Selection of Entries from Clusters/Groups on the basis of Optimized Diversity
Source:R/select.diversity.R
select.diversity.RdSelect entries from cluster/groups in the entire collection which form a subset with the highest trait diversity according to a either pooled or mean diversity index estimate.
Usage
select.diversity(
data,
names,
group,
alloc,
qualitative,
always.selected = NULL,
div.index = c("richness", "shannon", "simpson", "mcintosh"),
shannon.base = exp(1),
div.fun = NULL,
metric = c("mean", "pooled"),
search = c("random", "greedy"),
local.search = c("best.improvement", "first.improvement"),
n.iter = 1000,
max.iter = 30
)Arguments
- data
The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.
- names
Name of column with the accession names as a character string.
- group
Name of column with the accession group/cluster names as a character string.
- alloc
A named numeric vector specifying the number of entries to be selected. Names should correspond to the levels of the "
"group"column, and values indicate the number of elements to be selected from each level.- qualitative
Name of columns with the qualitative traits as a character vector.
- always.selected
Names of accessions to be always included in the core set as a character vector.
- div.index
The diversity index to be used to estimate within cluster/group diversity.
- shannon.base
The logarithm base to be used for estimation of Shannon diversity index. Default is
exp(1).- div.fun
A function to estimate diversity index from a factor vector of qualitative trait data.
- metric
The metric to be computed from the diversity index. Either
"pooled"or"mean".- search
Character string specifying the search strategy used to find the subset with the highest diversity score. Either
"random"(default) or"greedy"(See Details).- local.search
Character string specifying the local search strategy used in the 1-opt improvement phase of the greedy search (
search = "greedy"). Either"best.improvement"(default) or"first.improvement". Ignored whensearch = "random".- n.iter
Integer specifying the number of random candidate subsets generated per group to optimze the diversity for random search (
search = "random").- max.iter
The maximum number of 1-opt passes for greedy search (
search = "greedy").
Details
To identify subsets with highest diversity estimates, the following strategies are available. These strategies are similar to the "Maximization" or M strategy of Schoen and Brown (1993) .
Random search / Monte Carlo Method
For each cluster/group,
multiple candidate subsets are sampled randomly and the subset with the
highest trait diversity according to either pooled or mean diversity index
estimate is retained. The quality of the solution improves with increasing
n.iter but is not guaranteed to find the global optimum
(Anatoly Zhigljavsky and Antanas Zilinskas 2008)
.
Greedy search with 1-opt
This method builds a solution
incrementally by adding the accession that maximises the diversity score at
each step, starting from the always.selected accessions (or a single
randomly drawn accession when there are no accessions specified in
always.selected) present in the particular cluster/group
(Nemhauser et al. 1978; Fisher et al. 1978; Cormen et al. 2022)
.
The 'greedy' solution is then refined by a 1-opt local search controlled by
local.search and max.iter
(Lin 1965)
. Greedy search is deterministic
given a fixed always.selected set; when there are no accessions
specified in always.selected present in the particular cluster/group
results may vary across runs due to the random initialisation.
local.search = "best.improvement" scans all possible single swaps
in each pass and applies the one yielding the greatest improvement before
restarting. his guarantees the steepest ascent at each pass but requires
evaluating all \(k \times (n - k)\) swap pairs per pass, where
\(k\) is the number of swappable accessions and \(n - k\) is the
size of the candidate pool
(Papadimitriou and Steiglitz 1998)
.
local.search = "first.improvement" applies the first swap that
improves the score and immediately restarts the search. This typically
requires fewer score evaluations per pass and converges faster, but may
find a different local optimum than "best.improvement"
(Papadimitriou and Steiglitz 1998)
.
Both strategies terminate when no improving swap exists (local optimum)
or when max.iter passes have been completed.
Entries listed as always.selected are mandatorily included in the
selection. Warnings are issued if requested allocation is smaller than the
number of always-selected entries in a cluster/group and/or when the
cluster/group does not contain enough remaining entries to fulfill the
allocation.
References
Anatoly Zhigljavsky, Antanas Zilinskas (2008).
Stochastic Global Optimization, volume 9 of Springer Optimization and Its Applications.
Springer US, Boston, MA.
ISBN 978-0-387-74022-5.
Cormen TH, Leiserson CE, Rivest RL, Stein C (2022).
Introduction to Algorithms, 4 edition.
MIT Press, Cambridge, MA, USA.
ISBN 978-0-262-04630-5.
Fisher ML, Nemhauser GL, Wolsey LA (1978).
“An analysis of approximations for maximizing submodular set functions-II.”
Mathematical Programming Study, 8, 73–87.
Lin S (1965).
“Computer solutions of the traveling salesman problem.”
Bell System Technical Journal, 44(10), 2245–2269.
Nemhauser GL, Wolsey LA, Fisher ML (1978).
“An analysis of approximations for maximizing submodular set functions-I.”
Mathematical Programming, 14(1), 265–294.
Papadimitriou CH, Steiglitz K (1998).
Combinatorial optimization: Algorithms and complexity.
Dover Publications, Mineola, N.Y.
ISBN 978-0-486-40258-1.
Schoen DJ, Brown AHD (1993).
“Conservation of allelic richness in wild crop relatives is aided by assessment of genetic markers.”
Proceedings of the National Academy of Sciences, 90(22), 10623–10627.
Examples
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Prepare example data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
library(cluster)
library(ggplot2)
data(cassava_EC_gp)
set.seed(123)
cassava_EC_gp <- cassava_EC_gp[sample(1:nrow(cassava_EC_gp), 500), ]
data <- cbind(genotypes = rownames(cassava_EC_gp), cassava_EC_gp)
quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
"ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
"ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
"PSTR")
# Convert qualitative data columns to factor
data[, qual] <- lapply(data[, qual], as.factor)
# Convert quantitative data columns to qualitative scores
quant_to_score5 <- function(x) {
brks <- unique( quantile(x,
probs = seq(0, 1, 0.2),
na.rm = TRUE))
cut(x, breaks = brks,
include.lowest = TRUE,
labels = seq_len(length(brks) - 1))
}
data[, quant] <- lapply(data[, quant], quant_to_score5)
traits <- c(quant, qual)
# Prepare inputs
counts <- c(I = 31, II = 31, III = 18, IV = 35, V = 40, VI = 17)
mand_accns <-
c("TMe-2018", "TMe-801", "TMe-3191", "TMe-1830", "TMe-1790")
# Get distance matrix - Only for visualization
# Convert qualitative data columns to factor
cassava_EC_gp[, qual] <- lapply(cassava_EC_gp[, qual], as.factor)
# Standardise quantitative data column
cassava_EC_gp[, quant] <- lapply(cassava_EC_gp[, quant], function(x) {
scale(x)[, 1]
})
gp_vec <- setNames(as.character(data[, "Cluster"]), data[, "genotypes"])
# Get the Gower's distance matrix
dist_matrix <- daisy(x = cassava_EC_gp[, c(qual, quant)],
metric = "gower")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Custom Diversity functions
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
div_fun_brillouin <- function(x) {
n <- tabulate(x)
n <- n[n > 0]
N <- sum(n)
if (N <= 1) {
return(0)
}
(lgamma(N + 1) - sum(lgamma(n + 1)))/N
}
div_fun_margalef <- function(x) {
tab <- tabulate(x)
tab <- tab[tab > 0]
S <- length(tab)
N <- length(x)
if (N <= 1) {
return(0)
}
(S - 1)/log(N)
}
# \donttest{
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Random search
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Mean richness
randomsel_mean_richness <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "richness",
metric = "mean", search = "random", local.search = NULL,
n.iter = 50)
randomsel_mean_richness
#> [[1]]
#> [1] "TMe-1830" "TMe-3110" "TMe-910" "TMe-2810" "TMe-1425" "TMe-888"
#> [7] "TMe-469" "TMe-3465" "TMe-2453" "TMe-3548" "TMe-3252" "TMe-3111"
#> [13] "TMe-3112" "TMe-3236" "TMe-569" "TMe-3132" "TMe-937" "TMe-2027"
#> [19] "TMe-1117" "TMe-3726" "TMe-306" "TMe-3553" "TMe-3623" "TMe-2066"
#> [25] "TMe-1589" "TMe-3353" "TMe-41" "TMe-2103" "TMe-2785" "TMe-1914"
#> [31] "TMe-3685"
#>
#> [[2]]
#> [1] "TMe-681" "TMe-369" "TMe-3447" "TMe-3239" "TMe-2033" "TMe-3766"
#> [7] "TMe-2997" "TMe-2211" "TMe-3800" "TMe-339" "TMe-3547" "TMe-2000"
#> [13] "TMe-1732" "TMe-1668" "TMe-3200" "TMe-1474" "TMe-2995" "TMe-74"
#> [19] "TMe-3557" "TMe-674" "TMe-796" "TMe-1754" "TMe-2951" "TMe-2757"
#> [25] "TMe-960" "TMe-2412" "TMe-3284" "TMe-2715" "TMe-2258" "TMe-3366"
#> [31] "TMe-196"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-2748" "TMe-1863" "TMe-381" "TMe-2161" "TMe-2356"
#> [7] "TMe-3569" "TMe-70" "TMe-1804" "TMe-3207" "TMe-3715" "TMe-3631"
#> [13] "TMe-2270" "TMe-1787" "TMe-2086" "TMe-1819" "TMe-2374" "TMe-3133"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-428" "TMe-460" "TMe-241" "TMe-280"
#> [7] "TMe-1376" "TMe-368" "TMe-3538" "TMe-1456" "TMe-3214" "TMe-386"
#> [13] "TMe-698" "TMe-353" "TMe-3428" "TMe-2958" "TMe-266" "TMe-2247"
#> [19] "TMe-12" "TMe-3248" "TMe-2924" "TMe-3189" "TMe-3757" "TMe-1700"
#> [25] "TMe-3255" "TMe-2567" "TMe-1988" "TMe-3000" "TMe-2375" "TMe-1155"
#> [31] "TMe-65" "TMe-3537" "TMe-1579" "TMe-1027" "TMe-3558"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1273" "TMe-612" "TMe-1401" "TMe-256" "TMe-863"
#> [7] "TMe-823" "TMe-323" "TMe-688" "TMe-712" "TMe-629" "TMe-487"
#> [13] "TMe-585" "TMe-2853" "TMe-1788" "TMe-1453" "TMe-1131" "TMe-360"
#> [19] "TMe-877" "TMe-2590" "TMe-98" "TMe-344" "TMe-2907" "TMe-574"
#> [25] "TMe-645" "TMe-1188" "TMe-1003" "TMe-723" "TMe-423" "TMe-1388"
#> [31] "TMe-1680" "TMe-362" "TMe-892" "TMe-2124" "TMe-1004" "TMe-1160"
#> [37] "TMe-406" "TMe-167" "TMe-2213" "TMe-439"
#>
#> [[6]]
#> [1] "TMe-1076" "TMe-310" "TMe-1566" "TMe-1124" "TMe-222" "TMe-693"
#> [7] "TMe-631" "TMe-620" "TMe-1900" "TMe-751" "TMe-809" "TMe-1661"
#> [13] "TMe-2818" "TMe-1302" "TMe-2791" "TMe-531" "TMe-1816"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_mean_richness,
use.names = FALSE)) +
labs(title = "Random search", subtitle = "Mean richness")
# Pooled richness
randomsel_sum_richness <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "richness",
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_sum_richness
#> [[1]]
#> [1] "TMe-1830" "TMe-2943" "TMe-2964" "TMe-2810" "TMe-952" "TMe-1218"
#> [7] "TMe-1425" "TMe-2975" "TMe-2152" "TMe-3481" "TMe-2453" "TMe-3501"
#> [13] "TMe-1117" "TMe-3252" "TMe-2785" "TMe-3539" "TMe-3514" "TMe-3623"
#> [19] "TMe-1930" "TMe-2066" "TMe-3353" "TMe-3262" "TMe-3553" "TMe-3087"
#> [25] "TMe-1922" "TMe-3112" "TMe-569" "TMe-2027" "TMe-1914" "TMe-2944"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-3557" "TMe-3495" "TMe-2952" "TMe-960" "TMe-196" "TMe-2903"
#> [7] "TMe-3101" "TMe-74" "TMe-3447" "TMe-2568" "TMe-40" "TMe-3800"
#> [13] "TMe-3366" "TMe-3239" "TMe-3805" "TMe-3766" "TMe-2258" "TMe-2021"
#> [19] "TMe-2211" "TMe-2257" "TMe-251" "TMe-1754" "TMe-3200" "TMe-369"
#> [25] "TMe-796" "TMe-2033" "TMe-409" "TMe-3547" "TMe-2352" "TMe-3258"
#> [31] "TMe-2000"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-1939" "TMe-3631" "TMe-3397" "TMe-1965" "TMe-425"
#> [7] "TMe-64" "TMe-3133" "TMe-3088" "TMe-14" "TMe-617" "TMe-2968"
#> [13] "TMe-2161" "TMe-2356" "TMe-2374" "TMe-1804" "TMe-3750" "TMe-161"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-3542" "TMe-372" "TMe-1988" "TMe-3327"
#> [7] "TMe-1020" "TMe-2059" "TMe-2039" "TMe-1167" "TMe-2458" "TMe-3511"
