diff --git a/404.html b/404.html index 2815f65..4cc9f46 100644 --- a/404.html +++ b/404.html @@ -12,7 +12,7 @@ - + @@ -31,7 +31,7 @@ - +
- +
@@ -139,16 +139,16 @@

Page not found (404)

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.1.0.

- - + + diff --git a/articles/EvaluationofCoreCollections.html b/articles/EvaluationofCoreCollections.html index 8a18aed..2bd0ff0 100644 --- a/articles/EvaluationofCoreCollections.html +++ b/articles/EvaluationofCoreCollections.html @@ -12,14 +12,13 @@ - + - - +
@@ -125,9 +124,9 @@

Aravind, Nanjundan, J.4

-

2023-08-20

+

2024-08-18

- Source: vignettes/EvaluationofCoreCollections.Rmd + Source: vignettes/EvaluationofCoreCollections.Rmd
@@ -171,8 +170,7 @@

Overview

A core collection is a “limited set of accessions representing, with minimum repetitiveness, the genetic diversity of a crop species and -its wild relatives”(Frankel, -1984).

+its wild relatives”(Frankel, 1984).

In case of several large and unwieldy germplasm collections conserved in genebanks, development of several such core collections has facilitated increasing the efficiency of their characterisation and in @@ -181,7 +179,7 @@

OverviewOdong et al. (2013) have been implemented in +criteria including the distance based metrics described in Odong et al. (2013) have been implemented in EvaluateCore and this document shows how to use them. This document assumes a basic knowledge of R programming language.

@@ -199,11 +197,6 @@

Installation
 library(EvaluateCore)

-
Registered S3 methods overwritten by 'vegan':
-  method      from
-  plot.rda    klaR
-  predict.rda klaR
-  print.rda   klaR

 --------------------------------------------------------------------------------
 Welcome to EvaluateCore version 0.1.3.9000
@@ -269,30 +262,33 @@ 
Version History
 
 Genetic distance
-Average entry-to-nearest-entry distance (\(E-EN\))
+Average entry-to-nearest-entry distance
+(E−ENE-EN)
 dist.evaluate.core
 Quantitative & Qualitative
 CC–I
 Multivariate
-Odong et al. (2013)
+Odong et al. (2013)
 
 
 Genetic distance
-Average accession-to-nearest-entry distance (\(A-EN\))
+Average accession-to-nearest-entry distance
+(A−ENA-EN)
 dist.evaluate.core
 Quantitative & Qualitative
 CC–X
 Multivariate
-Odong et al. (2013)
+Odong et al. (2013)
 
 
 Genetic distance
-Average entry-to-entry distance (\(E-E\))
+Average entry-to-entry distance
+(E−EE-E)
 dist.evaluate.core
 Quantitative & Qualitative
 CC–X
 Multivariate
-Odong et al. (2013)
+Odong et al. (2013)
 
 
 Mean
@@ -302,7 +298,7 @@ 
Version HistoryCC–D
 Univariate
 
-Newman (1939); Keuls (1952)
+Newman (1939); Keuls (1952)
 
 
 
@@ -586,9 +582,7 @@ 
References
 
-        
-
+
@@ -601,16 +595,16 @@

References

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.1.0.

- - + + diff --git a/articles/additional/Example Core Data.html b/articles/additional/Example Core Data.html index 51b7399..732875a 100644 --- a/articles/additional/Example Core Data.html +++ b/articles/additional/Example Core Data.html @@ -12,14 +12,13 @@ - + - - - - - - -
+ + + + + + +

Example Datasets in @@ -126,9 +126,9 @@

Example Datasets in

J. Aravind

-

2023-08-20

+

2024-08-18

- Source: vignettes/additional/Example Core Data.Rmd + Source: vignettes/additional/Example Core Data.Rmd
@@ -144,8 +144,8 @@

Introduction(International Institute of Tropical Agriculture et -al., 2019).

+source data (International Institute of Tropical +Agriculture et al., 2019).

Setup the environment @@ -235,8 +235,8 @@

Load and prepare data DT::datatable(na_status, options = list(scrollX = TRUE, paging=TRUE))

-
-
+

+
 # Remove non informative fields
 cassava_EC$ID <- NULL
 cassava_EC$`Ploidy level` <- NULL
@@ -265,8 +265,8 @@ 
Prepare the descriptors
 DT::datatable(descriptors,
               options = list(scrollX = TRUE, paging=TRUE))

-

-
+

+
 colnames(cassava_EC) <- c("Accession name", descriptors$Abbr)
 
 qual <- descriptors[descriptors$Type == "Qualitative", ]$Abbr
@@ -579,13 +579,13 @@ 
Session Info
 sessionInfo()

-
## R version 4.3.1 (2023-06-16)
-## Platform: x86_64-apple-darwin20 (64-bit)
-## Running under: macOS Monterey 12.6.7
+
## R version 4.4.1 (2024-06-14)
+## Platform: aarch64-apple-darwin20
+## Running under: macOS Sonoma 14.6.1
 ## 
 ## Matrix products: default
-## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
-## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
 ## 
 ## locale:
 ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
@@ -597,21 +597,20 @@ 
Session Info## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-## [1] corehunter_3.2.2 rJava_1.0-6      readxl_1.4.3    
+## [1] corehunter_3.2.3 rJava_1.0-11     readxl_1.4.3    
 ## 
 ## loaded via a namespace (and not attached):
-##  [1] jsonlite_1.8.7    highr_0.10        dplyr_1.1.2       compiler_4.3.1   
-##  [5] tidyselect_1.2.0  stringr_1.5.0     jquerylib_0.1.4   systemfonts_1.0.4
-##  [9] textshaping_0.3.6 yaml_2.3.7        fastmap_1.1.1     R6_2.5.1         
-## [13] generics_0.1.3    knitr_1.43        htmlwidgets_1.6.2 naturalsort_0.1.3
-## [17] tibble_3.2.1      desc_1.4.2        rprojroot_2.0.3   bslib_0.5.1      
-## [21] pillar_1.9.0      rlang_1.1.1       DT_0.28           utf8_1.2.3       
-## [25] cachem_1.0.8      stringi_1.7.12    xfun_0.40         fs_1.6.3         
-## [29] sass_0.4.7        memoise_2.0.1     cli_3.6.1         pkgdown_2.0.7    
-## [33] magrittr_2.0.3    crosstalk_1.2.0   digest_0.6.33     lifecycle_1.0.3  
-## [37] vctrs_0.6.3       evaluate_0.21     glue_1.6.2        cellranger_1.1.0 
-## [41] ragg_1.2.5        fansi_1.0.4       rmarkdown_2.24    purrr_1.0.2      
-## [45] ellipsis_0.3.2    tools_4.3.1       pkgconfig_2.0.3   htmltools_0.5.6

+##  [1] vctrs_0.6.5       cli_3.6.3         knitr_1.48        rlang_1.1.4      
+##  [5] xfun_0.47         highr_0.11        generics_0.1.3    textshaping_0.4.0
+##  [9] jsonlite_1.8.8    glue_1.7.0        DT_0.33           htmltools_0.5.8.1
+## [13] ragg_1.3.2        sass_0.4.9        fansi_1.0.6       rmarkdown_2.28   
+## [17] cellranger_1.1.0  crosstalk_1.2.1   tibble_3.2.1      evaluate_0.24.0  
+## [21] jquerylib_0.1.4   fastmap_1.2.0     yaml_2.3.10       lifecycle_1.0.4  
+## [25] naturalsort_0.1.3 compiler_4.4.1    dplyr_1.1.4       fs_1.6.4         
+## [29] pkgconfig_2.0.3   htmlwidgets_1.6.4 systemfonts_1.1.0 digest_0.6.36    
+## [33] R6_2.5.1          tidyselect_1.2.1  utf8_1.2.4        pillar_1.9.0     
+## [37] magrittr_2.0.3    bslib_0.8.0       tools_4.4.1       pkgdown_2.1.0    
+## [41] cachem_1.1.0      desc_1.4.3

 

 

 
References
@@ -630,9 +629,7 @@ 
References
 
-        
-

+
@@ -645,16 +642,16 @@

References

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.1.0.

