Descriptive (Change) statistics — print

This function perform two tasks: (i) it generate the descriptive and change statistics of groups, particularly suited for the outputs form the akclustr function, and (ii) generates the plots of the groups (performances).

print_akstats(ak_object, k = 3, reference = 1, n_quant = 4, show_plots = FALSE)

Arguments

ak_object	An output of `akclustr` function. The object contains individual trajectories and their cluster solution(s) at the specified values of `k`. Also, includes the optimal value of `k` based on the criterion specified. at (different) values of `k` the `traj`.
k	[integer] `k` cluster to generate its solution.
reference	[numeric] Specifying the reference line from which the direction of each group is measured. Options are: `1`: slope of mean trajectory, `2`: slope of medoid trajectory, `3`: slope of a horizontal line (i.e. slope = 0). Default: `1`.
n_quant	[numeric] Number of equal intervals (quantiles) to create between the reference line `(R)` and the medoids `(M)` of the most-diverging groups of both sides of `(R)`. Default is `4` - meaning quartile subdivisions on each side of `(R)`. In this scenario, the function returns the quartile in which the medoid of each group falls. This result can be used to further categorize the groups into 'classes'. For example, groups that fall within the `1st` quartile may be classified as 'Stable' groups (Adepeju et al. 2021).
show_plots	[TRUE or FALSE] Provides the trajectory group plot. Please, see `plot_akstats` function for more plot options. Defaults `FALSE`

Value

A plot showing group membership or sizes (proportion) and statistics.

Details

Generates the plot of trajectory groupings. Given an ak_object class (from the akclustr function), this function show the plots of cluster groups. The plot component draws from plot_akstats function.

References

1. Adepeju, M. et al. (2021). Anchored k-medoids: A novel adaptation of k-medoids further refined to measure inequality in the exposure to crime across micro places, doi: 10.1007/s42001-021-00103-1.

2. Wickham H. (2016). Elegant graphics for Data Analysis. Spring-Verlag New York (2016).

Examples


data(traj)

trajectry <- data_imputation(traj, id_field = TRUE, method = 1,
replace_with = 1, fill_zeros = FALSE)
#> [1] "8 entries were found/filled!"

print(trajectry$CompleteData)
#>    location_ids X2001  X2002     X2003 X2004 X2005     X2006  X2007 X2008 X2009
#> 1     E01012628     3  0.000  1.000000     2     1  0.000000  1.000     4     0
#> 2     E01004768     9  5.125  2.000000     4     7  5.000000  1.000     3     1
#> 3     E01004803     4  3.000  0.000000    10     2  3.000000  6.000     6     8
#> 4     E01004804     7  3.000  9.000000     3     2  4.857143  6.000     3     2
#> 5     E01004807     2  5.125  5.000000     5     6  4.857143  3.000     5     4
#> 6     E01004808     8  5.000  8.000000     4     1  5.000000  6.000     1     1
#> 7     E01004788     2  4.000  7.333333     2     2  4.000000  1.000     3     0
#> 8     E01004790    10  9.000 17.000000    13    15  4.857143  5.625    19     9
#> 9     E01004805     8  5.000 10.000000     7     9  4.000000  5.625     6     3
#> 10    E01004806    25 12.000 14.000000    15    18 13.000000 21.000    27    17

trajectry <- props(trajectry$CompleteData, id_field = TRUE)

aksolution <- akclustr(trajectry, id_field = TRUE,
    method = "linear", k = c(3,5), crit='Calinski_Harabasz')
#> [1] "Processing...."
#> [1] ".............."
#> [1] "solution of k = 3 determined!"
#> [1] "solution of k = 4 determined!"
#> [1] "solution of k = 5 determined!"

print_akstats(aksolution, k = 4, show_plots=FALSE)
#> Warning: `fun.y` is deprecated. Use `fun` instead.
#> $descriptive_stats
#>   group n n(%) %Prop.time1 %Prop.timeT Change %Change
#> 1     A 2   20        21.8         4.4  -17.4  -395.5
#> 2     B 3   30        21.8        11.1  -10.7   -96.4
#> 3     C 3   30        19.2        28.9    9.7    33.6
#> 4     D 2   20        37.2        55.6   18.4    33.1
#> 
#> $change_stats
#>   group sn %+ve Traj. %-ve Traj. Qtl:1st-4th
#> 1     A  1          0        100   4th (-ve)
#> 2     B  2          0        100   3rd (-ve)
#> 3     C  3       66.7       33.3   1st (+ve)
#> 4     D  4        100          0   4th (+ve)
#>