#> [13] "TMe-812" "TMe-63" "TMe-2240" "TMe-78" "TMe-3428" "TMe-241"
#> [19] "TMe-2924" "TMe-170" "TMe-280" "TMe-699" "TMe-2318" "TMe-2928"
#> [25] "TMe-387" "TMe-526" "TMe-3378" "TMe-2946" "TMe-516" "TMe-761"
#> [31] "TMe-737" "TMe-3108" "TMe-2947" "TMe-1987" "TMe-2788"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1220" "TMe-2003" "TMe-2853" "TMe-645" "TMe-745"
#> [7] "TMe-1622" "TMe-769" "TMe-1131" "TMe-1273" "TMe-2271" "TMe-2124"
#> [13] "TMe-682" "TMe-1299" "TMe-730" "TMe-2425" "TMe-98" "TMe-600"
#> [19] "TMe-1979" "TMe-2441" "TMe-803" "TMe-532" "TMe-892" "TMe-1290"
#> [25] "TMe-585" "TMe-1388" "TMe-1037" "TMe-1500" "TMe-406" "TMe-997"
#> [31] "TMe-2290" "TMe-1694" "TMe-323" "TMe-1375" "TMe-1427" "TMe-712"
#> [37] "TMe-954" "TMe-1004" "TMe-167" "TMe-1788"
#>
#> [[6]]
#> [1] "TMe-625" "TMe-1062" "TMe-1124" "TMe-1413" "TMe-531" "TMe-1646"
#> [7] "TMe-2791" "TMe-2983" "TMe-1945" "TMe-936" "TMe-1756" "TMe-222"
#> [13] "TMe-693" "TMe-598" "TMe-1832" "TMe-1661" "TMe-1035"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_sum_richness,
use.names = FALSE)) +
labs(title = "Random search", subtitle = "Pooled richness")
# Mean Shannon-Weaver diversity index
randomsel_mean_shannon <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "shannon",
metric = "mean", search = "random", local.search = NULL,
n.iter = 50)
randomsel_mean_shannon
#> [[1]]
#> [1] "TMe-1830" "TMe-2785" "TMe-469" "TMe-1973" "TMe-3481" "TMe-1869"
#> [7] "TMe-3548" "TMe-2967" "TMe-3104" "TMe-3353" "TMe-566" "TMe-3419"
#> [13] "TMe-1096" "TMe-3115" "TMe-778" "TMe-41" "TMe-1532" "TMe-1218"
#> [19] "TMe-3262" "TMe-300" "TMe-1581" "TMe-3726" "TMe-2027" "TMe-3694"
#> [25] "TMe-569" "TMe-3252" "TMe-815" "TMe-3465" "TMe-2103" "TMe-3132"
#> [31] "TMe-1922"
#>
#> [[2]]
#> [1] "TMe-2258" "TMe-1831" "TMe-196" "TMe-2352" "TMe-3766" "TMe-2951"
#> [7] "TMe-539" "TMe-2412" "TMe-2033" "TMe-3447" "TMe-2952" "TMe-369"
#> [13] "TMe-3101" "TMe-2329" "TMe-960" "TMe-2995" "TMe-3258" "TMe-2997"
#> [19] "TMe-171" "TMe-2568" "TMe-796" "TMe-3800" "TMe-3200" "TMe-681"
#> [25] "TMe-1732" "TMe-2211" "TMe-890" "TMe-2757" "TMe-3284" "TMe-2715"
#> [31] "TMe-3805"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-1819" "TMe-2270" "TMe-3133" "TMe-1804" "TMe-2161"
#> [7] "TMe-3397" "TMe-2968" "TMe-3715" "TMe-261" "TMe-2394" "TMe-234"
#> [13] "TMe-70" "TMe-3556" "TMe-14" "TMe-1738" "TMe-3638" "TMe-3644"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2839" "TMe-266" "TMe-182" "TMe-2240"
#> [7] "TMe-2971" "TMe-3538" "TMe-241" "TMe-3459" "TMe-1377" "TMe-3527"
#> [13] "TMe-897" "TMe-386" "TMe-460" "TMe-1150" "TMe-1434" "TMe-2788"
#> [19] "TMe-1987" "TMe-3537" "TMe-353" "TMe-3602" "TMe-2567" "TMe-427"
#> [25] "TMe-1988" "TMe-3257" "TMe-1336" "TMe-25" "TMe-516" "TMe-1580"
#> [31] "TMe-812" "TMe-368" "TMe-12" "TMe-2807" "TMe-1700"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1188" "TMe-2425" "TMe-332" "TMe-1196" "TMe-707"
#> [7] "TMe-877" "TMe-1559" "TMe-439" "TMe-1680" "TMe-723" "TMe-543"
#> [13] "TMe-245" "TMe-363" "TMe-1273" "TMe-1401" "TMe-2057" "TMe-419"
#> [19] "TMe-224" "TMe-360" "TMe-1037" "TMe-1934" "TMe-2907" "TMe-1004"
#> [25] "TMe-730" "TMe-1414" "TMe-1534" "TMe-688" "TMe-532" "TMe-803"
#> [31] "TMe-1427" "TMe-795" "TMe-362" "TMe-2750" "TMe-1265" "TMe-627"
#> [37] "TMe-823" "TMe-603" "TMe-167" "TMe-423"
#>
#> [[6]]
#> [1] "TMe-3177" "TMe-1392" "TMe-2791" "TMe-1481" "TMe-1566" "TMe-505"
#> [7] "TMe-1832" "TMe-1403" "TMe-1445" "TMe-625" "TMe-1992" "TMe-598"
#> [13] "TMe-1124" "TMe-1646" "TMe-1035" "TMe-693" "TMe-1413"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_mean_shannon,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Mean Shannon-Weaver diversity index")
# Pooled Shannon-Weaver diversity index
randomsel_sum_shannon <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "shannon",
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_sum_shannon
#> [[1]]
#> [1] "TMe-1830" "TMe-1532" "TMe-3132" "TMe-3115" "TMe-882" "TMe-2934"
#> [7] "TMe-3111" "TMe-28" "TMe-2810" "TMe-3087" "TMe-1096" "TMe-815"
#> [13] "TMe-1823" "TMe-3465" "TMe-3262" "TMe-44" "TMe-1451" "TMe-1425"
#> [19] "TMe-937" "TMe-2975" "TMe-865" "TMe-1973" "TMe-694" "TMe-2237"
#> [25] "TMe-778" "TMe-2964" "TMe-469" "TMe-2967" "TMe-2152" "TMe-3236"
#> [31] "TMe-3437"
#>
#> [[2]]
#> [1] "TMe-339" "TMe-2211" "TMe-1732" "TMe-674" "TMe-3366" "TMe-960"
#> [7] "TMe-2033" "TMe-2021" "TMe-2952" "TMe-2352" "TMe-3766" "TMe-40"
#> [13] "TMe-2329" "TMe-2757" "TMe-3557" "TMe-1474" "TMe-3495" "TMe-1505"
#> [19] "TMe-1754" "TMe-369" "TMe-2903" "TMe-3093" "TMe-1385" "TMe-2412"
#> [25] "TMe-3101" "TMe-3447" "TMe-2568" "TMe-74" "TMe-171" "TMe-1831"
#> [31] "TMe-196"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-14" "TMe-3638" "TMe-234" "TMe-3207"
#> [7] "TMe-1965" "TMe-2394" "TMe-1230" "TMe-3715" "TMe-1787" "TMe-1939"
#> [13] "TMe-1819" "TMe-3133" "TMe-381" "TMe-3569" "TMe-70" "TMe-3230"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-427" "TMe-460" "TMe-27" "TMe-1336"
#> [7] "TMe-1123" "TMe-1988" "TMe-1350" "TMe-3494" "TMe-1700" "TMe-3538"
#> [13] "TMe-3585" "TMe-840" "TMe-3108" "TMe-372" "TMe-2946" "TMe-2755"
#> [19] "TMe-1179" "TMe-2567" "TMe-3757" "TMe-1456" "TMe-63" "TMe-1020"
#> [25] "TMe-279" "TMe-3591" "TMe-1776" "TMe-699" "TMe-1376" "TMe-3378"
#> [31] "TMe-2928" "TMe-3409" "TMe-3542" "TMe-3255" "TMe-2924"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-823" "TMe-1500" "TMe-2124" "TMe-1188" "TMe-1037"
#> [7] "TMe-707" "TMe-870" "TMe-603" "TMe-532" "TMe-1159" "TMe-333"
#> [13] "TMe-712" "TMe-1979" "TMe-629" "TMe-2192" "TMe-2750" "TMe-1160"
#> [19] "TMe-3329" "TMe-224" "TMe-688" "TMe-487" "TMe-419" "TMe-1388"
#> [25] "TMe-2907" "TMe-627" "TMe-1534" "TMe-423" "TMe-167" "TMe-1427"
#> [31] "TMe-997" "TMe-1788" "TMe-245" "TMe-1273" "TMe-816" "TMe-600"
#> [37] "TMe-892" "TMe-2853" "TMe-2138" "TMe-612"
#>
#> [[6]]
#> [1] "TMe-1992" "TMe-2791" "TMe-2543" "TMe-751" "TMe-1403" "TMe-1076"
#> [7] "TMe-1413" "TMe-1062" "TMe-1217" "TMe-1124" "TMe-1302" "TMe-1661"
#> [13] "TMe-1608" "TMe-1646" "TMe-1503" "TMe-1756" "TMe-598"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_sum_shannon,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Pooled Shannon-Weaver diversity index")
# Mean Gini-Simpson diversity index
randomsel_mean_simpson <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "simpson",
metric = "mean", search = "random", local.search = NULL,
n.iter = 50)
randomsel_mean_simpson
#> [[1]]
#> [1] "TMe-1830" "TMe-3262" "TMe-1589" "TMe-3353" "TMe-3252" "TMe-2785"
#> [7] "TMe-3115" "TMe-3553" "TMe-1170" "TMe-3694" "TMe-2943" "TMe-1930"
#> [13] "TMe-3398" "TMe-1091" "TMe-3111" "TMe-3142" "TMe-815" "TMe-44"
#> [19] "TMe-2066" "TMe-3112" "TMe-2967" "TMe-1451" "TMe-1960" "TMe-3236"
#> [25] "TMe-3437" "TMe-2237" "TMe-1190" "TMe-952" "TMe-2103" "TMe-1425"
#> [31] "TMe-2810"
#>
#> [[2]]
#> [1] "TMe-3547" "TMe-3495" "TMe-2329" "TMe-2412" "TMe-2757" "TMe-3530"
#> [7] "TMe-251" "TMe-2611" "TMe-2021" "TMe-3200" "TMe-539" "TMe-2000"
#> [13] "TMe-171" "TMe-2258" "TMe-339" "TMe-674" "TMe-1698" "TMe-3101"
#> [19] "TMe-1385" "TMe-3805" "TMe-2903" "TMe-2952" "TMe-1754" "TMe-2568"
#> [25] "TMe-2715" "TMe-40" "TMe-3239" "TMe-369" "TMe-2352" "TMe-2211"
#> [31] "TMe-3800"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3631" "TMe-1819" "TMe-3100" "TMe-3750" "TMe-2374"
#> [7] "TMe-2968" "TMe-425" "TMe-3148" "TMe-2748" "TMe-1198" "TMe-3715"
#> [13] "TMe-1804" "TMe-3638" "TMe-3133" "TMe-1176" "TMe-161" "TMe-3644"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-1027" "TMe-3428" "TMe-3189" "TMe-812"
#> [7] "TMe-1350" "TMe-3108" "TMe-63" "TMe-699" "TMe-3250" "TMe-170"
#> [13] "TMe-840" "TMe-280" "TMe-3562" "TMe-191" "TMe-2247" "TMe-737"
#> [19] "TMe-2841" "TMe-25" "TMe-266" "TMe-3255" "TMe-3527" "TMe-2956"
#> [25] "TMe-3257" "TMe-460" "TMe-1016" "TMe-3537" "TMe-2039" "TMe-1988"
#> [31] "TMe-3542" "TMe-3757" "TMe-2755" "TMe-2924" "TMe-368"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1290" "TMe-1680" "TMe-1440" "TMe-612" "TMe-3329"
#> [7] "TMe-1760" "TMe-1453" "TMe-730" "TMe-627" "TMe-2213" "TMe-1004"
#> [13] "TMe-712" "TMe-247" "TMe-439" "TMe-1234" "TMe-795" "TMe-2853"
#> [19] "TMe-344" "TMe-543" "TMe-803" "TMe-98" "TMe-1295" "TMe-2441"
#> [25] "TMe-2750" "TMe-2753" "TMe-954" "TMe-688" "TMe-1220" "TMe-1979"
#> [31] "TMe-1375" "TMe-1188" "TMe-997" "TMe-256" "TMe-2425" "TMe-487"
#> [37] "TMe-2290" "TMe-1159" "TMe-1299" "TMe-323"
#>
#> [[6]]
#> [1] "TMe-2791" "TMe-1403" "TMe-310" "TMe-1992" "TMe-936" "TMe-531"
#> [7] "TMe-598" "TMe-1503" "TMe-1900" "TMe-1035" "TMe-751" "TMe-1062"
#> [13] "TMe-1744" "TMe-2543" "TMe-1816" "TMe-3177" "TMe-1217"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_mean_simpson,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Mean Gini-Simpson diversity index")
# Pooled Gini-Simpson diversity index
randomsel_sum_simpson <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "simpson",
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_sum_simpson
#> [[1]]
#> [1] "TMe-1830" "TMe-2103" "TMe-3132" "TMe-2785" "TMe-1717" "TMe-3262"
#> [7] "TMe-1869" "TMe-3623" "TMe-1589" "TMe-3087" "TMe-3685" "TMe-2066"
#> [13] "TMe-1922" "TMe-3437" "TMe-865" "TMe-910" "TMe-3112" "TMe-778"
#> [19] "TMe-2964" "TMe-44" "TMe-306" "TMe-2967" "TMe-3115" "TMe-3694"
#> [25] "TMe-500" "TMe-2513" "TMe-3539" "TMe-1086" "TMe-1914" "TMe-2943"
#> [31] "TMe-41"
#>
#> [[2]]
#> [1] "TMe-796" "TMe-2951" "TMe-3447" "TMe-3284" "TMe-3258" "TMe-2033"
#> [7] "TMe-2329" "TMe-2211" "TMe-369" "TMe-2021" "TMe-3805" "TMe-251"
#> [13] "TMe-1474" "TMe-674" "TMe-890" "TMe-2000" "TMe-3093" "TMe-1754"
#> [19] "TMe-2952" "TMe-1385" "TMe-3557" "TMe-3101" "TMe-2715" "TMe-2611"
#> [25] "TMe-1505" "TMe-171" "TMe-960" "TMe-1323" "TMe-1831" "TMe-40"
#> [31] "TMe-2352"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-1863" "TMe-2968" "TMe-425" "TMe-1965" "TMe-2733"
#> [7] "TMe-1819" "TMe-123" "TMe-234" "TMe-161" "TMe-3631" "TMe-1230"
#> [13] "TMe-3133" "TMe-261" "TMe-2270" "TMe-3638" "TMe-14" "TMe-3750"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-3757" "TMe-2807" "TMe-1016" "TMe-3242"
#> [7] "TMe-3542" "TMe-353" "TMe-956" "TMe-1988" "TMe-1996" "TMe-241"
#> [13] "TMe-27" "TMe-3585" "TMe-761" "TMe-460" "TMe-1020" "TMe-2059"
#> [19] "TMe-1776" "TMe-1456" "TMe-1336" "TMe-1765" "TMe-3273" "TMe-280"
#> [25] "TMe-2928" "TMe-2567" "TMe-2458" "TMe-619" "TMe-1350" "TMe-3758"
#> [31] "TMe-3000" "TMe-698" "TMe-3538" "TMe-2367" "TMe-737"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1375" "TMe-1453" "TMe-247" "TMe-816" "TMe-723"
#> [7] "TMe-1934" "TMe-224" "TMe-2853" "TMe-323" "TMe-1541" "TMe-419"
#> [13] "TMe-574" "TMe-348" "TMe-1414" "TMe-1440" "TMe-332" "TMe-2003"
#> [19] "TMe-803" "TMe-288" "TMe-688" "TMe-532" "TMe-543" "TMe-2750"
#> [25] "TMe-1159" "TMe-1979" "TMe-600" "TMe-2124" "TMe-98" "TMe-667"
#> [31] "TMe-823" "TMe-730" "TMe-1534" "TMe-363" "TMe-2138" "TMe-2855"
#> [37] "TMe-362" "TMe-1427" "TMe-2192" "TMe-256"
#>
#> [[6]]
#> [1] "TMe-1239" "TMe-1124" "TMe-1566" "TMe-693" "TMe-1945" "TMe-2543"
#> [7] "TMe-2791" "TMe-625" "TMe-1445" "TMe-751" "TMe-1392" "TMe-1900"
#> [13] "TMe-1992" "TMe-1403" "TMe-2983" "TMe-1076" "TMe-598"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_sum_simpson,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Pooled Gini-Simpson diversity index")
# Mean McIntosh diversity index
randomsel_mean_mcintosh <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "mcintosh",
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_mean_mcintosh
#> [[1]]
#> [1] "TMe-1830" "TMe-1581" "TMe-3051" "TMe-1425" "TMe-2810" "TMe-1869"
#> [7] "TMe-2513" "TMe-3553" "TMe-937" "TMe-3437" "TMe-2967" "TMe-1086"
#> [13] "TMe-1960" "TMe-3261" "TMe-3236" "TMe-44" "TMe-3142" "TMe-3111"
#> [19] "TMe-3112" "TMe-2934" "TMe-3539" "TMe-3726" "TMe-1914" "TMe-1190"
#> [25] "TMe-569" "TMe-815" "TMe-1096" "TMe-3087" "TMe-3132" "TMe-3351"