- - + + diff --git a/articles/additional/Example Core Data_files/crosstalk-1.2.0/css/crosstalk.min.css b/articles/additional/Example Core Data_files/crosstalk-1.2.1/css/crosstalk.min.css similarity index 100% rename from articles/additional/Example Core Data_files/crosstalk-1.2.0/css/crosstalk.min.css rename to articles/additional/Example Core Data_files/crosstalk-1.2.1/css/crosstalk.min.css diff --git a/articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.js b/articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.js similarity index 100% rename from articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.js rename to articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.js diff --git a/articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.js.map b/articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.js.map similarity index 100% rename from articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.js.map rename to articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.js.map diff --git a/articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.min.js b/articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.min.js similarity index 100% rename from articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.min.js rename to articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.min.js diff --git a/articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.min.js.map b/articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.min.js.map similarity index 100% rename from articles/additional/Example Core Data_files/crosstalk-1.2.0/js/crosstalk.min.js.map rename to articles/additional/Example Core Data_files/crosstalk-1.2.1/js/crosstalk.min.js.map diff --git a/articles/additional/Example Core Data_files/crosstalk-1.2.0/scss/crosstalk.scss b/articles/additional/Example Core Data_files/crosstalk-1.2.1/scss/crosstalk.scss similarity index 100% rename from articles/additional/Example Core Data_files/crosstalk-1.2.0/scss/crosstalk.scss rename to articles/additional/Example Core Data_files/crosstalk-1.2.1/scss/crosstalk.scss diff --git a/articles/additional/Example Core Data_files/datatables-binding-0.28/datatables.js b/articles/additional/Example Core Data_files/datatables-binding-0.33/datatables.js similarity index 95% rename from articles/additional/Example Core Data_files/datatables-binding-0.28/datatables.js rename to articles/additional/Example Core Data_files/datatables-binding-0.33/datatables.js index aee8ab5..765b53c 100644 --- a/articles/additional/Example Core Data_files/datatables-binding-0.28/datatables.js +++ b/articles/additional/Example Core Data_files/datatables-binding-0.33/datatables.js @@ -2,7 +2,7 @@ // some helper functions: using a global object DTWidget so that it can be used // in JS() code, e.g. datatable(options = list(foo = JS('code'))); unlike R's -// dynamic scoping, when 'code' is eval()'ed, JavaScript does not know objects +// dynamic scoping, when 'code' is eval'ed, JavaScript does not know objects // from the "parent frame", e.g. JS('DTWidget') will not work unless it was made // a global object var DTWidget = {}; @@ -348,6 +348,15 @@ HTMLWidgets.widget({ var table = $table.DataTable(options); $el.data('datatable', table); + if ('rowGroup' in options) { + // Maintain RowGroup dataSrc when columns are reordered (#1109) + table.on('column-reorder', function(e, settings, details) { + var oldDataSrc = table.rowGroup().dataSrc(); + var newDataSrc = details.mapping[oldDataSrc]; + table.rowGroup().dataSrc(newDataSrc); + }); + } + // Unregister previous Crosstalk event subscriptions, if they exist if (instance.ctfilterSubscription) { instance.ctfilterHandle.off("change", instance.ctfilterSubscription); @@ -432,10 +441,13 @@ HTMLWidgets.widget({ regex = options.search.regex, ci = options.search.caseInsensitive !== false; } + // need to transpose the column index when colReorder is enabled + if (table.colReorder) i = table.colReorder.transpose(i); return table.column(i).search(value, regex, !regex, ci); }; if (data.filter !== 'none') { + if (!data.hasOwnProperty('filterSettings')) data.filterSettings = {}; filterRow.each(function(i, td) { @@ -493,11 +505,13 @@ HTMLWidgets.widget({ $input.parent().hide(); $x.show().trigger('show'); filter[0].selectize.focus(); }, input: function() { - if ($input.val() === '') filter[0].selectize.setValue([]); + var v1 = JSON.stringify(filter[0].selectize.getValue()), v2 = $input.val(); + if (v1 === '[]') v1 = ''; + if (v1 !== v2) filter[0].selectize.setValue(v2 === '' ? [] : JSON.parse(v2)); } }); var $input2 = $x.children('select'); - filter = $input2.selectize({ + filter = $input2.selectize($.extend({ options: $input2.data('options').map(function(v, i) { return ({text: v, value: v}); }), @@ -509,15 +523,14 @@ HTMLWidgets.widget({ if (value.length) $input.trigger('input'); $input.attr('title', $input.val()); if (server) { - table.column(i).search(value.length ? JSON.stringify(value) : '').draw(); + searchColumn(i, value.length ? JSON.stringify(value) : '').draw(); return; } // turn off filter if nothing selected $td.data('filter', value.length > 0); table.draw(); // redraw table, and filters will be applied } - }); - if (searchCol) filter[0].selectize.setValue(JSON.parse(searchCol)); + }, data.filterSettings.select)); filter[0].selectize.on('blur', function() { $x.hide().trigger('hide'); $input.parent().show(); $input.trigger('blur'); }); @@ -526,10 +539,12 @@ HTMLWidgets.widget({ var fun = function() { searchColumn(i, $input.val()).draw(); }; - if (server) { - fun = $.fn.dataTable.util.throttle(fun, options.searchDelay); - } - $input.on('input', fun); + // throttle searching for server-side processing + var throttledFun = $.fn.dataTable.util.throttle(fun, options.searchDelay); + $input.on('input', function(e, immediate) { + // always bypass throttling when immediate = true (via the updateSearch method) + (immediate || !server) ? fun() : throttledFun(); + }); } else if (inArray(type, ['number', 'integer', 'date', 'time'])) { var $x0 = $x; $x = $x0.children('div').first(); @@ -615,13 +630,11 @@ HTMLWidgets.widget({ filter.val(v); } }); - var formatDate = function(d, isoFmt) { + var formatDate = function(d) { d = scaleBack(d, scale); if (type === 'number') return d; if (type === 'integer') return parseInt(d); var x = new Date(+d); - var fmt = ('filterDateFmt' in data) ? data.filterDateFmt[i] : undefined; - if (fmt !== undefined && isoFmt === false) return x[fmt.method].apply(x, fmt.params); if (type === 'date') { var pad0 = function(x) { return ('0' + x).substr(-2, 2); @@ -642,7 +655,7 @@ HTMLWidgets.widget({ start: [r1, r2], range: {min: r1, max: r2}, connect: true - }, opts)); + }, opts, data.filterSettings.slider)); if (scale > 1) (function() { var t1 = r1, t2 = r2; var val = filter.val(); @@ -657,13 +670,28 @@ HTMLWidgets.widget({ start: [t1, t2], range: {min: t1, max: t2}, connect: true - }, opts), true); + }, opts, data.filterSettings.slider), true); val = filter.val(); } r1 = t1; r2 = t2; })(); + // format with active column renderer, if defined + var colDef = data.options.columnDefs.find(function(def) { + return (def.targets === i || inArray(i, def.targets)) && 'render' in def; + }); var updateSliderText = function(v1, v2) { - $span1.text(formatDate(v1, false)); $span2.text(formatDate(v2, false)); + // we only know how to use function renderers + if (colDef && typeof colDef.render === 'function') { + var restore = function(v) { + v = scaleBack(v, scale); + return inArray(type, ['date', 'time']) ? new Date(+v) : v; + } + $span1.text(colDef.render(restore(v1), 'display')); + $span2.text(colDef.render(restore(v2), 'display')); + } else { + $span1.text(formatDate(v1)); + $span2.text(formatDate(v2)); + } }; updateSliderText(r1, r2); var updateSlider = function(e) { @@ -680,7 +708,7 @@ HTMLWidgets.widget({ updateSliderText(val[0], val[1]); if (e.type === 'slide') return; // no searching when sliding only if (server) { - table.column(i).search($td.data('filter') ? ival : '').draw(); + searchColumn(i, $td.data('filter') ? ival : '').draw(); return; } table.draw(); @@ -696,7 +724,7 @@ HTMLWidgets.widget({ // processing if (server) { // if a search string has been pre-set, search now - if (searchCol) searchColumn(i, searchCol).draw(); + if (searchCol) $input.trigger('input').trigger('change'); return; } @@ -742,15 +770,7 @@ HTMLWidgets.widget({ $.fn.dataTable.ext.search.push(customFilter); // search for the preset search strings if it is non-empty - if (searchCol) { - if (inArray(type, ['factor', 'logical'])) { - filter[0].selectize.setValue(JSON.parse(searchCol)); - } else if (type === 'character') { - $input.trigger('input'); - } else if (inArray(type, ['number', 'integer', 'date', 'time'])) { - $input.trigger('change'); - } - } + if (searchCol) $input.trigger('input').trigger('change'); }); @@ -1012,6 +1032,9 @@ HTMLWidgets.widget({ updateColsSelected(); updateCellsSelected(); }) + updateRowsSelected(); + updateColsSelected(); + updateCellsSelected(); } var selMode = data.selection.mode, selTarget = data.selection.target; @@ -1370,7 +1393,7 @@ HTMLWidgets.widget({ changeInput('cell_clicked', {}); // do not trigger table selection when clicking on links unless they have classes - table.on('click.dt', 'tbody td a', function(e) { + table.on('mousedown.dt', 'tbody td a', function(e) { if (this.className === '') e.stopPropagation(); }); @@ -1398,8 +1421,9 @@ HTMLWidgets.widget({ console.log('The search keyword for column ' + i + ' is undefined') return; } - $(td).find('input').first().val(v); - searchColumn(i, v); + // Update column search string and values on linked filter widgets. + // 'input' for factor and char filters, 'change' for numeric filters. + $(td).find('input').first().val(v).trigger('input', [true]).trigger('change'); }); table.draw(); } diff --git a/articles/additional/Example Core Data_files/dt-core-1.13.4/css/jquery.dataTables.min.css b/articles/additional/Example Core 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Data_files/dt-core-1.13.6/js/jquery.dataTables.min.js @@ -0,0 +1,4 @@ +/*! DataTables 1.13.6 + * ©2008-2023 SpryMedia Ltd - datatables.net/license + */ +!function(n){"use strict";var a;"function"==typeof define&&define.amd?define(["jquery"],function(t){return n(t,window,document)}):"object"==typeof exports?(a=require("jquery"),"undefined"==typeof window?module.exports=function(t,e){return t=t||window,e=e||a(t),n(e,t,t.document)}:n(a,window,window.document)):window.DataTable=n(jQuery,window,document)}(function(P,j,v,H){"use strict";function d(t){var e=parseInt(t,10);return!isNaN(e)&&isFinite(t)?e:null}function l(t,e,n){var a=typeof t,r="string"==a;return"number"==a||"bigint"==a||!!h(t)||(e&&r&&(t=$(t,e)),n&&r&&(t=t.replace(q,"")),!isNaN(parseFloat(t))&&isFinite(t))}function a(t,e,n){var a;return!!h(t)||(h(a=t)||"string"==typeof a)&&!!l(t.replace(V,"").replace(/Articles • EvaluateCore - +
- +
@@ -104,15 +104,15 @@

