093    504      3            0        0.683
#>  6 2       PREM…    89   1254    344      2            0        0.784
#>  7 3       PREM…    40    305   1292      3            0        0.191
#>  8 4       PREM…    14    270   1322      8            0        0.169
#>  9 1       SLOB…    18    273   1324      3            0        0.171
#> 10 2       SLOB…    24    810    788      2            0        0.506
#> 11 3       SLOB…    17   1155    440      5            0        0.722
#> 12 4       SLOB…     7   1292    303      5            0        0.808
#> # … with 1 more variable: association <chr>

# You may prefer a wide matrix
Reduce(rbind, tt_result)
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1 N.Hab.2
#> CASARB      29     1242      356        2              0       0.776250      20
#> PREMON      91     1093      504        3              0       0.683125      89
#> SLOBER      18      273     1324        3              0       0.170625      24
#>        Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2 N.Hab.3
#> CASARB      390     1206        4              0        0.24375      12
#> PREMON     1254      344        2              0        0.78375      40
#> SLOBER      810      788        2              0        0.50625      17
#>        Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3 N.Hab.4
#> CASARB      778      817        5              0       0.486250       5
#> PREMON      305     1292        3              0       0.190625      14
#> SLOBER     1155      440        5              0       0.721875       7
#>        Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> CASARB      932      658       10              0        0.58250
#> PREMON      270     1322        8              0        0.16875
#> SLOBER     1292      303        5              0        0.80750

# You may prefer a wide dataframe
tidyr::spread(summary(tt_result), "habitat", "association")
#> # A tibble: 3 x 5
#>   sp     `1`     `2`     `3`     `4`    
#>   <chr>  <chr>   <chr>   <chr>   <chr>  
#> 1 CASARB neutral neutral neutral neutral
#> 2 PREMON neutral neutral neutral neutral
#> 3 SLOBER neutral neutral neutral neutral