#> [31] "TMe-3353"
#>
#> [[2]]
#> [1] "TMe-681" "TMe-2611" "TMe-960" "TMe-1754" "TMe-2329" "TMe-1474"
#> [7] "TMe-2033" "TMe-3805" "TMe-796" "TMe-3766" "TMe-1668" "TMe-2715"
#> [13] "TMe-2568" "TMe-251" "TMe-2412" "TMe-3258" "TMe-3200" "TMe-339"
#> [19] "TMe-2757" "TMe-1831" "TMe-369" "TMe-171" "TMe-40" "TMe-3101"
#> [25] "TMe-3800" "TMe-1385" "TMe-2258" "TMe-3547" "TMe-196" "TMe-1505"
#> [31] "TMe-1732"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-64" "TMe-3007" "TMe-1176" "TMe-3569" "TMe-2356"
#> [7] "TMe-3644" "TMe-2968" "TMe-3631" "TMe-3148" "TMe-123" "TMe-1939"
#> [13] "TMe-3750" "TMe-1863" "TMe-1230" "TMe-70" "TMe-3715" "TMe-234"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2928" "TMe-3527" "TMe-2059" "TMe-1336"
#> [7] "TMe-2947" "TMe-1179" "TMe-2567" "TMe-65" "TMe-3511" "TMe-241"
#> [13] "TMe-1579" "TMe-1144" "TMe-1297" "TMe-186" "TMe-3255" "TMe-1155"
#> [19] "TMe-2458" "TMe-1776" "TMe-3378" "TMe-353" "TMe-259" "TMe-2956"
#> [25] "TMe-812" "TMe-1996" "TMe-3537" "TMe-3459" "TMe-1350" "TMe-2788"
#> [31] "TMe-698" "TMe-2924" "TMe-12" "TMe-460" "TMe-526"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-543" "TMe-224" "TMe-1934" "TMe-627" "TMe-1440"
#> [7] "TMe-1159" "TMe-1220" "TMe-2138" "TMe-419" "TMe-2907" "TMe-1453"
#> [13] "TMe-803" "TMe-532" "TMe-1003" "TMe-344" "TMe-439" "TMe-1788"
#> [19] "TMe-1880" "TMe-2016" "TMe-2753" "TMe-723" "TMe-1199" "TMe-1012"
#> [25] "TMe-795" "TMe-3329" "TMe-2425" "TMe-2355" "TMe-612" "TMe-730"
#> [31] "TMe-707" "TMe-2855" "TMe-997" "TMe-1427" "TMe-2853" "TMe-651"
#> [37] "TMe-2290" "TMe-256" "TMe-406" "TMe-629"
#>
#> [[6]]
#> [1] "TMe-1744" "TMe-968" "TMe-1302" "TMe-1062" "TMe-1646" "TMe-2983"
#> [7] "TMe-310" "TMe-1445" "TMe-1503" "TMe-1900" "TMe-1124" "TMe-1392"
#> [13] "TMe-1413" "TMe-2791" "TMe-854" "TMe-620" "TMe-693"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_mean_mcintosh,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Mean McIntosh diversity index")
# Pooled McIntosh diversity index
randomsel_sum_mcintosh <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "mcintosh",
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_sum_mcintosh
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-3539" "TMe-2103" "TMe-3337" "TMe-3548"
#> [7] "TMe-41" "TMe-3719" "TMe-865" "TMe-1425" "TMe-3437" "TMe-1973"
#> [13] "TMe-3398" "TMe-2810" "TMe-3115" "TMe-566" "TMe-2785" "TMe-2027"
#> [19] "TMe-2967" "TMe-1914" "TMe-3351" "TMe-3111" "TMe-778" "TMe-3694"
#> [25] "TMe-1960" "TMe-3252" "TMe-694" "TMe-3419" "TMe-2975" "TMe-2944"
#> [31] "TMe-888"
#>
#> [[2]]
#> [1] "TMe-3284" "TMe-2257" "TMe-409" "TMe-2329" "TMe-251" "TMe-2000"
#> [7] "TMe-3200" "TMe-2033" "TMe-74" "TMe-2611" "TMe-2952" "TMe-890"
#> [13] "TMe-1385" "TMe-2903" "TMe-3547" "TMe-171" "TMe-539" "TMe-2021"
#> [19] "TMe-2568" "TMe-3101" "TMe-3805" "TMe-2995" "TMe-796" "TMe-2997"
#> [25] "TMe-674" "TMe-2715" "TMe-3258" "TMe-681" "TMe-3557" "TMe-3800"
#> [31] "TMe-40"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-2161" "TMe-70" "TMe-2502" "TMe-425" "TMe-234"
#> [7] "TMe-1804" "TMe-14" "TMe-3230" "TMe-3715" "TMe-3638" "TMe-1198"
#> [13] "TMe-3750" "TMe-3643" "TMe-3336" "TMe-2086" "TMe-1863" "TMe-3100"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-1246" "TMe-1330" "TMe-737" "TMe-3511"
#> [7] "TMe-3527" "TMe-1376" "TMe-1020" "TMe-594" "TMe-427" "TMe-353"
#> [13] "TMe-2841" "TMe-812" "TMe-3000" "TMe-3781" "TMe-368" "TMe-1434"
#> [19] "TMe-3494" "TMe-840" "TMe-3602" "TMe-698" "TMe-761" "TMe-699"
#> [25] "TMe-3409" "TMe-1987" "TMe-2956" "TMe-1988" "TMe-3581" "TMe-3758"
#> [31] "TMe-3108" "TMe-428" "TMe-2924" "TMe-27" "TMe-3214"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-954" "TMe-247" "TMe-439" "TMe-2290" "TMe-1979"
#> [7] "TMe-1427" "TMe-870" "TMe-707" "TMe-98" "TMe-1500" "TMe-487"
#> [13] "TMe-623" "TMe-344" "TMe-823" "TMe-1273" "TMe-1760" "TMe-2355"
#> [19] "TMe-1299" "TMe-360" "TMe-406" "TMe-1788" "TMe-256" "TMe-723"
#> [25] "TMe-2853" "TMe-2016" "TMe-2425" "TMe-651" "TMe-755" "TMe-348"
#> [31] "TMe-167" "TMe-1541" "TMe-543" "TMe-1414" "TMe-2750" "TMe-2590"
#> [37] "TMe-1012" "TMe-419" "TMe-2907" "TMe-603"
#>
#> [[6]]
#> [1] "TMe-1832" "TMe-1035" "TMe-310" "TMe-2791" "TMe-3177" "TMe-505"
#> [7] "TMe-1124" "TMe-1566" "TMe-1445" "TMe-936" "TMe-1608" "TMe-751"
#> [13] "TMe-1302" "TMe-693" "TMe-1661" "TMe-1503" "TMe-2543"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_sum_mcintosh,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Pooled McIntosh diversity index")
# Mean Brillouin diversity index
randomsel_mean_brillouin <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.fun = div_fun_brillouin,
metric = "mean", search = "random", local.search = NULL,
n.iter = 50)
randomsel_mean_brillouin
#> [[1]]
#> [1] "TMe-1830" "TMe-2237" "TMe-2152" "TMe-1973" "TMe-694" "TMe-1922"
#> [7] "TMe-3539" "TMe-1581" "TMe-1914" "TMe-1086" "TMe-3142" "TMe-3110"
#> [13] "TMe-2967" "TMe-2943" "TMe-1190" "TMe-1823" "TMe-1096" "TMe-2934"
#> [19] "TMe-2785" "TMe-910" "TMe-1960" "TMe-3398" "TMe-1218" "TMe-1425"
#> [25] "TMe-3514" "TMe-3236" "TMe-2975" "TMe-41" "TMe-3104" "TMe-3130"
#> [31] "TMe-469"
#>
#> [[2]]
#> [1] "TMe-40" "TMe-3557" "TMe-2611" "TMe-2211" "TMe-2412" "TMe-2329"
#> [7] "TMe-2757" "TMe-1668" "TMe-539" "TMe-339" "TMe-3495" "TMe-369"
#> [13] "TMe-1505" "TMe-2257" "TMe-2997" "TMe-3101" "TMe-196" "TMe-74"
#> [19] "TMe-2951" "TMe-171" "TMe-1831" "TMe-1732" "TMe-3366" "TMe-2568"
#> [25] "TMe-3805" "TMe-3530" "TMe-2000" "TMe-2021" "TMe-3258" "TMe-3766"
#> [31] "TMe-1385"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3556" "TMe-123" "TMe-234" "TMe-3631" "TMe-3750"
#> [7] "TMe-2502" "TMe-64" "TMe-1804" "TMe-3715" "TMe-3638" "TMe-3207"
#> [13] "TMe-2356" "TMe-155" "TMe-3148" "TMe-1965" "TMe-1176" "TMe-3397"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2776" "TMe-1776" "TMe-182" "TMe-2567"
#> [7] "TMe-1139" "TMe-386" "TMe-259" "TMe-12" "TMe-3198" "TMe-840"
#> [13] "TMe-3581" "TMe-1376" "TMe-1350" "TMe-353" "TMe-1988" "TMe-3494"
#> [19] "TMe-25" "TMe-1377" "TMe-2956" "TMe-280" "TMe-737" "TMe-2807"
#> [25] "TMe-1297" "TMe-266" "TMe-1996" "TMe-2247" "TMe-3000" "TMe-1144"
#> [31] "TMe-3273" "TMe-3255" "TMe-1434" "TMe-3591" "TMe-279"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1196" "TMe-2271" "TMe-712" "TMe-1880" "TMe-2355"
#> [7] "TMe-2750" "TMe-288" "TMe-870" "TMe-795" "TMe-307" "TMe-167"
#> [13] "TMe-98" "TMe-730" "TMe-1680" "TMe-2003" "TMe-1220" "TMe-245"
#> [19] "TMe-362" "TMe-707" "TMe-2441" "TMe-769" "TMe-423" "TMe-487"
#> [25] "TMe-2213" "TMe-1453" "TMe-1375" "TMe-363" "TMe-2907" "TMe-997"
#> [31] "TMe-2853" "TMe-344" "TMe-1131" "TMe-612" "TMe-877" "TMe-224"
#> [37] "TMe-603" "TMe-1003" "TMe-623" "TMe-667"
#>
#> [[6]]
#> [1] "TMe-2791" "TMe-1646" "TMe-1945" "TMe-1744" "TMe-1566" "TMe-1392"
#> [7] "TMe-1900" "TMe-936" "TMe-620" "TMe-1445" "TMe-2983" "TMe-531"
#> [13] "TMe-1035" "TMe-661" "TMe-1816" "TMe-1403" "TMe-598"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_mean_brillouin,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Mean Brillouin diversity index")
# Pooled Brillouin diversity index
randomsel_sum_brillouin <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.fun = div_fun_brillouin,
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_sum_brillouin
#> [[1]]
#> [1] "TMe-1830" "TMe-1532" "TMe-3115" "TMe-3465" "TMe-2027" "TMe-2237"
#> [7] "TMe-3437" "TMe-3252" "TMe-1922" "TMe-3553" "TMe-3051" "TMe-2964"
#> [13] "TMe-3142" "TMe-306" "TMe-2934" "TMe-2967" "TMe-815" "TMe-910"
#> [19] "TMe-1869" "TMe-1117" "TMe-1086" "TMe-1096" "TMe-937" "TMe-1914"
#> [25] "TMe-3685" "TMe-1425" "TMe-2152" "TMe-28" "TMe-3419" "TMe-3539"
#> [31] "TMe-865"
#>
#> [[2]]
#> [1] "TMe-339" "TMe-369" "TMe-960" "TMe-674" "TMe-3766" "TMe-1754"
#> [7] "TMe-3447" "TMe-196" "TMe-2611" "TMe-3284" "TMe-3101" "TMe-2952"
#> [13] "TMe-409" "TMe-40" "TMe-3093" "TMe-3805" "TMe-3530" "TMe-3258"
#> [19] "TMe-3547" "TMe-2757" "TMe-1505" "TMe-2329" "TMe-171" "TMe-2903"
#> [25] "TMe-1323" "TMe-1385" "TMe-2995" "TMe-2257" "TMe-3200" "TMe-539"
#> [31] "TMe-796"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3336" "TMe-64" "TMe-3007" "TMe-234" "TMe-1230"
#> [7] "TMe-14" "TMe-1863" "TMe-1804" "TMe-2374" "TMe-3750" "TMe-3715"
#> [13] "TMe-2394" "TMe-123" "TMe-3631" "TMe-3643" "TMe-2161" "TMe-3569"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-525" "TMe-3242" "TMe-3527" "TMe-622"
#> [7] "TMe-1996" "TMe-2318" "TMe-3265" "TMe-2567" "TMe-2375" "TMe-3000"
#> [13] "TMe-2059" "TMe-3591" "TMe-3103" "TMe-3218" "TMe-3459" "TMe-372"
#> [19] "TMe-3409" "TMe-3072" "TMe-1020" "TMe-897" "TMe-698" "TMe-1377"
#> [25] "TMe-1988" "TMe-25" "TMe-266" "TMe-368" "TMe-1434" "TMe-526"
#> [31] "TMe-78" "TMe-241" "TMe-1987" "TMe-191" "TMe-318"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-2425" "TMe-769" "TMe-1299" "TMe-2750" "TMe-1934"
#> [7] "TMe-688" "TMe-712" "TMe-1265" "TMe-2590" "TMe-2441" "TMe-600"
#> [13] "TMe-2057" "TMe-730" "TMe-2213" "TMe-870" "TMe-667" "TMe-1268"
#> [19] "TMe-288" "TMe-423" "TMe-795" "TMe-2355" "TMe-574" "TMe-167"
#> [25] "TMe-98" "TMe-745" "TMe-645" "TMe-1694" "TMe-823" "TMe-2003"
#> [31] "TMe-1453" "TMe-877" "TMe-487" "TMe-1273" "TMe-1622" "TMe-1414"
#> [37] "TMe-954" "TMe-1293" "TMe-419" "TMe-1788"
#>
#> [[6]]
#> [1] "TMe-936" "TMe-1756" "TMe-531" "TMe-1062" "TMe-1403" "TMe-1646"
#> [7] "TMe-1992" "TMe-1302" "TMe-2983" "TMe-2791" "TMe-1744" "TMe-1217"
#> [13] "TMe-1445" "TMe-310" "TMe-1076" "TMe-3177" "TMe-1566"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_sum_brillouin,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Pooled Brillouin diversity index")
# Mean Margalef's richness index
randomsel_mean_margalef <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.fun = div_fun_margalef,
metric = "mean", search = "random", local.search = NULL,
n.iter = 50)
randomsel_mean_margalef
#> [[1]]
#> [1] "TMe-1830" "TMe-2975" "TMe-3694" "TMe-2810" "TMe-2027" "TMe-1532"
#> [7] "TMe-3685" "TMe-882" "TMe-3465" "TMe-3437" "TMe-3719" "TMe-3337"
#> [13] "TMe-3514" "TMe-3112" "TMe-2513" "TMe-910" "TMe-1091" "TMe-2944"
#> [19] "TMe-2967" "TMe-778" "TMe-1823" "TMe-2453" "TMe-3111" "TMe-1922"
#> [25] "TMe-2943" "TMe-937" "TMe-1170" "TMe-3351" "TMe-1096" "TMe-2152"
#> [31] "TMe-1218"
#>
#> [[2]]
#> [1] "TMe-2000" "TMe-3200" "TMe-3547" "TMe-369" "TMe-1754" "TMe-2903"
#> [7] "TMe-3805" "TMe-2033" "TMe-251" "TMe-2257" "TMe-3284" "TMe-40"
#> [13] "TMe-1668" "TMe-2329" "TMe-2997" "TMe-2951" "TMe-2352" "TMe-339"
#> [19] "TMe-1732" "TMe-74" "TMe-1385" "TMe-796" "TMe-2568" "TMe-2611"
#> [25] "TMe-3530" "TMe-1698" "TMe-3093" "TMe-409" "TMe-2715" "TMe-1474"
#> [31] "TMe-3239"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-1725" "TMe-3230" "TMe-123" "TMe-2161" "TMe-1230"
#> [7] "TMe-1863" "TMe-3397" "TMe-1804" "TMe-148" "TMe-3148" "TMe-1198"
#> [13] "TMe-3556" "TMe-3644" "TMe-3715" "TMe-14" "TMe-3620" "TMe-425"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-619" "TMe-186" "TMe-2958" "TMe-25"
#> [7] "TMe-1330" "TMe-3409" "TMe-3562" "TMe-812" "TMe-3558" "TMe-2240"
#> [13] "TMe-1179" "TMe-63" "TMe-191" "TMe-372" "TMe-259" "TMe-57"
#> [19] "TMe-3591" "TMe-2458" "TMe-3108" "TMe-386" "TMe-3000" "TMe-241"
#> [25] "TMe-3198" "TMe-1434" "TMe-737" "TMe-1297" "TMe-279" "TMe-12"
#> [31] "TMe-3144" "TMe-1020" "TMe-526" "TMe-1987" "TMe-3189"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-312" "TMe-1622" "TMe-651" "TMe-1534" "TMe-1500"
#> [7] "TMe-745" "TMe-2138" "TMe-2213" "TMe-423" "TMe-1220" "TMe-1101"
#> [13] "TMe-603" "TMe-1273" "TMe-2425" "TMe-755" "TMe-2855" "TMe-769"
#> [19] "TMe-723" "TMe-2853" "TMe-2590" "TMe-333" "TMe-532" "TMe-2290"
#> [25] "TMe-348" "TMe-2192" "TMe-1934" "TMe-1188" "TMe-823" "TMe-877"
#> [31] "TMe-588" "TMe-612" "TMe-892" "TMe-406" "TMe-2057" "TMe-1012"
#> [37] "TMe-1004" "TMe-288" "TMe-1559" "TMe-1290"
#>
#> [[6]]
#> [1] "TMe-1035" "TMe-2983" "TMe-1124" "TMe-1239" "TMe-693" "TMe-1217"