All vignettes

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.1.0.

- - + + diff --git a/authors.html b/authors.html index 14dc282..6ef37ef 100644 --- a/authors.html +++ b/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • EvaluateCoreAuthors and Citation • EvaluateCore - +
- +
-

Authors

+

Authors and Citation

- +

  • J. Aravind. Author, maintainer.

    @@ -105,23 +105,23 @@

    Authors

  • -

    ICAR-NBGPR. Copyright holder. +

    ICAR-NBGPR. Copyright holder.
    www.nbpgr.ernet.in

Citation

- Source: inst/CITATION + Source: inst/CITATION
-

Aravind, J., Kaur, V., Wankhede, D. P. and Nanjundan, J. (2023). EvaluateCore: Quality Evaluation of Core Collections. R package version 0.1.3.9000, https://aravind-j.github.io/EvaluateCore/https://CRAN.R-project.org/package=EvaluateCore.

+

Aravind, J., Kaur, V., Wankhede, D. P. and Nanjundan, J. (2024). EvaluateCore: Quality Evaluation of Core Collections. R package version 0.1.3.9000, https://aravind-j.github.io/EvaluateCore/https://CRAN.R-project.org/package=EvaluateCore.

@Manual{,
   title = {EvaluateCore: Quality Evaluation of Core Collections},
   author = {J. Aravind and Vikender Kaur and Dhammaprakash Pandhari Wankhede and J. Nanjundan},
-  year = {2023},
+  year = {2024},
   note = {R package version 0.1.3.9000 https://aravind-j.github.io/EvaluateCore/ https://CRAN.R-project.org/package=EvaluateCore},
 }
@@ -136,15 +136,15 @@

Citation

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.1.0.

- - + + diff --git a/index.html b/index.html index 49de8de..1dd0c7a 100644 --- a/index.html +++ b/index.html @@ -12,17 +12,14 @@ - + - + - +
@@ -125,8 +122,8 @@

EvaluateCore: Quality Evaluation of Core Collections logo

-
Version : 0.1.3.9000; Copyright (C) 2018-2023: ICAR-NBPGR; License: GPL-2|GPL-3 - +
Version : 0.1.3.9000; Copyright (C) 2018-2024: ICAR-NBPGR; License: GPL-2|GPL-3 +
@@ -140,7 +137,7 @@
  • ICAR-Indian Agricultural Research Institute, Regional Station, Wellington, Tamil Nadu.
    • -

    minimal R version License: GPL v3 CRAN_Status_Badge Dependencies rstudio mirror downloads develVersion Github Code Size R-CMD-check Project Status: WIP lifecycle Last-changedate Zenodo DOI Website - pkgdown .