#> [7] "TMe-1832" "TMe-505" "TMe-1566" "TMe-1445" "TMe-2196" "TMe-1900"
#> [13] "TMe-2818" "TMe-1392" "TMe-2791" "TMe-1646" "TMe-631"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_mean_margalef,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Mean Margalef's diversity index")
# Pooled Margalef's richness index
randomsel_sum_margalef <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.fun = div_fun_margalef,
metric = "pooled", search = "random", local.search = NULL,
n.iter = 50)
randomsel_sum_margalef
#> [[1]]
#> [1] "TMe-1830" "TMe-2152" "TMe-300" "TMe-2934" "TMe-3112" "TMe-882"
#> [7] "TMe-3553" "TMe-1425" "TMe-2027" "TMe-3685" "TMe-1960" "TMe-865"
#> [13] "TMe-1914" "TMe-694" "TMe-1086" "TMe-1117" "TMe-3110" "TMe-2975"
#> [19] "TMe-3130" "TMe-3437" "TMe-3132" "TMe-937" "TMe-2943" "TMe-1170"
#> [25] "TMe-778" "TMe-3262" "TMe-41" "TMe-3548" "TMe-1218" "TMe-3514"
#> [31] "TMe-306"
#>
#> [[2]]
#> [1] "TMe-2000" "TMe-369" "TMe-1385" "TMe-2903" "TMe-40" "TMe-1754"
#> [7] "TMe-74" "TMe-3547" "TMe-3766" "TMe-674" "TMe-539" "TMe-2033"
#> [13] "TMe-1505" "TMe-1668" "TMe-2258" "TMe-2995" "TMe-3101" "TMe-2412"
#> [19] "TMe-339" "TMe-2021" "TMe-1323" "TMe-2211" "TMe-3093" "TMe-3200"
#> [25] "TMe-2715" "TMe-1831" "TMe-196" "TMe-2611" "TMe-251" "TMe-171"
#> [31] "TMe-2952"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3407" "TMe-2356" "TMe-1787" "TMe-3638" "TMe-3643"
#> [7] "TMe-3088" "TMe-148" "TMe-3100" "TMe-234" "TMe-3631" "TMe-3133"
#> [13] "TMe-3569" "TMe-1804" "TMe-14" "TMe-830" "TMe-1819" "TMe-70"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-1246" "TMe-2375" "TMe-170" "TMe-3538"
#> [7] "TMe-2839" "TMe-3248" "TMe-2788" "TMe-3072" "TMe-3108" "TMe-737"
#> [13] "TMe-2458" "TMe-1144" "TMe-1020" "TMe-619" "TMe-2956" "TMe-108"
#> [19] "TMe-235" "TMe-460" "TMe-698" "TMe-622" "TMe-1579" "TMe-279"
#> [25] "TMe-3511" "TMe-1148" "TMe-2841" "TMe-372" "TMe-3198" "TMe-2567"
#> [31] "TMe-3758" "TMe-191" "TMe-1434" "TMe-63" "TMe-3428"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-1234" "TMe-1220" "TMe-332" "TMe-1196" "TMe-920"
#> [7] "TMe-2907" "TMe-1004" "TMe-712" "TMe-629" "TMe-645" "TMe-1934"
#> [13] "TMe-344" "TMe-1188" "TMe-1541" "TMe-1290" "TMe-2124" "TMe-2192"
#> [19] "TMe-612" "TMe-247" "TMe-723" "TMe-487" "TMe-98" "TMe-707"
#> [25] "TMe-1979" "TMe-1453" "TMe-2425" "TMe-1265" "TMe-3329" "TMe-1160"
#> [31] "TMe-574" "TMe-667" "TMe-997" "TMe-2003" "TMe-795" "TMe-1414"
#> [37] "TMe-1273" "TMe-588" "TMe-2853" "TMe-816"
#>
#> [[6]]
#> [1] "TMe-1661" "TMe-531" "TMe-1445" "TMe-620" "TMe-1239" "TMe-1816"
#> [7] "TMe-1566" "TMe-693" "TMe-2818" "TMe-1062" "TMe-661" "TMe-751"
#> [13] "TMe-854" "TMe-2791" "TMe-1945" "TMe-1992" "TMe-1124"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(randomsel_sum_margalef,
use.names = FALSE)) +
labs(title = "Random search",
subtitle = "Pooled Margalef's diversity index")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Greedy search with 1-opt best improvement
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Mean richness
greedysel_best_mean_richness <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "richness",
metric = "mean", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_mean_richness
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-865" "TMe-2967" "TMe-3481" "TMe-3051" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-937" "TMe-1218" "TMe-2810" "TMe-3112"
#> [19] "TMe-2027" "TMe-778" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066"
#> [25] "TMe-469" "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-409" "TMe-2568" "TMe-3766" "TMe-2033" "TMe-2211" "TMe-1831"
#> [7] "TMe-369" "TMe-2903" "TMe-796" "TMe-3800" "TMe-74" "TMe-3366"
#> [13] "TMe-40" "TMe-3547" "TMe-3200" "TMe-2412" "TMe-2715" "TMe-681"
#> [19] "TMe-2951" "TMe-196" "TMe-339" "TMe-2995" "TMe-674" "TMe-2352"
#> [25] "TMe-2997" "TMe-1505" "TMe-2329" "TMe-1668" "TMe-1732" "TMe-3495"
#> [31] "TMe-960"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-234" "TMe-1804" "TMe-2161" "TMe-1738"
#> [13] "TMe-3100" "TMe-3336" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-2458" "TMe-3255" "TMe-3327"
#> [7] "TMe-1376" "TMe-698" "TMe-1434" "TMe-280" "TMe-3428" "TMe-3527"
#> [13] "TMe-241" "TMe-737" "TMe-1020" "TMe-259" "TMe-279" "TMe-25"
#> [19] "TMe-619" "TMe-3273" "TMe-1988" "TMe-1148" "TMe-2956" "TMe-1297"
#> [25] "TMe-699" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-57" "TMe-2318" "TMe-1016"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2853" "TMe-1003" "TMe-423" "TMe-1427"
#> [13] "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333" "TMe-2124"
#> [19] "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532" "TMe-745"
#> [25] "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247" "TMe-1012"
#> [31] "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131" "TMe-487"
#> [37] "TMe-997" "TMe-1880" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1816" "TMe-1124" "TMe-1566" "TMe-2791" "TMe-2818" "TMe-1661"
#> [7] "TMe-693" "TMe-751" "TMe-1445" "TMe-1608" "TMe-1302" "TMe-1392"
#> [13] "TMe-1900" "TMe-1481" "TMe-1239" "TMe-1062" "TMe-1945"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_mean_richness,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Mean richness")
# Pooled richness
greedysel_best_sum_richness <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "richness",
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_sum_richness
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-865" "TMe-2967" "TMe-3481" "TMe-3051" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-937" "TMe-1218" "TMe-2810" "TMe-3112"
#> [19] "TMe-2027" "TMe-778" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066"
#> [25] "TMe-469" "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-2715" "TMe-3200" "TMe-2412" "TMe-2329" "TMe-3530" "TMe-3766"
#> [7] "TMe-1668" "TMe-2568" "TMe-369" "TMe-3366" "TMe-74" "TMe-2033"
#> [13] "TMe-40" "TMe-3547" "TMe-3800" "TMe-681" "TMe-2951" "TMe-196"
#> [19] "TMe-339" "TMe-2995" "TMe-674" "TMe-2352" "TMe-1831" "TMe-2997"
#> [25] "TMe-796" "TMe-1505" "TMe-1732" "TMe-3495" "TMe-960" "TMe-2757"
#> [31] "TMe-3557"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-234" "TMe-1804" "TMe-2161" "TMe-1738"
#> [13] "TMe-3100" "TMe-3336" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-2807" "TMe-3255" "TMe-698"
#> [7] "TMe-266" "TMe-1434" "TMe-3248" "TMe-1020" "TMe-3527" "TMe-1579"
#> [13] "TMe-241" "TMe-737" "TMe-279" "TMe-25" "TMe-3428" "TMe-619"
#> [19] "TMe-3273" "TMe-280" "TMe-1148" "TMe-2956" "TMe-1297" "TMe-699"
#> [25] "TMe-259" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-1376" "TMe-57" "TMe-2318"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2853" "TMe-1003" "TMe-423" "TMe-1427"
#> [13] "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333" "TMe-2124"
#> [19] "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532" "TMe-745"
#> [25] "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247" "TMe-1012"
#> [31] "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131" "TMe-487"
#> [37] "TMe-997" "TMe-1880" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-936" "TMe-1403" "TMe-2791" "TMe-1816" "TMe-661" "TMe-1566"
#> [7] "TMe-2543" "TMe-1124" "TMe-3177" "TMe-693" "TMe-751" "TMe-1302"
#> [13] "TMe-1392" "TMe-1756" "TMe-1900" "TMe-1481" "TMe-1608"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_sum_richness,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Pooled richness")
# Mean Shannon-Weaver diversity index
greedysel_best_mean_shannon <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "shannon",
metric = "mean", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_mean_shannon
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2453" "TMe-2785"
#> [7] "TMe-1096" "TMe-778" "TMe-3112" "TMe-41" "TMe-3130" "TMe-1086"
#> [13] "TMe-2027" "TMe-1190" "TMe-3481" "TMe-3115" "TMe-3262" "TMe-1922"
#> [19] "TMe-1218" "TMe-3252" "TMe-3437" "TMe-1869" "TMe-2237" "TMe-566"
#> [25] "TMe-2975" "TMe-3398" "TMe-3111" "TMe-2810" "TMe-569" "TMe-937"
#> [31] "TMe-3236"
#>
#> [[2]]
#> [1] "TMe-409" "TMe-3200" "TMe-2903" "TMe-1754" "TMe-2033" "TMe-2952"
#> [7] "TMe-674" "TMe-2329" "TMe-74" "TMe-369" "TMe-3805" "TMe-40"
#> [13] "TMe-3766" "TMe-1385" "TMe-2211" "TMe-3101" "TMe-3366" "TMe-3547"
#> [19] "TMe-1668" "TMe-3530" "TMe-171" "TMe-2352" "TMe-539" "TMe-2568"
#> [25] "TMe-3447" "TMe-2021" "TMe-3093" "TMe-3284" "TMe-2257" "TMe-2000"
#> [31] "TMe-1732"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-234" "TMe-3397" "TMe-3569" "TMe-1804" "TMe-3100" "TMe-425"
#> [13] "TMe-3631" "TMe-2161" "TMe-1230" "TMe-1819" "TMe-261" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-3248" "TMe-698"
#> [7] "TMe-1434" "TMe-3527" "TMe-956" "TMe-241" "TMe-3542" "TMe-1020"
#> [13] "TMe-2958" "TMe-1988" "TMe-2924" "TMe-3273" "TMe-619" "TMe-18"
#> [19] "TMe-2971" "TMe-3428" "TMe-812" "TMe-1377" "TMe-280" "TMe-266"
#> [25] "TMe-3189" "TMe-3242" "TMe-2956" "TMe-1700" "TMe-1776" "TMe-1123"
#> [31] "TMe-25" "TMe-1376" "TMe-279" "TMe-427" "TMe-3390"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-419" "TMe-723"
#> [7] "TMe-3329" "TMe-2003" "TMe-2853" "TMe-877" "TMe-2290" "TMe-423"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-755" "TMe-532" "TMe-1220"
#> [19] "TMe-2907" "TMe-1541" "TMe-167" "TMe-1004" "TMe-2425" "TMe-1500"
#> [25] "TMe-816" "TMe-1427" "TMe-2750" "TMe-730" "TMe-667" "TMe-487"
#> [31] "TMe-1534" "TMe-344" "TMe-98" "TMe-1196" "TMe-1934" "TMe-1290"
#> [37] "TMe-823" "TMe-2855" "TMe-2192" "TMe-439"
#>
#> [[6]]
#> [1] "TMe-531" "TMe-1035" "TMe-1661" "TMe-2983" "TMe-1566" "TMe-2791"
#> [7] "TMe-625" "TMe-1124" "TMe-1816" "TMe-1392" "TMe-693" "TMe-2543"
#> [13] "TMe-751" "TMe-1592" "TMe-1403" "TMe-1900" "TMe-1302"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_mean_shannon,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Mean Shannon-Weaver diversity index")
# Pooled Shannon-Weaver diversity index
greedysel_best_sum_shannon <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "shannon",
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_sum_shannon
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2453" "TMe-2785"
#> [7] "TMe-1096" "TMe-778" "TMe-3112" "TMe-41" "TMe-3130" "TMe-1086"
#> [13] "TMe-2027" "TMe-1190" "TMe-3481" "TMe-3115" "TMe-3262" "TMe-1922"
#> [19] "TMe-1218" "TMe-3252" "TMe-3437" "TMe-1869" "TMe-2237" "TMe-566"
#> [25] "TMe-2975" "TMe-3398" "TMe-3111" "TMe-2810" "TMe-569" "TMe-937"
#> [31] "TMe-3236"
#>
#> [[2]]
#> [1] "TMe-40" "TMe-2352" "TMe-369" "TMe-2329" "TMe-251" "TMe-2568"
#> [7] "TMe-171" "TMe-2952" "TMe-2033" "TMe-3766" "TMe-1385" "TMe-1668"
#> [13] "TMe-539" "TMe-1754" "TMe-3200" "TMe-3547" "TMe-3101" "TMe-3530"
#> [19] "TMe-2021" "TMe-74" "TMe-674" "TMe-3447" "TMe-3805" "TMe-3366"
#> [25] "TMe-2257" "TMe-3093" "TMe-2000" "TMe-2211" "TMe-2903" "TMe-796"
#> [31] "TMe-3258"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-234" "TMe-3397" "TMe-3569" "TMe-1804" "TMe-3100" "TMe-425"
#> [13] "TMe-3631" "TMe-2161" "TMe-1230" "TMe-1819" "TMe-261" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-3248" "TMe-698"
#> [7] "TMe-1434" "TMe-3527" "TMe-956" "TMe-241" "TMe-3542" "TMe-1020"
#> [13] "TMe-2958" "TMe-1988" "TMe-2924" "TMe-3273" "TMe-619" "TMe-18"
#> [19] "TMe-2971" "TMe-3428" "TMe-812" "TMe-1377" "TMe-280" "TMe-266"
#> [25] "TMe-3189" "TMe-3242" "TMe-2956" "TMe-1700" "TMe-1776" "TMe-1123"
#> [31] "TMe-25" "TMe-1376" "TMe-279" "TMe-427" "TMe-3390"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-419" "TMe-723"
#> [7] "TMe-3329" "TMe-2003" "TMe-2853" "TMe-877" "TMe-2290" "TMe-423"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-755" "TMe-532" "TMe-1220"
#> [19] "TMe-2907" "TMe-1541" "TMe-167" "TMe-1004" "TMe-2425" "TMe-1500"
#> [25] "TMe-816" "TMe-1427" "TMe-2750" "TMe-730" "TMe-667" "TMe-487"
#> [31] "TMe-1534" "TMe-344" "TMe-98" "TMe-1196" "TMe-1934" "TMe-1290"
#> [37] "TMe-823" "TMe-2855" "TMe-2192" "TMe-439"
#>
#> [[6]]
#> [1] "TMe-1900" "TMe-1413" "TMe-1816" "TMe-2543" "TMe-1124" "TMe-3177"
#> [7] "TMe-693" "TMe-1661" "TMe-1392" "TMe-1566" "TMe-2983" "TMe-2791"
#> [13] "TMe-1403" "TMe-751" "TMe-1035" "TMe-1302" "TMe-310"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_sum_shannon,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Pooled Shannon-Weaver diversity index")
# Mean Gini-Simpson diversity index
greedysel_best_mean_simpson <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "simpson",
metric = "mean", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_mean_simpson
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-566"
#> [7] "TMe-2453" "TMe-41" "TMe-3252" "TMe-1096" "TMe-500" "TMe-3112"
#> [13] "TMe-1190" "TMe-3437" "TMe-2237" "TMe-3115" "TMe-1922" "TMe-778"
#> [19] "TMe-1086" "TMe-3685" "TMe-2027" "TMe-2810" "TMe-1960" "TMe-1589"
#> [25] "TMe-3262" "TMe-3398" "TMe-1869" "TMe-569" "TMe-3236" "TMe-937"
#> [31] "TMe-2975"
#>
#> [[2]]
#> [1] "TMe-3101" "TMe-1732" "TMe-369" "TMe-2033" "TMe-2952" "TMe-1385"
#> [7] "TMe-1754" "TMe-3766" "TMe-2568" "TMe-2329" "TMe-40" "TMe-2352"
#> [13] "TMe-3530" "TMe-539" "TMe-171" "TMe-3200" "TMe-2021" "TMe-2903"
#> [19] "TMe-674" "TMe-3805" "TMe-2257" "TMe-3284" "TMe-3447" "TMe-3366"
#> [25] "TMe-2000" "TMe-2211" "TMe-3547" "TMe-251" "TMe-1831" "TMe-3258"
#> [31] "TMe-3093"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-3148" "TMe-2161" "TMe-3631" "TMe-425" "TMe-1804"
#> [13] "TMe-1819" "TMe-261" "TMe-3100" "TMe-2374" "TMe-1230" "TMe-3397"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-241" "TMe-2924" "TMe-812"
#> [7] "TMe-3542" "TMe-1377" "TMe-2971" "TMe-3273" "TMe-619" "TMe-3527"
#> [13] "TMe-1434" "TMe-698" "TMe-3242" "TMe-280" "TMe-1020" "TMe-2958"
#> [19] "TMe-3189" "TMe-1776" "TMe-1988" "TMe-3390" "TMe-3255" "TMe-1376"
#> [25] "TMe-1297" "TMe-3198" "TMe-1350" "TMe-3428" "TMe-2956" "TMe-1144"
#> [31] "TMe-25" "TMe-372" "TMe-259" "TMe-266" "TMe-1579"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1375" "TMe-2853" "TMe-423" "TMe-1541" "TMe-2750" "TMe-344"
#> [19] "TMe-823" "TMe-1534" "TMe-730" "TMe-2192" "TMe-1500" "TMe-532"
#> [25] "TMe-487" "TMe-2907" "TMe-877" "TMe-1196" "TMe-167" "TMe-1159"
#> [31] "TMe-795" "TMe-98" "TMe-667" "TMe-2855" "TMe-816" "TMe-224"
#> [37] "TMe-1934" "TMe-1265" "TMe-1427" "TMe-688"
#>
#> [[6]]
#> [1] "TMe-598" "TMe-1816" "TMe-1992" "TMe-1566" "TMe-2983" "TMe-2543"
#> [7] "TMe-2791" "TMe-1035" "TMe-1403" "TMe-693" "TMe-1124" "TMe-1661"
#> [13] "TMe-1392" "TMe-751" "TMe-310" "TMe-2818" "TMe-1503"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_mean_simpson,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Mean Gini-Simpson diversity index")
# Pooled Gini-Simpson diversity index
greedysel_best_sum_simpson <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "simpson",
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_sum_simpson
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-1190"
#> [7] "TMe-2453" "TMe-41" "TMe-1589" "TMe-3262" "TMe-1096" "TMe-3481"
#> [13] "TMe-1086" "TMe-3115" "TMe-3437" "TMe-778" "TMe-2027" "TMe-3252"
#> [19] "TMe-566" "TMe-3112" "TMe-1922" "TMe-1869" "TMe-2810" "TMe-3685"
#> [25] "TMe-2237" "TMe-1218" "TMe-569" "TMe-3111" "TMe-2975" "TMe-300"
#> [31] "TMe-3398"
#>
#> [[2]]
#> [1] "TMe-3258" "TMe-2033" "TMe-369" "TMe-3766" "TMe-2568" "TMe-1754"
#> [7] "TMe-2952" "TMe-171" "TMe-1385" "TMe-3093" "TMe-2329" "TMe-3200"
#> [13] "TMe-40" "TMe-3805" "TMe-3530" "TMe-3366" "TMe-674" "TMe-3101"
#> [19] "TMe-539" "TMe-2352" "TMe-3284" "TMe-2021" "TMe-2257" "TMe-3547"
#> [25] "TMe-2000" "TMe-3447" "TMe-2903" "TMe-251" "TMe-2211" "TMe-1831"
#> [31] "TMe-1732"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-3148" "TMe-2161" "TMe-3631" "TMe-425" "TMe-1804"
#> [13] "TMe-2374" "TMe-3100" "TMe-261" "TMe-1230" "TMe-1819" "TMe-3397"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-241" "TMe-2971" "TMe-1377"
#> [7] "TMe-2924" "TMe-3255" "TMe-698" "TMe-1434" "TMe-3527" "TMe-1020"
#> [13] "TMe-3542" "TMe-3273" "TMe-3242" "TMe-619" "TMe-3390" "TMe-280"
#> [19] "TMe-812" "TMe-3189" "TMe-2958" "TMe-1988" "TMe-1376" "TMe-1776"
#> [25] "TMe-1297" "TMe-3198" "TMe-1350" "TMe-3428" "TMe-2956" "TMe-1144"
#> [31] "TMe-25" "TMe-372" "TMe-259" "TMe-266" "TMe-1579"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1375" "TMe-2853" "TMe-423" "TMe-1541" "TMe-2750" "TMe-344"
#> [19] "TMe-823" "TMe-1534" "TMe-730" "TMe-2192" "TMe-1500" "TMe-532"
#> [25] "TMe-487" "TMe-2907" "TMe-877" "TMe-1196" "TMe-167" "TMe-1159"
#> [31] "TMe-795" "TMe-98" "TMe-667" "TMe-2855" "TMe-816" "TMe-224"
#> [37] "TMe-1934" "TMe-1265" "TMe-1427" "TMe-688"
#>
#> [[6]]
#> [1] "TMe-310" "TMe-1124" "TMe-1503" "TMe-2791" "TMe-598" "TMe-2983"
#> [7] "TMe-1566" "TMe-1816" "TMe-1403" "TMe-693" "TMe-1413" "TMe-2543"
#> [13] "TMe-751" "TMe-1608" "TMe-1035" "TMe-1661" "TMe-1392"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_sum_simpson,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Pooled Gini-Simpson diversity index")
# Mean McIntosh diversity index
greedysel_best_mean_mcintosh <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "mcintosh",
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_mean_mcintosh
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3685" "TMe-2027" "TMe-3719" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3262" "TMe-566" "TMe-3112"
#> [19] "TMe-1922" "TMe-3437" "TMe-3539" "TMe-3236" "TMe-569" "TMe-2237"
#> [25] "TMe-2810" "TMe-300" "TMe-1589" "TMe-3398" "TMe-1869" "TMe-865"
#> [31] "TMe-2975"
#>
#> [[2]]
#> [1] "TMe-796" "TMe-3766" "TMe-369" "TMe-2568" "TMe-2033" "TMe-2952"
#> [7] "TMe-171" "TMe-1385" "TMe-2329" "TMe-3200" "TMe-2611" "TMe-1754"
#> [13] "TMe-3530" "TMe-40" "TMe-3805" "TMe-3101" "TMe-2352" "TMe-3366"
#> [19] "TMe-3447" "TMe-674" "TMe-539" "TMe-2903" "TMe-251" "TMe-1831"
#> [25] "TMe-3258" "TMe-2211" "TMe-2021" "TMe-3093" "TMe-2257" "TMe-3284"
#> [31] "TMe-2000"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1230" "TMe-1863" "TMe-1819" "TMe-261" "TMe-3148" "TMe-1804"
#> [13] "TMe-425" "TMe-3631" "TMe-2161" "TMe-2374" "TMe-3397" "TMe-3100"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-1144" "TMe-1434"
#> [7] "TMe-3527" "TMe-698" "TMe-2971" "TMe-241" "TMe-3409" "TMe-1988"
#> [13] "TMe-2924" "TMe-3273" "TMe-1376" "TMe-2958" "TMe-1020" "TMe-3542"
#> [19] "TMe-3242" "TMe-280" "TMe-812" "TMe-1776" "TMe-3189" "TMe-2956"
#> [25] "TMe-3198" "TMe-1377" "TMe-3428" "TMe-353" "TMe-3390" "TMe-1297"
#> [31] "TMe-1579" "TMe-372" "TMe-25" "TMe-1350" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1220" "TMe-2192" "TMe-2853" "TMe-423" "TMe-1375" "TMe-532"
#> [19] "TMe-877" "TMe-1500" "TMe-2750" "TMe-730" "TMe-167" "TMe-2907"
#> [25] "TMe-816" "TMe-667" "TMe-1541" "TMe-487" "TMe-1534" "TMe-344"
#> [31] "TMe-1196" "TMe-920" "TMe-98" "TMe-1401" "TMe-1159" "TMe-823"
#> [37] "TMe-1934" "TMe-224" "TMe-1004" "TMe-1295"
#>
#> [[6]]
#> [1] "TMe-598" "TMe-1816" "TMe-1992" "TMe-1566" "TMe-2791" "TMe-2543"
#> [7] "TMe-1124" "TMe-1035" "TMe-693" "TMe-1403" "TMe-2983" "TMe-1661"
#> [13] "TMe-1392" "TMe-751" "TMe-310" "TMe-2818" "TMe-1503"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_mean_mcintosh,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Mean McIntosh diversity index")
# Pooled McIntosh diversity index
greedysel_best_sum_mcintosh <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "mcintosh",
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_sum_mcintosh
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3685" "TMe-2027" "TMe-3719" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3262" "TMe-566" "TMe-3112"
#> [19] "TMe-1922" "TMe-3437" "TMe-3539" "TMe-3236" "TMe-569" "TMe-2237"
#> [25] "TMe-2810" "TMe-300" "TMe-1589" "TMe-3398" "TMe-1869" "TMe-865"
#> [31] "TMe-2975"
#>
#> [[2]]
#> [1] "TMe-1831" "TMe-2329" "TMe-40" "TMe-3805" "TMe-674" "TMe-3101"
#> [7] "TMe-1754" "TMe-1385" "TMe-2952" "TMe-2033" "TMe-369" "TMe-3766"
#> [13] "TMe-2611" "TMe-3200" "TMe-2021" "TMe-171" "TMe-3530" "TMe-2568"
#> [19] "TMe-3447" "TMe-539" "TMe-3366" "TMe-2352" "TMe-2257" "TMe-3284"
#> [25] "TMe-3093" "TMe-2000" "TMe-796" "TMe-2211" "TMe-3258" "TMe-2903"
#> [31] "TMe-251"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1230" "TMe-1863" "TMe-1819" "TMe-261" "TMe-3148" "TMe-1804"
#> [13] "TMe-425" "TMe-3631" "TMe-2161" "TMe-2374" "TMe-3397" "TMe-3100"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-1144" "TMe-1434"
#> [7] "TMe-3527" "TMe-698" "TMe-2971" "TMe-241" "TMe-3409" "TMe-1988"
#> [13] "TMe-2924" "TMe-3273" "TMe-1376" "TMe-2958" "TMe-1020" "TMe-3542"
#> [19] "TMe-3242" "TMe-280" "TMe-812" "TMe-1776" "TMe-3189" "TMe-2956"
#> [25] "TMe-3198" "TMe-1377" "TMe-3428" "TMe-353" "TMe-3390" "TMe-1297"
#> [31] "TMe-1579" "TMe-372" "TMe-25" "TMe-1350" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1220" "TMe-2192" "TMe-2853" "TMe-423" "TMe-1375" "TMe-532"
#> [19] "TMe-877" "TMe-1500" "TMe-2750" "TMe-730" "TMe-167" "TMe-2907"
#> [25] "TMe-816" "TMe-667" "TMe-1541" "TMe-487" "TMe-1534" "TMe-344"
#> [31] "TMe-1196" "TMe-920" "TMe-98" "TMe-1401" "TMe-1159" "TMe-823"
#> [37] "TMe-1934" "TMe-224" "TMe-1004" "TMe-1295"
#>
#> [[6]]
#> [1] "TMe-1503" "TMe-1992" "TMe-1661" "TMe-2983" "TMe-1566" "TMe-2791"
#> [7] "TMe-2543" "TMe-693" "TMe-1816" "TMe-1124" "TMe-1392" "TMe-1403"
#> [13] "TMe-1035" "TMe-2818" "TMe-751" "TMe-310" "TMe-598"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_sum_mcintosh,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Pooled McIntosh diversity index")
# Mean Brillouin diversity index
greedysel_best_mean_brillouin <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_brillouin,
metric = "mean", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_mean_brillouin
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3481" "TMe-2027" "TMe-3130" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3112" "TMe-1922" "TMe-3437"
#> [19] "TMe-566" "TMe-3262" "TMe-2237" "TMe-3539" "TMe-2810" "TMe-1869"
#> [25] "TMe-3398" "TMe-3252" "TMe-2975" "TMe-3111" "TMe-569" "TMe-1218"
#> [31] "TMe-300"
#>
#> [[2]]
#> [1] "TMe-1668" "TMe-171" "TMe-3200" "TMe-3547" "TMe-2033" "TMe-369"
#> [7] "TMe-2568" "TMe-3766" "TMe-40" "TMe-1385" "TMe-2329" "TMe-1732"
#> [13] "TMe-2952" "TMe-1754" "TMe-3805" "TMe-3366" "TMe-3530" "TMe-2211"
#> [19] "TMe-3101" "TMe-2352" "TMe-539" "TMe-2021" "TMe-674" "TMe-74"
#> [25] "TMe-3447" "TMe-2000" "TMe-2257" "TMe-3093" "TMe-2903" "TMe-3284"
#> [31] "TMe-409"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-3397" "TMe-3569" "TMe-3100" "TMe-1804" "TMe-425"
#> [13] "TMe-3631" "TMe-1819" "TMe-1230" "TMe-2161" "TMe-261" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-1434" "TMe-1700"
#> [7] "TMe-698" "TMe-3527" "TMe-2971" "TMe-241" "TMe-1155" "TMe-2924"
#> [13] "TMe-1988" "TMe-3273" "TMe-1020" "TMe-2958" "TMe-18" "TMe-372"
#> [19] "TMe-280" "TMe-812" "TMe-3542" "TMe-1776" "TMe-3428" "TMe-1376"
#> [25] "TMe-956" "TMe-3189" "TMe-3242" "TMe-1297" "TMe-2956" "TMe-266"
#> [31] "TMe-25" "TMe-1123" "TMe-3390" "TMe-1579" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-3329"
#> [7] "TMe-419" "TMe-2853" "TMe-2003" "TMe-755" "TMe-423" "TMe-2290"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-1220" "TMe-532" "TMe-1500"
#> [19] "TMe-877" "TMe-2750" "TMe-2907" "TMe-167" "TMe-816" "TMe-1004"
#> [25] "TMe-1427" "TMe-487" "TMe-667" "TMe-730" "TMe-1534" "TMe-1196"
#> [31] "TMe-344" "TMe-98" "TMe-2855" "TMe-823" "TMe-688" "TMe-2192"
#> [37] "TMe-362" "TMe-1290" "TMe-1541" "TMe-1934"
#>
#> [[6]]
#> [1] "TMe-1445" "TMe-1124" "TMe-1816" "TMe-2791" "TMe-598" "TMe-693"
#> [7] "TMe-1992" "TMe-1566" "TMe-2543" "TMe-2983" "TMe-1392" "TMe-1661"
#> [13] "TMe-751" "TMe-310" "TMe-2818" "TMe-1403" "TMe-1035"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_mean_brillouin,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Mean Brillouin diversity index")