    +

    minimal R version License: GPL v3 CRAN_Status_Badge Dependencies rstudio mirror downloads develVersion Github Code Size R-CMD-check Project Status: WIP lifecycle Last-changedate Zenodo DOI Website - pkgdown .

    @@ -164,9 +161,8 @@

    Installationdevtools::install_github("aravind-j/EvaluateCore")

    @@ -195,29 +191,33 @@

    CRAN checks + +Flavour +CRAN check + -r-devel-linux-x86_64-debian-clang +r-devel-linux-x86_64-debian-clang CRAN check - r-devel-linux-x86_64-debian-clang -r-devel-linux-x86_64-debian-gcc +r-devel-linux-x86_64-debian-gcc CRAN check - r-devel-linux-x86_64-debian-gcc -r-devel-linux-x86_64-fedora-clang +r-devel-linux-x86_64-fedora-clang CRAN check - r-devel-linux-x86_64-fedora-clang -r-devel-linux-x86_64-fedora-gcc +r-devel-linux-x86_64-fedora-gcc CRAN check - r-devel-linux-x86_64-fedora-gcc -r-patched-linux-x86_64 +r-patched-linux-x86_64 CRAN check - r-patched-linux-x86_64 -r-release-linux-x86_64 +r-release-linux-x86_64 CRAN check - r-release-linux-x86_64 @@ -228,17 +228,21 @@

    CRAN checks + +Flavour +CRAN check + -r-devel-windows-x86_64 +r-devel-windows-x86_64 CRAN check - r-devel-windows-x86_64 -r-release-windows-x86_64 +r-release-windows-x86_64 CRAN check - r-release-windows-x86_64 -r-oldrel-windows-x86_64 +r-oldrel-windows-x86_64 CRAN check - r-oldrel-windows-x86_64 @@ -249,13 +253,17 @@

    CRAN checks + +Flavour +CRAN check + -r-release-macos-x86_64 +r-release-macos-x86_64 CRAN check - r-release-macos-x86_64 -r-oldrel-macos-x86_64 +r-oldrel-macos-x86_64 CRAN check - r-oldrel-macos-x86_64 @@ -268,25 +276,23 @@

    Citing EvaluateCore

    To cite the methods in the package use:

     citation("EvaluateCore")
    -
    To cite the R package 'EvaluateCore' in publications use:
-
-  Aravind, J., Kaur, V., Wankhede, D. P. and Nanjundan, J. (2023).
-  EvaluateCore: Quality Evaluation of Core Collections. R package
-  version 0.1.3.9000,
-  https://aravind-j.github.io/EvaluateCore/https://CRAN.R-project.org/package=EvaluateCore.
-
-A BibTeX entry for LaTeX users is
-
-  @Manual{,
-    title = {EvaluateCore: Quality Evaluation of Core Collections},
-    author = {J. Aravind and Vikender Kaur and Dhammaprakash Pandhari Wankhede and J. Nanjundan},
-    year = {2023},
-    note = {R package version 0.1.3.9000 https://aravind-j.github.io/EvaluateCore/ https://CRAN.R-project.org/package=EvaluateCore},
-  }
-
-This free and open-source software implements academic research by the
-authors and co-workers. If you use it, please support the project by
-citing the package.
    +
    To cite the R package 'EvaluateCore' in publications use:
+
+  Aravind, J., Kaur, V., Wankhede, D. P. and Nanjundan, J. (2023).  EvaluateCore: Quality Evaluation of Core
+  Collections. R package version 0.1.3.9000,
+  https://aravind-j.github.io/EvaluateCore/https://CRAN.R-project.org/package=EvaluateCore.
+
+A BibTeX entry for LaTeX users is
+
+  @Manual{,
+    title = {EvaluateCore: Quality Evaluation of Core Collections},
+    author = {J. Aravind and Vikender Kaur and Dhammaprakash Pandhari Wankhede and J. Nanjundan},
+    year = {2023},
+    note = {R package version 0.1.3.9000 https://aravind-j.github.io/EvaluateCore/ https://CRAN.R-project.org/package=EvaluateCore},
+  }
+
+This free and open-source software implements academic research by the authors and co-workers. If you use it, please
+support the project by citing the package.

    @@ -342,16 +348,16 @@

    Developers

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/news/index.html b/news/index.html index 5533479..5f3d2d4 100644 --- a/news/index.html +++ b/news/index.html @@ -1,5 +1,5 @@ -Changelog • EvaluateCoreChangelog • EvaluateCore - +
    - +

    Changelog

    - Source: NEWS.md + Source: NEWS.md
    @@ -166,15 +166,15 @@

    Eva

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/pkgdown.yml b/pkgdown.yml index fba1db3..fbbc625 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -1,11 +1,10 @@ -pandoc: 2.19.2 -pkgdown: 2.0.7 +pandoc: 3.1.11 +pkgdown: 2.1.0 pkgdown_sha: ~ articles: EvaluationofCoreCollections: EvaluationofCoreCollections.html - Example Core Data: additional/Example Core Data.html -last_built: 2023-08-20T16:34Z + additional/Example Core Data: additional/Example Core Data.html +last_built: 2024-08-18T15:01Z urls: reference: https://aravind-j.github.io/EvaluateCore/reference article: https://aravind-j.github.io/EvaluateCore/articles - diff --git a/reference/EvaluateCore-deprecated.html b/reference/EvaluateCore-deprecated.html index 9be7fd8..feb1c2c 100644 --- a/reference/EvaluateCore-deprecated.html +++ b/reference/EvaluateCore-deprecated.html @@ -1,5 +1,5 @@ -Deprecated functions in package EvaluateCore. — EvaluateCore-deprecated • EvaluateCore
    - +

    Deprecated functions in package EvaluateCore.

    - Source: R/EvaluateCore-deprecated.R, R/shannon.evaluate.core.R + Source: R/EvaluateCore-deprecated.R, R/shannon.evaluate.core.R
    @@ -104,7 +104,7 @@

    Deprecated functions in package EvaluateCore.

    shannon.evaluate.core

    - +

    For shannon.evaluate.core, use diversity.evaluate.core.

    @@ -121,15 +121,15 @@

    shannon.evaluate.core

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

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    - +

    Bar Plots

    - Source: R/bar.evaluate.core.R + Source: R/bar.evaluate.core.R
    @@ -100,31 +100,31 @@

    Bar Plots

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A list with the ggplot objects of relative frequency bar plots +

    A list with the ggplot objects of relative frequency bar plots of CS and EC for each trait specified as qualitative.

    @@ -223,15 +223,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/box.evaluate.core-1.png b/reference/box.evaluate.core-1.png index ddb7f7c..0a4b440 100644 Binary files a/reference/box.evaluate.core-1.png and b/reference/box.evaluate.core-1.png differ diff --git a/reference/box.evaluate.core-10.png b/reference/box.evaluate.core-10.png index bc47738..c1b5c34 100644 Binary files a/reference/box.evaluate.core-10.png and b/reference/box.evaluate.core-10.png differ diff --git a/reference/box.evaluate.core-2.png b/reference/box.evaluate.core-2.png index 524fef0..3d22e11 100644 Binary files a/reference/box.evaluate.core-2.png and b/reference/box.evaluate.core-2.png differ diff --git a/reference/box.evaluate.core-3.png b/reference/box.evaluate.core-3.png index ef2f39e..4aba343 100644 Binary files a/reference/box.evaluate.core-3.png and b/reference/box.evaluate.core-3.png differ diff --git a/reference/box.evaluate.core-4.png b/reference/box.evaluate.core-4.png index e8de884..6a5cabf 100644 Binary files a/reference/box.evaluate.core-4.png and b/reference/box.evaluate.core-4.png differ diff --git a/reference/box.evaluate.core-5.png b/reference/box.evaluate.core-5.png index 7f030ad..b593c52 100644 Binary files a/reference/box.evaluate.core-5.png and b/reference/box.evaluate.core-5.png differ diff --git a/reference/box.evaluate.core-6.png b/reference/box.evaluate.core-6.png index e2584fc..2c51c25 100644 Binary files a/reference/box.evaluate.core-6.png and b/reference/box.evaluate.core-6.png differ diff --git a/reference/box.evaluate.core-7.png b/reference/box.evaluate.core-7.png index c202be0..a9c7e7e 100644 Binary files a/reference/box.evaluate.core-7.png and b/reference/box.evaluate.core-7.png differ diff --git a/reference/box.evaluate.core-8.png b/reference/box.evaluate.core-8.png index e035b42..b9f76dd 100644 Binary files a/reference/box.evaluate.core-8.png and b/reference/box.evaluate.core-8.png differ diff --git a/reference/box.evaluate.core-9.png b/reference/box.evaluate.core-9.png index 3defb39..5bfda72 100644 Binary files a/reference/box.evaluate.core-9.png and b/reference/box.evaluate.core-9.png differ diff --git a/reference/box.evaluate.core.html b/reference/box.evaluate.core.html index 5d5df07..422589b 100644 --- a/reference/box.evaluate.core.html +++ b/reference/box.evaluate.core.html @@ -1,5 +1,5 @@ -Box Plots — box.evaluate.core • EvaluateCoreBox Plots — box.evaluate.core • EvaluateCore - +
    - +

    Box Plots

    - Source: R/box.evaluate.core.R + Source: R/box.evaluate.core.R
    @@ -106,31 +106,31 @@

    Box Plots

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A list with the ggplot objects of box plots of CS and EC for +

    A list with the ggplot objects of box plots of CS and EC for each trait specified as quantitative.

    @@ -214,15 +214,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/cassava_CC.html b/reference/cassava_CC.html index 2303971..0b29e35 100644 --- a/reference/cassava_CC.html +++ b/reference/cassava_CC.html @@ -1,5 +1,5 @@ -IITA Cassava Germplasm Data - Core Collection — cassava_CC • EvaluateCoreIITA Cassava Germplasm Data - Core Collection — cassava_CC • EvaluateCore - +
    - +

    IITA Cassava Germplasm Data - Core Collection

    - Source: R/cassava_CC.R + Source: R/cassava_CC.R
    @@ -104,7 +104,7 @@

    IITA Cassava Germplasm Data - Core Collection

    (International Institute of Tropical Agriculture et al. 2019) using 10 quantitative and 48 qualitative trait data with CoreHunter3 -(corehunter). The core set was generated using +(corehunter). The core set was generated using distance based measures giving equal weightage to Average entry-to-nearest-entry distance (EN) and Average accession-to-nearest-entry distance (AN). Includes data on 26 descriptors for 168 (10 % of @@ -186,7 +186,7 @@

    Format

    per plant

    SRDM

    Storage root dry matter

    - +

    Details

    @@ -654,15 +654,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/cassava_EC.html b/reference/cassava_EC.html index 3df4e15..5c772ff 100644 --- a/reference/cassava_EC.html +++ b/reference/cassava_EC.html @@ -1,5 +1,5 @@ -IITA Cassava Germplasm Data - Entire Collection — cassava_EC • EvaluateCoreIITA Cassava Germplasm Data - Entire Collection — cassava_EC • EvaluateCore - +
    - +

    IITA Cassava Germplasm Data - Entire Collection

    - Source: R/cassava_EC.R + Source: R/cassava_EC.R
    @@ -174,7 +174,7 @@

    Format

    per plant

    SRDM

    Storage root dry matter

    - +

    Details

    @@ -650,15 +650,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/checks.evaluate.core.html b/reference/checks.evaluate.core.html index d68a4d5..5278d9c 100644 --- a/reference/checks.evaluate.core.html +++ b/reference/checks.evaluate.core.html @@ -1,5 +1,5 @@ -Common checks for all functions — checks.evaluate.core • EvaluateCoreCommon checks for all functions — checks.evaluate.core • EvaluateCore - +
    - +

    Common checks for all functions

    - Source: R/checks.evaluate.core.R + Source: R/checks.evaluate.core.R
    @@ -104,27 +104,29 @@

    Common checks for all functions

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    @@ -142,15 +144,15 @@

    Arguments

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/chisquare.evaluate.core.html b/reference/chisquare.evaluate.core.html index 395f490..547a41b 100644 --- a/reference/chisquare.evaluate.core.html +++ b/reference/chisquare.evaluate.core.html @@ -1,5 +1,5 @@ -Chi-squared Test for Homogeneity — chisquare.evaluate.core • EvaluateCoreChi-squared Test for Homogeneity — chisquare.evaluate.core • EvaluateCore - +
    - +

    Chi-squared Test for Homogeneity

    - Source: R/chisquare.evalueate.core.R + Source: R/chisquare.evalueate.core.R
    @@ -106,31 +106,31 @@

    Chi-squared Test for Homogeneity

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A a data frame with the following columns.

    +

    A a data frame with the following columns.

    Trait

    The qualitative trait.

    @@ -161,9 +161,9 @@

    Value

    References

    Pearson K (1900). “X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling.” -The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157--175.

    Snedecor G, Irwin MR (1933). +The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157–175.

    Snedecor G, Irwin MR (1933). “On the chi-square test for homogeneity.” -Iowa State College Journal of Science, 8, 75--81.

    +Iowa State College Journal of Science, 8, 75–81.