# Pooled Brillouin diversity index
greedysel_best_sum_brillouin <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_brillouin,
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_sum_brillouin
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3481" "TMe-2027" "TMe-3130" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3112" "TMe-1922" "TMe-3437"
#> [19] "TMe-566" "TMe-3262" "TMe-2237" "TMe-3539" "TMe-2810" "TMe-1869"
#> [25] "TMe-3398" "TMe-3252" "TMe-2975" "TMe-3111" "TMe-569" "TMe-1218"
#> [31] "TMe-300"
#>
#> [[2]]
#> [1] "TMe-251" "TMe-2352" "TMe-369" "TMe-2568" "TMe-2329" "TMe-40"
#> [7] "TMe-171" "TMe-2952" "TMe-2033" "TMe-3766" "TMe-1385" "TMe-2021"
#> [13] "TMe-3530" "TMe-3200" "TMe-1754" "TMe-539" "TMe-3101" "TMe-1668"
#> [19] "TMe-3547" "TMe-674" "TMe-3805" "TMe-3366" "TMe-3447" "TMe-74"
#> [25] "TMe-2257" "TMe-3093" "TMe-2000" "TMe-2211" "TMe-2903" "TMe-796"
#> [31] "TMe-3258"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-3397" "TMe-3569" "TMe-3100" "TMe-1804" "TMe-425"
#> [13] "TMe-3631" "TMe-1819" "TMe-1230" "TMe-2161" "TMe-261" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-1434" "TMe-1700"
#> [7] "TMe-698" "TMe-3527" "TMe-2971" "TMe-241" "TMe-1155" "TMe-2924"
#> [13] "TMe-1988" "TMe-3273" "TMe-1020" "TMe-2958" "TMe-18" "TMe-372"
#> [19] "TMe-280" "TMe-812" "TMe-3542" "TMe-1776" "TMe-3428" "TMe-1376"
#> [25] "TMe-956" "TMe-3189" "TMe-3242" "TMe-1297" "TMe-2956" "TMe-266"
#> [31] "TMe-25" "TMe-1123" "TMe-3390" "TMe-1579" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-3329"
#> [7] "TMe-419" "TMe-2853" "TMe-2003" "TMe-755" "TMe-423" "TMe-2290"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-1220" "TMe-532" "TMe-1500"
#> [19] "TMe-877" "TMe-2750" "TMe-2907" "TMe-167" "TMe-816" "TMe-1004"
#> [25] "TMe-1427" "TMe-487" "TMe-667" "TMe-730" "TMe-1534" "TMe-1196"
#> [31] "TMe-344" "TMe-98" "TMe-2855" "TMe-823" "TMe-688" "TMe-2192"
#> [37] "TMe-362" "TMe-1290" "TMe-1541" "TMe-1934"
#>
#> [[6]]
#> [1] "TMe-2791" "TMe-1992" "TMe-1403" "TMe-1566" "TMe-1816" "TMe-2818"
#> [7] "TMe-751" "TMe-693" "TMe-2543" "TMe-1035" "TMe-1124" "TMe-1661"
#> [13] "TMe-1392" "TMe-2983" "TMe-310" "TMe-598" "TMe-1445"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_sum_brillouin,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Pooled Brillouin diversity index")
# Mean Margalef's richness index
greedysel_best_mean_margalef <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_margalef,
metric = "mean", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_mean_margalef
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-865" "TMe-41" "TMe-3051" "TMe-2785" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-937" "TMe-1218" "TMe-3112" "TMe-2027"
#> [19] "TMe-778" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066" "TMe-469"
#> [25] "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914" "TMe-1451"
#> [31] "TMe-1117"
#>
#> [[2]]
#> [1] "TMe-2258" "TMe-3766" "TMe-2952" "TMe-1385" "TMe-2329" "TMe-40"
#> [7] "TMe-3530" "TMe-369" "TMe-3547" "TMe-74" "TMe-3366" "TMe-2033"
#> [13] "TMe-3200" "TMe-2412" "TMe-2715" "TMe-3800" "TMe-681" "TMe-2951"
#> [19] "TMe-196" "TMe-339" "TMe-2995" "TMe-674" "TMe-2352" "TMe-1831"
#> [25] "TMe-2997" "TMe-796" "TMe-2568" "TMe-1505" "TMe-1668" "TMe-1732"
#> [31] "TMe-3495"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-234" "TMe-1804" "TMe-2161" "TMe-1738"
#> [13] "TMe-3336" "TMe-3100" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-2458" "TMe-3255" "TMe-3327"
#> [7] "TMe-1376" "TMe-698" "TMe-1434" "TMe-280" "TMe-3428" "TMe-3527"
#> [13] "TMe-241" "TMe-737" "TMe-1020" "TMe-3273" "TMe-259" "TMe-279"
#> [19] "TMe-25" "TMe-619" "TMe-1988" "TMe-1148" "TMe-2956" "TMe-1297"
#> [25] "TMe-699" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-57" "TMe-2318" "TMe-1016"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2853" "TMe-1003" "TMe-423" "TMe-1427"
#> [13] "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333" "TMe-2124"
#> [19] "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532" "TMe-745"
#> [25] "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247" "TMe-1012"
#> [31] "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131" "TMe-487"
#> [37] "TMe-997" "TMe-1880" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1900" "TMe-1302" "TMe-2543" "TMe-2791" "TMe-1062" "TMe-1816"
#> [7] "TMe-751" "TMe-1608" "TMe-693" "TMe-1392" "TMe-1124" "TMe-1756"
#> [13] "TMe-1592" "TMe-1566" "TMe-1481" "TMe-1239" "TMe-1945"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_mean_margalef,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Mean Margalef's diversity index")
# Pooled Margalef's richness index
greedysel_best_sum_margalef <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_margalef,
metric = "pooled", search = "greedy",
local.search = "best.improvement",max.iter = 3)
greedysel_best_sum_margalef
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-3481" "TMe-865" "TMe-2967" "TMe-3051" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-2810" "TMe-937" "TMe-1218" "TMe-2027"
#> [19] "TMe-778" "TMe-3112" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066"
#> [25] "TMe-469" "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-3766" "TMe-369" "TMe-2033" "TMe-2952" "TMe-40" "TMe-2611"
#> [7] "TMe-1385" "TMe-3800" "TMe-796" "TMe-74" "TMe-3366" "TMe-3200"
#> [13] "TMe-3547" "TMe-2412" "TMe-2715" "TMe-681" "TMe-2951" "TMe-196"
#> [19] "TMe-339" "TMe-2995" "TMe-674" "TMe-2352" "TMe-1831" "TMe-2997"
#> [25] "TMe-2568" "TMe-1505" "TMe-2329" "TMe-1668" "TMe-1732" "TMe-3495"
#> [31] "TMe-960"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-261" "TMe-1804" "TMe-2161" "TMe-234"
#> [13] "TMe-3100" "TMe-3336" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-1377" "TMe-1148" "TMe-3273" "TMe-3781"
#> [7] "TMe-2971" "TMe-698" "TMe-1434" "TMe-241" "TMe-3428" "TMe-3255"
#> [13] "TMe-3527" "TMe-280" "TMe-259" "TMe-1020" "TMe-279" "TMe-619"
#> [19] "TMe-25" "TMe-2567" "TMe-1988" "TMe-737" "TMe-2956" "TMe-1297"
#> [25] "TMe-699" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-1376" "TMe-57" "TMe-2318"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2355" "TMe-1880" "TMe-423" "TMe-2853"
#> [13] "TMe-1427" "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333"
#> [19] "TMe-2124" "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532"
#> [25] "TMe-745" "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247"
#> [31] "TMe-1012" "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131"
#> [37] "TMe-487" "TMe-997" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1062" "TMe-693" "TMe-1816" "TMe-1302" "TMe-1403" "TMe-1392"
#> [7] "TMe-1646" "TMe-2791" "TMe-1756" "TMe-1124" "TMe-1900" "TMe-751"
#> [13] "TMe-1592" "TMe-1566" "TMe-1481" "TMe-1608" "TMe-1239"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_best_sum_margalef,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt best improvement",
subtitle = "Pooled Margalef's diversity index")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Greedy search with 1-opt first improvement
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Mean richness
greedysel_first_mean_richness <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "richness",
metric = "mean", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_mean_richness
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-865" "TMe-2967" "TMe-3481" "TMe-3051" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-937" "TMe-1218" "TMe-2810" "TMe-3112"
#> [19] "TMe-2027" "TMe-778" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066"
#> [25] "TMe-469" "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-2033" "TMe-369" "TMe-196" "TMe-2952" "TMe-2568" "TMe-2021"
#> [7] "TMe-40" "TMe-3800" "TMe-796" "TMe-2715" "TMe-74" "TMe-3366"
#> [13] "TMe-3547" "TMe-3200" "TMe-2412" "TMe-681" "TMe-2951" "TMe-339"
#> [19] "TMe-2995" "TMe-674" "TMe-2352" "TMe-1831" "TMe-2997" "TMe-3766"
#> [25] "TMe-1505" "TMe-2329" "TMe-1668" "TMe-1732" "TMe-3495" "TMe-960"
#> [31] "TMe-2757"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-234" "TMe-1804" "TMe-2161" "TMe-1738"
#> [13] "TMe-3100" "TMe-3336" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-2458" "TMe-3255" "TMe-3327"
#> [7] "TMe-1376" "TMe-698" "TMe-1434" "TMe-280" "TMe-3428" "TMe-3527"
#> [13] "TMe-241" "TMe-737" "TMe-1020" "TMe-259" "TMe-279" "TMe-25"
#> [19] "TMe-619" "TMe-3273" "TMe-1988" "TMe-1148" "TMe-2956" "TMe-1297"
#> [25] "TMe-699" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-57" "TMe-2318" "TMe-1016"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2853" "TMe-1003" "TMe-423" "TMe-1427"
#> [13] "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333" "TMe-2124"
#> [19] "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532" "TMe-745"
#> [25] "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247" "TMe-1012"
#> [31] "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131" "TMe-487"
#> [37] "TMe-997" "TMe-1880" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1503" "TMe-1608" "TMe-2983" "TMe-1816" "TMe-693" "TMe-1392"
#> [7] "TMe-1062" "TMe-2791" "TMe-625" "TMe-1124" "TMe-1302" "TMe-1756"
#> [13] "TMe-751" "TMe-1900" "TMe-1592" "TMe-1566" "TMe-1481"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_mean_richness,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Mean richness")
# Pooled richness
greedysel_first_sum_richness <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "richness",
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_sum_richness
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-865" "TMe-2967" "TMe-3481" "TMe-3051" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-937" "TMe-1218" "TMe-2810" "TMe-3112"
#> [19] "TMe-2027" "TMe-778" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066"
#> [25] "TMe-469" "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-2995" "TMe-2329" "TMe-3530" "TMe-40" "TMe-681" "TMe-960"
#> [7] "TMe-2033" "TMe-2568" "TMe-369" "TMe-74" "TMe-3366" "TMe-3547"
#> [13] "TMe-3200" "TMe-2412" "TMe-2715" "TMe-3800" "TMe-2951" "TMe-196"
#> [19] "TMe-339" "TMe-674" "TMe-2352" "TMe-1831" "TMe-2997" "TMe-796"
#> [25] "TMe-3766" "TMe-1505" "TMe-1668" "TMe-1732" "TMe-3495" "TMe-2757"
#> [31] "TMe-3557"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-234" "TMe-1804" "TMe-2161" "TMe-1738"
#> [13] "TMe-3100" "TMe-3336" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-2807" "TMe-3255" "TMe-698"
#> [7] "TMe-266" "TMe-1434" "TMe-3248" "TMe-1020" "TMe-3527" "TMe-1579"
#> [13] "TMe-241" "TMe-737" "TMe-279" "TMe-25" "TMe-3428" "TMe-619"
#> [19] "TMe-3273" "TMe-280" "TMe-1148" "TMe-2956" "TMe-1297" "TMe-699"
#> [25] "TMe-259" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-1376" "TMe-57" "TMe-2318"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2853" "TMe-1003" "TMe-423" "TMe-1427"
#> [13] "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333" "TMe-2124"
#> [19] "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532" "TMe-745"
#> [25] "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247" "TMe-1012"
#> [31] "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131" "TMe-487"
#> [37] "TMe-997" "TMe-1880" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1992" "TMe-531" "TMe-1661" "TMe-693" "TMe-1392" "TMe-1816"
#> [7] "TMe-1124" "TMe-625" "TMe-751" "TMe-1608" "TMe-1302" "TMe-1900"
#> [13] "TMe-1592" "TMe-2791" "TMe-1566" "TMe-1481" "TMe-1239"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_sum_richness,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Pooled richness")
# Mean Shannon-Weaver diversity index
greedysel_first_mean_shannon <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "shannon",
metric = "mean", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_mean_shannon