    See also

    @@ -262,21 +262,21 @@

    Examples

    #> 15 Absent(68); Present(100) #> 16 Irregular(56); Tending toward horizontal(112); Tending toward vertical(0) #> chisq_statistic chisq_pvalue chisq_significance -#> 1 0.5046947 0.96840316 ns -#> 2 5.0473183 0.08019198 ns -#> 3 25.8082829 0.00029997 ** -#> 4 24.1043807 0.00039996 ** -#> 5 3.7515039 0.36426357 ns -#> 6 15.8517808 0.01019898 * -#> 7 2.8705932 0.24557544 ns -#> 8 9.0091683 0.38566143 ns -#> 9 8.3226194 0.03869613 * -#> 10 3.9554501 0.41315868 ns -#> 11 7.2401831 0.11738826 ns -#> 12 5.0840571 0.27137286 ns -#> 13 0.6650546 0.80611939 ns -#> 14 1.6140159 0.66733327 ns -#> 15 0.3416413 0.56564344 ns +#> 1 0.5046947 0.96920308 ns +#> 2 5.0473183 0.07839216 ns +#> 3 25.8082829 0.00039996 ** +#> 4 24.1043807 0.00009999 ** +#> 5 3.7515039 0.36366363 ns +#> 6 15.8517808 0.00859914 ** +#> 7 2.8705932 0.24817518 ns +#> 8 9.0091683 0.38416158 ns +#> 9 8.3226194 0.03959604 * +#> 10 3.9554501 0.41075892 ns +#> 11 7.2401831 0.11618838 ns +#> 12 5.0840571 0.27517248 ns +#> 13 0.6650546 0.80361964 ns +#> 14 1.6140159 0.66543346 ns +#> 15 0.3416413 0.57124288 ns #> 16 0.3043632 1.00000000 ns
    @@ -293,15 +293,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/corr.evaluate.core-1.png b/reference/corr.evaluate.core-1.png index edfe644..52b4f0f 100644 Binary files a/reference/corr.evaluate.core-1.png and b/reference/corr.evaluate.core-1.png differ diff --git a/reference/corr.evaluate.core.html b/reference/corr.evaluate.core.html index 0a0da9f..bb4da71 100644 --- a/reference/corr.evaluate.core.html +++ b/reference/corr.evaluate.core.html @@ -1,5 +1,5 @@ -Phenotypic Correlations — corr.evaluate.core • EvaluateCorePhenotypic Correlations — corr.evaluate.core • EvaluateCore - +
    - +

    Phenotypic Correlations

    - Source: R/corr.evaluate.core.R + Source: R/corr.evaluate.core.R
    @@ -112,36 +112,36 @@

    Phenotypic Correlations

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A list with the following components.

    +

    A list with the following components.

    Correlation Matrix

    The matrix with phenotypic correlations between traits in EC (below diagonal) @@ -161,12 +161,12 @@

    Value

    References

    Friendly M (2002). “Corrgrams.” -The American Statistician, 56(4), 316--324.

    Legendre P, Legendre L (2012). +The American Statistician, 56(4), 316–324.

    Legendre P, Legendre L (2012). “Interpretation of ecological structures.” -In Developments in Environmental Modelling, volume 24, 521--624. +In Developments in Environmental Modelling, volume 24, 521–624. Elsevier.

    Pearson K (1895). “Note on regression and inheritance in the case of two parents.” -Proceedings of the Royal Society of London, 58, 240--242.

    +Proceedings of the Royal Society of London, 58, 240–242.

    See also

    @@ -303,15 +303,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/coverage.evaluate.core.html b/reference/coverage.evaluate.core.html index 7bb28e9..71b7c2f 100644 --- a/reference/coverage.evaluate.core.html +++ b/reference/coverage.evaluate.core.html @@ -1,5 +1,5 @@ -Class Coverage — coverage.evaluate.core • EvaluateCoreClass Coverage — coverage.evaluate.core • EvaluateCore
    - +

    Class Coverage

    - Source: R/coverage.evaluate.core.R + Source: R/coverage.evaluate.core.R
    @@ -104,31 +104,31 @@

    Class Coverage

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    The Class Coverage value.

    +

    The Class Coverage value.

    Details

    @@ -146,7 +146,7 @@

    Details

    References

    Kim K, Chung H, Cho G, Ma K, Chandrabalan D, Gwag J, Kim T, Cho E, Park Y (2007). “PowerCore: A program applying the advanced M strategy with a heuristic search for establishing core sets.” -Bioinformatics, 23(16), 2155--2162.

    +Bioinformatics, 23(16), 2155–2162.

    @@ -188,15 +188,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/cr.evaluate.core.html b/reference/cr.evaluate.core.html index d07b2b1..846f5e5 100644 --- a/reference/cr.evaluate.core.html +++ b/reference/cr.evaluate.core.html @@ -1,5 +1,5 @@ -Coincidence Rate of Range — cr.evaluate.core • EvaluateCoreCoincidence Rate of Range — cr.evaluate.core • EvaluateCore - +
    - +

    Coincidence Rate of Range

    - Source: R/cr.evaluate.core.R + Source: R/cr.evaluate.core.R
    @@ -113,31 +113,31 @@

    Coincidence Rate of Range

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    The \(CR\) value.

    +

    The \(CR\) value.

    Details

    @@ -180,11 +180,11 @@

    Note

    References

    Diwan N, McIntosh MS, Bauchan GR (1995). “Methods of developing a core collection of annual Medicago species.” -Theoretical and Applied Genetics, 90(6), 755--761.

    Hu J, Zhu J, Xu HM (2000). +Theoretical and Applied Genetics, 90(6), 755–761.

    Hu J, Zhu J, Xu HM (2000). “Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.” -Theoretical and Applied Genetics, 101(1), 264--268.

    Wang J, Hu J, Zhang C, Zhang S (2007). +Theoretical and Applied Genetics, 101(1), 264–268.

    Wang J, Hu J, Zhang C, Zhang S (2007). “Assessment on evaluating parameters of rice core collections constructed by genotypic values and molecular marker information.” -Rice Science, 14(2), 101--110.

    +Rice Science, 14(2), 101–110.

    See also

    @@ -231,15 +231,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/dist.evaluate.core.html b/reference/dist.evaluate.core.html index 9a28b9d..9164d3a 100644 --- a/reference/dist.evaluate.core.html +++ b/reference/dist.evaluate.core.html @@ -1,5 +1,5 @@ -Distance Measures — dist.evaluate.core • EvaluateCoreDistance Measures — dist.evaluate.core • EvaluateCore - +
    - +

    Distance Measures

    - Source: R/dist.evaluate.core.R + Source: R/dist.evaluate.core.R
    @@ -114,34 +114,36 @@

    Distance Measures

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    -
    d
    +
    d

    A distance matrix of class "dist" with individual names in -the names column in data as labels. If NULL (default), +the names column in data as labels. If NULL (default), then a distance matrix is computed using Gower's metric. (Gower 1971) .

    @@ -149,9 +151,7 @@

    Arguments

    Value

    - - -

    A data frame with the average values of +

    A data frame with the average values of \(E\text{-}EN\), \(E\text{-}EN\) and \(E\text{-}EN\).

    @@ -160,9 +160,9 @@

    Value

    References

    Gower JC (1971). “A general coefficient of similarity and some of its properties.” -Biometrics, 27(4), 857--871.

    Odong TL, Jansen J, van Eeuwijk FA, van Hintum TJL (2013). +Biometrics, 27(4), 857–871.

    Odong TL, Jansen J, van Eeuwijk FA, van Hintum TJL (2013). “Quality of core collections for effective utilisation of genetic resources review, discussion and interpretation.” -Theoretical and Applied Genetics, 126(2), 289--305.

    +Theoretical and Applied Genetics, 126(2), 289–305.

    See also

    @@ -240,15 +240,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/diversity.evaluate.core.html b/reference/diversity.evaluate.core.html index 1e10803..c2480ae 100644 --- a/reference/diversity.evaluate.core.html +++ b/reference/diversity.evaluate.core.html @@ -1,5 +1,5 @@ -Diversity Indices — diversity.evaluate.core • EvaluateCoreDiversity Indices — diversity.evaluate.core • EvaluateCore - +
    - +

    Diversity Indices

    - Source: R/diversity.evaluate.core.R + Source: R/diversity.evaluate.core.R
    @@ -164,40 +164,40 @@

    Diversity Indices

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    -
    base
    +
    base

    The logarithm base to be used for computation of Shannon-Weaver Diversity Index (\(I\)). Default is 2.

    -
    R
    +
    R

    The number of bootstrap replicates. Default is 1000.

    Value

    - - -

    A list with three data frames as follows.

    +

    A list with three data frames as follows.

    simpson

    EC_No.Classes

    The number of classes in the trait for EC.

    @@ -528,7 +528,7 @@

    Testing for difference with b

    References

    Berger WH, Parker FL (1970). “Diversity of planktonic foraminifera in deep-sea sediments.” -Science, 168(3937), 1345--1347.

    Gini C (1912). +Science, 168(3937), 1345–1347.

    Gini C (1912). Variabilita e Mutabilita. Contributo allo Studio delle Distribuzioni e delle Relazioni Statistiche. [Fasc. I.]. Tipogr. di P. Cuppini, Bologna.

    Gini C (1912). “Variabilita e mutabilita.” @@ -537,23 +537,23 @@

    References

    “The measurement of linguistic diversity.” Language, 32(1), 109.

    Hennink S, Zeven AC (1990). “The interpretation of Nei and Shannon-Weaver within population variation indices.” -Euphytica, 51(3), 235--240.

    Hill MO (1973). +Euphytica, 51(3), 235–240.

    Hill MO (1973). “Diversity and evenness: A unifying notation and its consequences.” -Ecology, 54(2), 427--432.

    Hutcheson K (1970). +Ecology, 54(2), 427–432.

    Hutcheson K (1970). “A test for comparing diversities based on the Shannon formula.” -Journal of Theoretical Biology, 29(1), 151--154.

    Lyons NI, Hutcheson K (1978). +Journal of Theoretical Biology, 29(1), 151–154.

    Lyons NI, Hutcheson K (1978). “C20. Comparing diversities: Gini's index.” -Journal of Statistical Computation and Simulation, 8(1), 75--78.

    McIntosh RP (1967). +Journal of Statistical Computation and Simulation, 8(1), 75–78.

    McIntosh RP (1967). “An index of diversity and the relation of certain concepts to diversity.” -Ecology, 48(3), 392--404.

    Nei M (1973). +Ecology, 48(3), 392–404.

    Nei M (1973). “Analysis of gene diversity in subdivided populations.” -Proceedings of the National Academy of Sciences, 70(12), 3321--3323.

    Peet RK (1974). +Proceedings of the National Academy of Sciences, 70(12), 3321–3323.

    Peet RK (1974). “The measurement of species diversity.” -Annual Review of Ecology and Systematics, 5(1), 285--307.

    Shannon CE, Weaver W (1949). +Annual Review of Ecology and Systematics, 5(1), 285–307.

    Shannon CE, Weaver W (1949). The Mathematical Theory of Communication, number v. 2 in The Mathematical Theory of Communication. University of Illinois Press.

    Simpson EH (1949). “Measurement of diversity.” -Nature, 163(4148), 688--688.

    Solow AR (1993). +Nature, 163(4148), 688–688.

    Solow AR (1993). “A simple test for change in community structure.” The Journal of Animal Ecology, 62(1), 191.

    Williams CB (1964). Patterns in the Balance of Nature and Related Problems in Quantitative Ecology. @@ -599,7 +599,7 @@

    Examples

    #> 6 LBTEF 6 6 0.223971174841036 0.776028825158964 #> 7 CBTR 3 3 0.51874284166756 0.48125715833244 #> 8 NMLB 10 9 0.206703725435988 0.793296274564012 -#> 9 ANGB 4 4 0.350516387291879 0.649483612708121 +#> 9 ANGB 4 4 0.350516387291879 0.64948361270812 #> 10 CUAL9M 5 5 0.309073521363567 0.690926478636433 #> 11 LVC9M 5 5 0.425299451029954 0.574700548970046 #> 12 TNPR9M 5 5 0.247165582455527 0.752834417544473 @@ -609,89 +609,89 @@