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2453" "TMe-2785"
#> [7] "TMe-1096" "TMe-778" "TMe-3112" "TMe-41" "TMe-3130" "TMe-1086"
#> [13] "TMe-2027" "TMe-1190" "TMe-3481" "TMe-3115" "TMe-3262" "TMe-1922"
#> [19] "TMe-1218" "TMe-3252" "TMe-3437" "TMe-1869" "TMe-2237" "TMe-566"
#> [25] "TMe-2975" "TMe-3398" "TMe-3111" "TMe-2810" "TMe-569" "TMe-937"
#> [31] "TMe-3236"
#>
#> [[2]]
#> [1] "TMe-3101" "TMe-1474" "TMe-1385" "TMe-2952" "TMe-2033" "TMe-369"
#> [7] "TMe-1754" "TMe-3766" "TMe-2611" "TMe-2329" "TMe-40" "TMe-3805"
#> [13] "TMe-3200" "TMe-3366" "TMe-2568" "TMe-3530" "TMe-2257" "TMe-2352"
#> [19] "TMe-539" "TMe-171" "TMe-3547" "TMe-1831" "TMe-2903" "TMe-674"
#> [25] "TMe-2021" "TMe-74" "TMe-3447" "TMe-2000" "TMe-3093" "TMe-2211"
#> [31] "TMe-796"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-234" "TMe-261" "TMe-3133" "TMe-1804" "TMe-3100" "TMe-425"
#> [13] "TMe-3631" "TMe-2161" "TMe-1230" "TMe-1819" "TMe-2374" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-956" "TMe-698"
#> [7] "TMe-1434" "TMe-3527" "TMe-2807" "TMe-241" "TMe-3542" "TMe-1020"
#> [13] "TMe-2958" "TMe-1988" "TMe-2924" "TMe-3273" "TMe-619" "TMe-18"
#> [19] "TMe-2971" "TMe-3428" "TMe-812" "TMe-1377" "TMe-280" "TMe-266"
#> [25] "TMe-3189" "TMe-3242" "TMe-2956" "TMe-1700" "TMe-1776" "TMe-1123"
#> [31] "TMe-25" "TMe-1376" "TMe-279" "TMe-427" "TMe-3390"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-419" "TMe-723"
#> [7] "TMe-3329" "TMe-2003" "TMe-2853" "TMe-877" "TMe-2290" "TMe-423"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-755" "TMe-532" "TMe-1220"
#> [19] "TMe-2907" "TMe-1541" "TMe-167" "TMe-1004" "TMe-2425" "TMe-1500"
#> [25] "TMe-816" "TMe-1427" "TMe-2750" "TMe-730" "TMe-667" "TMe-487"
#> [31] "TMe-1534" "TMe-344" "TMe-98" "TMe-1196" "TMe-1934" "TMe-1290"
#> [37] "TMe-823" "TMe-2855" "TMe-2192" "TMe-439"
#>
#> [[6]]
#> [1] "TMe-1302" "TMe-1413" "TMe-1816" "TMe-2543" "TMe-1124" "TMe-3177"
#> [7] "TMe-693" "TMe-1661" "TMe-1392" "TMe-1566" "TMe-2983" "TMe-2791"
#> [13] "TMe-1403" "TMe-751" "TMe-1035" "TMe-2818" "TMe-310"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_mean_shannon,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Mean Shannon-Weaver diversity index")
# Pooled Shannon-Weaver diversity index
greedysel_first_sum_shannon <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "shannon",
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_sum_shannon
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2453" "TMe-2785"
#> [7] "TMe-1096" "TMe-778" "TMe-3112" "TMe-41" "TMe-3130" "TMe-1086"
#> [13] "TMe-2027" "TMe-1190" "TMe-3481" "TMe-3115" "TMe-3262" "TMe-1922"
#> [19] "TMe-1218" "TMe-3252" "TMe-3437" "TMe-1869" "TMe-2237" "TMe-566"
#> [25] "TMe-2975" "TMe-3398" "TMe-3111" "TMe-2810" "TMe-569" "TMe-937"
#> [31] "TMe-3236"
#>
#> [[2]]
#> [1] "TMe-2211" "TMe-3530" "TMe-1754" "TMe-2033" "TMe-2903" "TMe-2329"
#> [7] "TMe-3200" "TMe-1385" "TMe-40" "TMe-2952" "TMe-369" "TMe-3766"
#> [13] "TMe-2611" "TMe-171" "TMe-2568" "TMe-539" "TMe-3101" "TMe-2352"
#> [19] "TMe-2021" "TMe-3547" "TMe-3366" "TMe-3447" "TMe-3805" "TMe-674"
#> [25] "TMe-74" "TMe-1668" "TMe-2257" "TMe-3093" "TMe-2000" "TMe-3284"
#> [31] "TMe-796"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-234" "TMe-261" "TMe-3133" "TMe-1804" "TMe-3100" "TMe-425"
#> [13] "TMe-3631" "TMe-2161" "TMe-1230" "TMe-1819" "TMe-2374" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-956" "TMe-698"
#> [7] "TMe-1434" "TMe-3527" "TMe-2807" "TMe-241" "TMe-3542" "TMe-1020"
#> [13] "TMe-2958" "TMe-1988" "TMe-2924" "TMe-3273" "TMe-619" "TMe-18"
#> [19] "TMe-2971" "TMe-3428" "TMe-812" "TMe-1377" "TMe-280" "TMe-266"
#> [25] "TMe-3189" "TMe-3242" "TMe-2956" "TMe-1700" "TMe-1776" "TMe-1123"
#> [31] "TMe-25" "TMe-1376" "TMe-279" "TMe-427" "TMe-3390"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-419" "TMe-723"
#> [7] "TMe-3329" "TMe-2003" "TMe-2853" "TMe-877" "TMe-2290" "TMe-423"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-755" "TMe-532" "TMe-1220"
#> [19] "TMe-2907" "TMe-1541" "TMe-167" "TMe-1004" "TMe-2425" "TMe-1500"
#> [25] "TMe-816" "TMe-1427" "TMe-2750" "TMe-730" "TMe-667" "TMe-487"
#> [31] "TMe-1534" "TMe-344" "TMe-98" "TMe-1196" "TMe-1934" "TMe-1290"
#> [37] "TMe-823" "TMe-2855" "TMe-2192" "TMe-439"
#>
#> [[6]]
#> [1] "TMe-598" "TMe-2983" "TMe-1566" "TMe-2791" "TMe-2543" "TMe-693"
#> [7] "TMe-1124" "TMe-1816" "TMe-1661" "TMe-1392" "TMe-1035" "TMe-751"
#> [13] "TMe-1403" "TMe-2818" "TMe-661" "TMe-1900" "TMe-310"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_sum_shannon,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Pooled Shannon-Weaver diversity index")
# Mean Gini-Simpson diversity index
greedysel_first_mean_simpson <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "simpson",
metric = "mean", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_mean_simpson
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-566"
#> [7] "TMe-2453" "TMe-41" "TMe-3252" "TMe-1096" "TMe-500" "TMe-3112"
#> [13] "TMe-1190" "TMe-3437" "TMe-2237" "TMe-3115" "TMe-1922" "TMe-778"
#> [19] "TMe-1086" "TMe-3685" "TMe-2027" "TMe-2810" "TMe-1960" "TMe-3726"
#> [25] "TMe-3262" "TMe-3398" "TMe-1869" "TMe-569" "TMe-3236" "TMe-937"
#> [31] "TMe-2975"
#>
#> [[2]]
#> [1] "TMe-40" "TMe-2352" "TMe-369" "TMe-2952" "TMe-2033" "TMe-171"
#> [7] "TMe-3766" "TMe-2568" "TMe-2329" "TMe-3530" "TMe-3200" "TMe-2021"
#> [13] "TMe-1385" "TMe-1754" "TMe-3101" "TMe-539" "TMe-674" "TMe-3284"
#> [19] "TMe-3805" "TMe-2257" "TMe-3366" "TMe-2211" "TMe-2000" "TMe-3447"
#> [25] "TMe-3547" "TMe-251" "TMe-2903" "TMe-1831" "TMe-1732" "TMe-3093"
#> [31] "TMe-3258"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-3148" "TMe-2161" "TMe-3631" "TMe-425" "TMe-1804"
#> [13] "TMe-1819" "TMe-261" "TMe-3100" "TMe-2374" "TMe-1230" "TMe-3397"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-241" "TMe-2924" "TMe-812"
#> [7] "TMe-3542" "TMe-1377" "TMe-2971" "TMe-3273" "TMe-619" "TMe-3527"
#> [13] "TMe-1434" "TMe-698" "TMe-3242" "TMe-280" "TMe-1020" "TMe-2958"
#> [19] "TMe-3189" "TMe-1776" "TMe-1988" "TMe-3390" "TMe-3255" "TMe-1376"
#> [25] "TMe-1297" "TMe-3198" "TMe-1350" "TMe-3428" "TMe-2956" "TMe-1144"
#> [31] "TMe-25" "TMe-372" "TMe-259" "TMe-266" "TMe-1579"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1375" "TMe-2853" "TMe-423" "TMe-1541" "TMe-2750" "TMe-344"
#> [19] "TMe-823" "TMe-1534" "TMe-730" "TMe-2192" "TMe-1500" "TMe-532"
#> [25] "TMe-487" "TMe-2907" "TMe-877" "TMe-1196" "TMe-167" "TMe-1159"
#> [31] "TMe-795" "TMe-98" "TMe-667" "TMe-2855" "TMe-816" "TMe-224"
#> [37] "TMe-1934" "TMe-1265" "TMe-1427" "TMe-688"
#>
#> [[6]]
#> [1] "TMe-1816" "TMe-1124" "TMe-1392" "TMe-2791" "TMe-1566" "TMe-1608"
#> [7] "TMe-2983" "TMe-2543" "TMe-693" "TMe-598" "TMe-1035" "TMe-1661"
#> [13] "TMe-751" "TMe-2818" "TMe-1403" "TMe-310" "TMe-1992"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_mean_simpson,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Mean Gini-Simpson diversity index")
# Pooled Gini-Simpson diversity index
greedysel_first_sum_simpson <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "simpson",
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_sum_simpson
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-1190"
#> [7] "TMe-2453" "TMe-41" "TMe-1589" "TMe-3262" "TMe-1096" "TMe-3481"
#> [13] "TMe-1086" "TMe-3115" "TMe-3437" "TMe-778" "TMe-2027" "TMe-3252"
#> [19] "TMe-566" "TMe-3112" "TMe-1922" "TMe-1869" "TMe-2810" "TMe-3726"
#> [25] "TMe-2237" "TMe-1218" "TMe-569" "TMe-3111" "TMe-2975" "TMe-300"
#> [31] "TMe-3398"
#>
#> [[2]]
#> [1] "TMe-3766" "TMe-369" "TMe-2568" "TMe-2033" "TMe-1754" "TMe-2952"
#> [7] "TMe-1385" "TMe-2329" "TMe-3258" "TMe-171" "TMe-3200" "TMe-539"
#> [13] "TMe-3366" "TMe-2352" "TMe-3530" "TMe-40" "TMe-3805" "TMe-3101"
#> [19] "TMe-674" "TMe-3447" "TMe-2021" "TMe-3284" "TMe-2257" "TMe-3547"
#> [25] "TMe-2000" "TMe-2211" "TMe-2903" "TMe-251" "TMe-1732" "TMe-796"
#> [31] "TMe-3093"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-3148" "TMe-2161" "TMe-3631" "TMe-425" "TMe-1804"
#> [13] "TMe-2374" "TMe-3100" "TMe-261" "TMe-1230" "TMe-1819" "TMe-3397"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-241" "TMe-2971" "TMe-1377"
#> [7] "TMe-2924" "TMe-3255" "TMe-698" "TMe-1434" "TMe-3527" "TMe-1020"
#> [13] "TMe-3542" "TMe-3273" "TMe-3242" "TMe-619" "TMe-3390" "TMe-280"
#> [19] "TMe-812" "TMe-3189" "TMe-2958" "TMe-1988" "TMe-1376" "TMe-1776"
#> [25] "TMe-1297" "TMe-3198" "TMe-1350" "TMe-3428" "TMe-2956" "TMe-1144"
#> [31] "TMe-25" "TMe-372" "TMe-259" "TMe-266" "TMe-1579"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1375" "TMe-2853" "TMe-423" "TMe-1541" "TMe-2750" "TMe-344"
#> [19] "TMe-823" "TMe-1534" "TMe-730" "TMe-2192" "TMe-1500" "TMe-532"
#> [25] "TMe-487" "TMe-2907" "TMe-877" "TMe-1196" "TMe-167" "TMe-1159"
#> [31] "TMe-795" "TMe-98" "TMe-667" "TMe-2855" "TMe-816" "TMe-224"
#> [37] "TMe-1934" "TMe-1265" "TMe-1427" "TMe-688"
#>
#> [[6]]
#> [1] "TMe-1035" "TMe-310" "TMe-1062" "TMe-1403" "TMe-2818" "TMe-1816"
#> [7] "TMe-751" "TMe-2791" "TMe-1566" "TMe-2983" "TMe-2543" "TMe-693"
#> [13] "TMe-1124" "TMe-598" "TMe-1661" "TMe-1392" "TMe-1992"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_sum_simpson,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Pooled Gini-Simpson diversity index")
# Mean McIntosh diversity index
greedysel_first_mean_mcintosh <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "mcintosh",
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_mean_mcintosh
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3685" "TMe-2027" "TMe-3719" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3262" "TMe-566" "TMe-3112"
#> [19] "TMe-1922" "TMe-3437" "TMe-3539" "TMe-3236" "TMe-569" "TMe-2237"
#> [25] "TMe-2810" "TMe-300" "TMe-1589" "TMe-3398" "TMe-1869" "TMe-865"
#> [31] "TMe-2975"
#>
#> [[2]]
#> [1] "TMe-3258" "TMe-171" "TMe-3200" "TMe-1831" "TMe-2033" "TMe-369"
#> [7] "TMe-2568" "TMe-3766" "TMe-1385" "TMe-1754" "TMe-2952" "TMe-2611"
#> [13] "TMe-2329" "TMe-40" "TMe-3805" "TMe-3530" "TMe-3101" "TMe-2352"
#> [19] "TMe-3366" "TMe-2257" "TMe-539" "TMe-674" "TMe-2021" "TMe-3284"
#> [25] "TMe-2211" "TMe-2000" "TMe-2903" "TMe-251" "TMe-3447" "TMe-3093"
#> [31] "TMe-796"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1230" "TMe-1863" "TMe-1819" "TMe-261" "TMe-3148" "TMe-1804"
#> [13] "TMe-425" "TMe-3631" "TMe-2161" "TMe-2374" "TMe-3397" "TMe-3100"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-2807" "TMe-1434"
#> [7] "TMe-3527" "TMe-698" "TMe-2971" "TMe-241" "TMe-3409" "TMe-1988"
#> [13] "TMe-2924" "TMe-3273" "TMe-1376" "TMe-2958" "TMe-1020" "TMe-3542"
#> [19] "TMe-3242" "TMe-280" "TMe-812" "TMe-1776" "TMe-3189" "TMe-2956"
#> [25] "TMe-3198" "TMe-1377" "TMe-3428" "TMe-266" "TMe-3390" "TMe-1297"
#> [31] "TMe-1579" "TMe-619" "TMe-25" "TMe-1350" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1220" "TMe-2192" "TMe-2853" "TMe-423" "TMe-1375" "TMe-532"
#> [19] "TMe-877" "TMe-1500" "TMe-2750" "TMe-730" "TMe-167" "TMe-2907"
#> [25] "TMe-816" "TMe-667" "TMe-1541" "TMe-487" "TMe-1534" "TMe-344"
#> [31] "TMe-1196" "TMe-920" "TMe-98" "TMe-1401" "TMe-1159" "TMe-823"
#> [37] "TMe-1934" "TMe-224" "TMe-1004" "TMe-1295"
#>
#> [[6]]
#> [1] "TMe-1503" "TMe-1403" "TMe-2791" "TMe-1566" "TMe-2983" "TMe-1816"
#> [7] "TMe-2818" "TMe-693" "TMe-1661" "TMe-1124" "TMe-1035" "TMe-2543"
#> [13] "TMe-751" "TMe-598" "TMe-1392" "TMe-310" "TMe-1992"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_mean_mcintosh,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Mean McIntosh diversity index")
# Pooled McIntosh diversity index
greedysel_first_sum_mcintosh <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns, div.index = "mcintosh",
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_sum_mcintosh
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3685" "TMe-2027" "TMe-3719" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3262" "TMe-566" "TMe-3112"
#> [19] "TMe-1922" "TMe-3437" "TMe-3539" "TMe-3236" "TMe-569" "TMe-2237"
#> [25] "TMe-2810" "TMe-300" "TMe-1589" "TMe-3398" "TMe-1869" "TMe-865"
#> [31] "TMe-2975"
#>
#> [[2]]
#> [1] "TMe-2352" "TMe-369" "TMe-2000" "TMe-2329" "TMe-3366" "TMe-1754"
#> [7] "TMe-3530" "TMe-3766" "TMe-2033" "TMe-2952" "TMe-40" "TMe-1385"
#> [13] "TMe-3200" "TMe-171" "TMe-2568" "TMe-2611" "TMe-539" "TMe-3101"
#> [19] "TMe-3805" "TMe-1831" "TMe-674" "TMe-3547" "TMe-2257" "TMe-3284"
#> [25] "TMe-2412" "TMe-1732" "TMe-2903" "TMe-3258" "TMe-2211" "TMe-3093"
#> [31] "TMe-796"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1230" "TMe-1863" "TMe-1819" "TMe-261" "TMe-3148" "TMe-1804"
#> [13] "TMe-425" "TMe-3631" "TMe-2161" "TMe-2374" "TMe-3397" "TMe-3100"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-2807" "TMe-1434"
#> [7] "TMe-3527" "TMe-698" "TMe-2971" "TMe-241" "TMe-3409" "TMe-1988"
#> [13] "TMe-2924" "TMe-3273" "TMe-1376" "TMe-2958" "TMe-1020" "TMe-3542"
#> [19] "TMe-3242" "TMe-280" "TMe-812" "TMe-1776" "TMe-3189" "TMe-2956"
#> [25] "TMe-3198" "TMe-1377" "TMe-3428" "TMe-266" "TMe-3390" "TMe-1297"
#> [31] "TMe-1579" "TMe-619" "TMe-25" "TMe-1350" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-2290"
#> [7] "TMe-2355" "TMe-2003" "TMe-755" "TMe-419" "TMe-3329" "TMe-627"
#> [13] "TMe-1220" "TMe-2192" "TMe-2853" "TMe-423" "TMe-1375" "TMe-532"
#> [19] "TMe-877" "TMe-1500" "TMe-2750" "TMe-730" "TMe-167" "TMe-2907"
#> [25] "TMe-816" "TMe-667" "TMe-1541" "TMe-487" "TMe-1534" "TMe-344"
#> [31] "TMe-1196" "TMe-920" "TMe-98" "TMe-1401" "TMe-1159" "TMe-823"
#> [37] "TMe-1934" "TMe-224" "TMe-1004" "TMe-1295"
#>
#> [[6]]
#> [1] "TMe-1062" "TMe-1403" "TMe-693" "TMe-1816" "TMe-2818" "TMe-1035"
#> [7] "TMe-2791" "TMe-2543" "TMe-751" "TMe-1566" "TMe-2983" "TMe-1661"
#> [13] "TMe-1124" "TMe-598" "TMe-310" "TMe-1392" "TMe-1992"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_sum_mcintosh,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Pooled McIntosh diversity index")
# Mean Brillouin diversity index
greedysel_first_mean_brillouin <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_brillouin,
metric = "mean", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_mean_brillouin
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3481" "TMe-2027" "TMe-3130" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3112" "TMe-1922" "TMe-3437"
#> [19] "TMe-566" "TMe-3262" "TMe-2237" "TMe-3539" "TMe-2810" "TMe-1869"
#> [25] "TMe-3398" "TMe-3252" "TMe-2975" "TMe-3111" "TMe-569" "TMe-1218"
#> [31] "TMe-300"
#>
#> [[2]]
#> [1] "TMe-674" "TMe-1754" "TMe-3366" "TMe-369" "TMe-2033" "TMe-2952"
#> [7] "TMe-2211" "TMe-2329" "TMe-3200" "TMe-3805" "TMe-40" "TMe-1385"
#> [13] "TMe-3766" "TMe-3101" "TMe-3530" "TMe-2021" "TMe-3547" "TMe-171"
#> [19] "TMe-2568" "TMe-2352" "TMe-539" "TMe-74" "TMe-3447" "TMe-1668"
#> [25] "TMe-2000" "TMe-2257" "TMe-3093" "TMe-251" "TMe-2903" "TMe-796"
#> [31] "TMe-3258"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-261" "TMe-3133" "TMe-3100" "TMe-1804" "TMe-425"
#> [13] "TMe-3631" "TMe-1819" "TMe-1230" "TMe-2161" "TMe-2374" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-1434" "TMe-1700"
#> [7] "TMe-698" "TMe-3527" "TMe-2971" "TMe-241" "TMe-1155" "TMe-2924"
#> [13] "TMe-1988" "TMe-3273" "TMe-1020" "TMe-2958" "TMe-18" "TMe-372"
#> [19] "TMe-280" "TMe-812" "TMe-3542" "TMe-1776" "TMe-3428" "TMe-1376"
#> [25] "TMe-956" "TMe-3189" "TMe-3242" "TMe-1297" "TMe-2956" "TMe-266"
#> [31] "TMe-25" "TMe-1123" "TMe-3390" "TMe-1579" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-3329"
#> [7] "TMe-419" "TMe-2853" "TMe-2003" "TMe-755" "TMe-423" "TMe-2290"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-1220" "TMe-532" "TMe-1500"
#> [19] "TMe-877" "TMe-2750" "TMe-2907" "TMe-167" "TMe-816" "TMe-1004"
#> [25] "TMe-1427" "TMe-487" "TMe-667" "TMe-730" "TMe-1534" "TMe-1196"
#> [31] "TMe-344" "TMe-98" "TMe-2855" "TMe-823" "TMe-688" "TMe-2192"
#> [37] "TMe-362" "TMe-1290" "TMe-1541" "TMe-1934"
#>
#> [[6]]
#> [1] "TMe-1302" "TMe-1076" "TMe-531" "TMe-2983" "TMe-1816" "TMe-693"
#> [7] "TMe-1392" "TMe-1403" "TMe-1124" "TMe-2791" "TMe-1566" "TMe-2543"
#> [13] "TMe-310" "TMe-751" "TMe-1661" "TMe-1035" "TMe-2818"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_mean_brillouin,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Mean Brillouin diversity index")
# Pooled Brillouin diversity index
greedysel_first_sum_brillouin <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_brillouin,
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_sum_brillouin
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-2967" "TMe-2785" "TMe-2453"
#> [7] "TMe-1190" "TMe-41" "TMe-3481" "TMe-2027" "TMe-3130" "TMe-1096"
#> [13] "TMe-778" "TMe-1086" "TMe-3115" "TMe-3112" "TMe-1922" "TMe-3437"
#> [19] "TMe-566" "TMe-3262" "TMe-2237" "TMe-3539" "TMe-2810" "TMe-1869"
#> [25] "TMe-3398" "TMe-3252" "TMe-2975" "TMe-3111" "TMe-569" "TMe-1218"
#> [31] "TMe-300"
#>
#> [[2]]
#> [1] "TMe-539" "TMe-3766" "TMe-2903" "TMe-2329" "TMe-251" "TMe-2568"
#> [7] "TMe-171" "TMe-369" "TMe-2033" "TMe-2952" "TMe-2352" "TMe-40"
#> [13] "TMe-1385" "TMe-3200" "TMe-1754" "TMe-3530" "TMe-3101" "TMe-3547"
#> [19] "TMe-1668" "TMe-674" "TMe-3805" "TMe-2257" "TMe-3366" "TMe-3447"
#> [25] "TMe-74" "TMe-2021" "TMe-3093" "TMe-2000" "TMe-2211" "TMe-3258"
#> [31] "TMe-796"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-234"
#> [7] "TMe-1863" "TMe-261" "TMe-3133" "TMe-3100" "TMe-1804" "TMe-425"
#> [13] "TMe-3631" "TMe-1819" "TMe-1230" "TMe-2161" "TMe-2374" "TMe-3148"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-3255" "TMe-1434" "TMe-1700"
#> [7] "TMe-698" "TMe-3527" "TMe-2971" "TMe-241" "TMe-1155" "TMe-2924"
#> [13] "TMe-1988" "TMe-3273" "TMe-1020" "TMe-2958" "TMe-18" "TMe-372"
#> [19] "TMe-280" "TMe-812" "TMe-3542" "TMe-1776" "TMe-3428" "TMe-1376"
#> [25] "TMe-956" "TMe-3189" "TMe-3242" "TMe-1297" "TMe-2956" "TMe-266"
#> [31] "TMe-25" "TMe-1123" "TMe-3390" "TMe-1579" "TMe-897"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-2425" "TMe-723" "TMe-3329"
#> [7] "TMe-419" "TMe-2853" "TMe-2003" "TMe-755" "TMe-423" "TMe-2290"
#> [13] "TMe-1375" "TMe-2355" "TMe-627" "TMe-1220" "TMe-532" "TMe-1500"
#> [19] "TMe-877" "TMe-2750" "TMe-2907" "TMe-167" "TMe-816" "TMe-1004"
#> [25] "TMe-1427" "TMe-487" "TMe-667" "TMe-730" "TMe-1534" "TMe-1196"
#> [31] "TMe-344" "TMe-98" "TMe-2855" "TMe-823" "TMe-688" "TMe-2192"
#> [37] "TMe-362" "TMe-1290" "TMe-1541" "TMe-1934"
#>
#> [[6]]
#> [1] "TMe-1900" "TMe-693" "TMe-1816" "TMe-1302" "TMe-1403" "TMe-2791"
#> [7] "TMe-1124" "TMe-1062" "TMe-1566" "TMe-2983" "TMe-1661" "TMe-1392"
#> [13] "TMe-2818" "TMe-751" "TMe-1035" "TMe-2543" "TMe-310"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_sum_brillouin,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Pooled Brillouin diversity index")
# Mean Margalef's richness index
greedysel_first_mean_margalef <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_margalef,
metric = "mean", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_mean_margalef
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-865" "TMe-41" "TMe-3051" "TMe-2785" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-937" "TMe-1218" "TMe-3112" "TMe-2027"
#> [19] "TMe-778" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066" "TMe-469"
#> [25] "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914" "TMe-1451"
#> [31] "TMe-1117"
#>
#> [[2]]
#> [1] "TMe-3200" "TMe-2329" "TMe-3530" "TMe-2021" "TMe-40" "TMe-3766"
#> [7] "TMe-3101" "TMe-369" "TMe-1385" "TMe-3366" "TMe-74" "TMe-2033"
#> [13] "TMe-3547" "TMe-2412" "TMe-2715" "TMe-3800" "TMe-681" "TMe-2951"
#> [19] "TMe-196" "TMe-339" "TMe-2995" "TMe-674" "TMe-2352" "TMe-1831"
#> [25] "TMe-2997" "TMe-796" "TMe-2568" "TMe-1505" "TMe-1668" "TMe-1732"
#> [31] "TMe-3495"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-234" "TMe-1804" "TMe-2161" "TMe-1738"
#> [13] "TMe-3336" "TMe-3100" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-2567" "TMe-2458" "TMe-3255" "TMe-3327"
#> [7] "TMe-1376" "TMe-698" "TMe-1434" "TMe-280" "TMe-3428" "TMe-3527"
#> [13] "TMe-241" "TMe-737" "TMe-1020" "TMe-3273" "TMe-259" "TMe-279"
#> [19] "TMe-25" "TMe-619" "TMe-1988" "TMe-1148" "TMe-2956" "TMe-1297"
#> [25] "TMe-699" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-57" "TMe-2318" "TMe-1016"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2853" "TMe-1003" "TMe-423" "TMe-1427"
#> [13] "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333" "TMe-2124"
#> [19] "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532" "TMe-745"
#> [25] "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247" "TMe-1012"
#> [31] "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131" "TMe-487"
#> [37] "TMe-997" "TMe-1880" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1566" "TMe-2983" "TMe-310" "TMe-1392" "TMe-2543" "TMe-2791"
#> [7] "TMe-620" "TMe-1124" "TMe-1900" "TMe-693" "TMe-1302" "TMe-1608"
#> [13] "TMe-1756" "TMe-1592" "TMe-1481" "TMe-1239" "TMe-1062"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_mean_margalef,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Mean Margalef's richness index")
# Pooled Margalef's richness index
greedysel_first_sum_margalef <-
select.diversity(data = data, names = "genotypes", group = "Cluster",
alloc = counts, qualitative = traits,
always.selected = mand_accns,
div.fun = div_fun_margalef,
metric = "pooled", search = "greedy",
local.search = "first.improvement",max.iter = 3)
greedysel_first_sum_margalef
#> [[1]]
#> [1] "TMe-1830" "TMe-2934" "TMe-1425" "TMe-3694" "TMe-3236" "TMe-1086"
#> [7] "TMe-3481" "TMe-865" "TMe-2967" "TMe-3051" "TMe-2975" "TMe-1922"
#> [13] "TMe-3252" "TMe-3262" "TMe-2810" "TMe-937" "TMe-1218" "TMe-2027"
#> [19] "TMe-778" "TMe-3112" "TMe-1717" "TMe-3726" "TMe-1581" "TMe-2066"
#> [25] "TMe-469" "TMe-500" "TMe-952" "TMe-1170" "TMe-1532" "TMe-1914"
#> [31] "TMe-1451"
#>
#> [[2]]
#> [1] "TMe-251" "TMe-2951" "TMe-1831" "TMe-2568" "TMe-369" "TMe-40"
#> [7] "TMe-3805" "TMe-2329" "TMe-3200" "TMe-539" "TMe-74" "TMe-2033"
#> [13] "TMe-3547" "TMe-2412" "TMe-2715" "TMe-3800" "TMe-681" "TMe-196"
#> [19] "TMe-339" "TMe-2995" "TMe-674" "TMe-2352" "TMe-2997" "TMe-796"
#> [25] "TMe-3766" "TMe-1505" "TMe-1668" "TMe-1732" "TMe-3495" "TMe-960"
#> [31] "TMe-2757"
#>
#> [[3]]
#> [1] "TMe-1790" "TMe-3750" "TMe-3715" "TMe-3556" "TMe-70" "TMe-1863"
#> [7] "TMe-3133" "TMe-3397" "TMe-261" "TMe-1804" "TMe-2161" "TMe-234"
#> [13] "TMe-3100" "TMe-3336" "TMe-1787" "TMe-1939" "TMe-3644" "TMe-123"
#>
#> [[4]]
#> [1] "TMe-801" "TMe-3191" "TMe-1377" "TMe-1148" "TMe-3273" "TMe-3781"
#> [7] "TMe-2971" "TMe-698" "TMe-1434" "TMe-241" "TMe-3428" "TMe-3255"
#> [13] "TMe-3527" "TMe-280" "TMe-259" "TMe-1020" "TMe-279" "TMe-619"
#> [19] "TMe-25" "TMe-2567" "TMe-1988" "TMe-737" "TMe-2956" "TMe-1297"
#> [25] "TMe-699" "TMe-1987" "TMe-1155" "TMe-2375" "TMe-191" "TMe-1580"
#> [31] "TMe-1144" "TMe-3072" "TMe-1376" "TMe-57" "TMe-2318"
#>
#> [[5]]
#> [1] "TMe-2018" "TMe-712" "TMe-256" "TMe-707" "TMe-723" "TMe-419"
#> [7] "TMe-3329" "TMe-1004" "TMe-2355" "TMe-1880" "TMe-423" "TMe-2853"
#> [13] "TMe-1427" "TMe-588" "TMe-574" "TMe-629" "TMe-688" "TMe-333"
#> [19] "TMe-2124" "TMe-1375" "TMe-769" "TMe-344" "TMe-1220" "TMe-532"
#> [25] "TMe-745" "TMe-1534" "TMe-2151" "TMe-1293" "TMe-332" "TMe-247"
#> [31] "TMe-1012" "TMe-1290" "TMe-863" "TMe-439" "TMe-682" "TMe-1131"
#> [37] "TMe-487" "TMe-997" "TMe-1268" "TMe-1760"
#>
#> [[6]]
#> [1] "TMe-1413" "TMe-1403" "TMe-1816" "TMe-1566" "TMe-1124" "TMe-3177"
#> [7] "TMe-751" "TMe-1661" "TMe-1392" "TMe-1302" "TMe-693" "TMe-1900"
#> [13] "TMe-2791" "TMe-1481" "TMe-1608" "TMe-1239" "TMe-1062"
#>
plot_dist(d = dist_matrix, method = "isomds",
gp = gp_vec,
highlight = unlist(greedysel_first_sum_margalef,
use.names = FALSE)) +
labs(title = "Greed search | 1-opt first improvement",
subtitle = "Pooled Margalef's richness index")
# }