    Examples

    #> 16 PSTR 3 2 0.555306757465823 0.444693242534177 #> EC_D.max EC_D.inv EC_D.rel EC_d.V #> 1 0.8 2.63691156971154 0.775960591793095 6.2401326584027e-05 -#> 2 0.666666666666667 2.36742340128195 0.866399774882787 2.6630307524352e-05 -#> 3 0.8 2.09468826265002 0.653252492369147 6.43256463380356e-05 +#> 2 0.666666666666667 2.36742340128195 0.866399774882787 2.66303075243519e-05 +#> 3 0.8 2.09468826265002 0.653252492369147 6.43256463380354e-05 #> 4 0.8 2.84575924216273 0.810749911137942 4.49202770089222e-05 -#> 5 0.8 2.22749223165839 0.688830815104857 3.64033438958826e-05 +#> 5 0.8 2.22749223165839 0.688830815104857 3.64033438958825e-05 #> 6 0.833333333333333 4.46486026922774 0.931234590190757 1.01110395238633e-05 #> 7 0.666666666666667 1.92773744459853 0.72188573749866 4.63756492257553e-05 #> 8 0.9 4.83784217188518 0.881440305071124 1.57355754103247e-05 #> 9 0.75 2.85293366089411 0.865978150277494 3.10366375721298e-05 -#> 10 0.8 3.23547612745411 0.863658098295541 1.34905804296261e-05 +#> 10 0.8 3.23547612745411 0.863658098295541 1.34905804296262e-05 #> 11 0.8 2.35128448338761 0.718375686212558 4.98247003940663e-05 #> 12 0.8 4.04587074812462 0.941043021930592 2.56650302792209e-05 #> 13 0.666666666666667 2.005473608579 0.752047000976072 3.79031161219323e-06 -#> 14 0.75 3.10376016215672 0.903746875722886 9.34647324671546e-06 +#> 14 0.75 3.10376016215672 0.903746875722886 9.34647324671549e-06 #> 15 0.5 1.9595331707216 0.979348739851389 1.21791747807758e-05 #> 16 0.666666666666667 1.80080646697613 0.667039863801265 6.1357244865428e-05 #> EC_d.boot.V CS_d CS_D CS_D.max -#> 1 6.09528100648911e-05 0.378472222222222 0.621527777777778 0.75 -#> 2 2.6721581522594e-05 0.387755102040816 0.612244897959184 0.666666666666667 -#> 3 6.57531237081101e-05 0.421343537414966 0.578656462585034 0.8 -#> 4 4.32897698295606e-05 0.303500566893424 0.696499433106576 0.8 -#> 5 3.54012778918776e-05 0.426587301587302 0.573412698412698 0.75 -#> 6 9.93893885681588e-06 0.200538548752834 0.799461451247166 0.833333333333333 -#> 7 4.54222373683763e-05 0.487244897959184 0.512755102040816 0.666666666666667 -#> 8 1.55108875353918e-05 0.198058390022676 0.801941609977324 0.888888888888889 -#> 9 3.10410142356705e-05 0.342120181405896 0.657879818594104 0.75 -#> 10 1.41351950175709e-05 0.288052721088435 0.711947278911565 0.8 -#> 11 4.93588484057504e-05 0.388676303854875 0.611323696145125 0.8 -#> 12 2.4697787852265e-05 0.2218679138322 0.7781320861678 0.8 -#> 13 3.85851415513127e-06 0.500283446712018 0.499716553287982 0.5 -#> 14 9.54766019687746e-06 0.311862244897959 0.688137755102041 0.75 -#> 15 1.21307777942816e-05 0.518140589569161 0.481859410430839 0.5 -#> 16 6.452526895584e-05 0.555555555555556 0.444444444444444 0.5 +#> 1 6.68533450338314e-05 0.378472222222222 0.621527777777778 0.75 +#> 2 2.65879113857189e-05 0.387755102040816 0.612244897959184 0.666666666666667 +#> 3 6.50363094034534e-05 0.421343537414966 0.578656462585034 0.8 +#> 4 4.79746735208086e-05 0.303500566893424 0.696499433106576 0.8 +#> 5 3.49356184489191e-05 0.426587301587302 0.573412698412698 0.75 +#> 6 9.78416887814862e-06 0.200538548752834 0.799461451247165 0.833333333333333 +#> 7 4.76384743057889e-05 0.487244897959184 0.512755102040816 0.666666666666667 +#> 8 1.55424381336112e-05 0.198058390022676 0.801941609977324 0.888888888888889 +#> 9 3.19616815160651e-05 0.342120181405896 0.657879818594104 0.75 +#> 10 1.35390135457135e-05 0.288052721088435 0.711947278911565 0.8 +#> 11 4.73810044068571e-05 0.388676303854875 0.611323696145125 0.8 +#> 12 2.63678232813366e-05 0.2218679138322 0.7781320861678 0.8 +#> 13 3.95700024252201e-06 0.500283446712018 0.499716553287982 0.5 +#> 14 9.00891406453171e-06 0.311862244897959 0.688137755102041 0.75 +#> 15 1.11259330666434e-05 0.518140589569161 0.481859410430839 0.5 +#> 16 5.79882678523648e-05 0.555555555555556 0.444444444444444 0.5 #> CS_D.inv CS_D.rel CS_d.V CS_d.boot.V -#> 1 2.64220183486239 0.828703703703704 0.000664974207441761 0.000628561176013509 -#> 2 2.57894736842105 0.918367346938775 0.000253494320337725 0.000243977639960886 -#> 3 2.37336024217962 0.723320578231292 0.00106773305088107 0.00106243364121077 -#> 4 3.29488676161569 0.87062429138322 0.000464316667250201 0.000446585304752282 -#> 5 2.34418604651163 0.764550264550265 0.000434218289438062 0.000437508230092865 -#> 6 4.98657243816255 0.959353741496599 6.056274702847e-05 5.21238759848026e-05 -#> 7 2.05235602094241 0.769132653061224 0.000257136051752955 0.000246982675606407 -#> 8 5.04901610017889 0.90218431122449 0.000216132859442877 0.000214953672042989 -#> 9 2.92294946147473 0.877173091458806 0.000360254817691854 0.000381467045054603 -#> 10 3.47158671586716 0.889934098639456 0.000144703074224107 0.000146091118585118 -#> 11 2.57283500455789 0.764154620181406 0.000503057233986994 0.000508802487748134 -#> 12 4.50718620249122 0.972665107709751 0.000134121919501297 0.000123167203915527 -#> 13 1.99886685552408 0.999433106575964 2.11737525161229e-05 2.01829151317429e-05 -#> 14 3.20654396728016 0.917517006802721 8.69838142388246e-05 8.85698960713709e-05 -#> 15 1.92997811816193 0.963718820861678 0.00022467592663519 0.000228946748265308 -#> 16 1.8 0.888888888888889 0.000601970661850899 0.000593399485976873 +#> 1 2.64220183486239 0.828703703703704 0.000664974207441761 0.000711409006695385 +#> 2 2.57894736842105 0.918367346938775 0.000253494320337725 0.000240277035602111 +#> 3 2.37336024217962 0.723320578231292 0.00106773305088108 0.00104909427501709 +#> 4 3.29488676161569 0.87062429138322 0.000464316667250201 0.0004640806330644 +#> 5 2.34418604651163 0.764550264550265 0.000434218289438062 0.000428639235160984 +#> 6 4.98657243816254 0.959353741496599 6.05627470284696e-05 6.05634678928432e-05 +#> 7 2.05235602094241 0.769132653061224 0.000257136051752956 0.000271330248841789 +#> 8 5.04901610017889 0.90218431122449 0.000216132859442877 0.000204816497715932 +#> 9 2.92294946147473 0.877173091458806 0.000360254817691854 0.0003537099748542 +#> 10 3.47158671586716 0.889934098639456 0.000144703074224106 0.000152475008559194 +#> 11 2.57283500455789 0.764154620181406 0.000503057233986995 0.000488621895795101 +#> 12 4.50718620249122 0.972665107709751 0.000134121919501297 0.000143869065469617 +#> 13 1.99886685552408 0.999433106575964 2.11737525161229e-05 1.4057939053243e-05 +#> 14 3.20654396728016 0.917517006802721 8.69838142388246e-05 8.64975280349916e-05 +#> 15 1.92997811816193 0.963718820861678 0.00022467592663519 0.000201749293623147 +#> 16 1.8 0.888888888888889 0.000601970661850899 0.000601085481209453 #> d.t.df d.t.stat d.t.pvalue d.t.significance d.boot.z.df #> 1 7 0.02815376878219 0.978325351754391 ns 7 -#> 2 4 2.06997700767548 0.10723294813912 ns 4 -#> 3 8 1.66600295615056 0.134274950280504 ns 8 +#> 2 4 2.06997700767549 0.10723294813912 ns 4 +#> 3 8 1.66600295615057 0.134274950280504 ns 8 #> 4 8 2.12261420536943 0.0665538249488766 ns 8 -#> 5 7 1.0301574783912 0.337209790540496 ns 7 -#> 6 10 2.78735177789004 0.0192068432217536 * 10 +#> 5 7 1.0301574783912 0.337209790540494 ns 7 +#> 6 10 2.78735177789005 0.0192068432217533 * 10 #> 7 4 1.80798358527422 0.144886591272101 ns 4 -#> 8 17 0.567755084270466 0.577623407084811 ns 17 -#> 9 6 0.424456215383701 0.686031514328125 ns 6 +#> 8 17 0.567755084270463 0.577623407084813 ns 17 +#> 9 6 0.424456215383704 0.686031514328123 ns 6 #> 10 8 1.6713011659847 0.133206249679844 ns 8 #> 11 8 1.55754090990284 0.157957033254601 ns 8 -#> 12 8 2.00128917457502 0.0803555866511262 ns 8 +#> 12 8 2.00128917457502 0.0803555866511265 ns 8 #> 13 3 -0.329859967245677 0.763196306139146 ns 3 #> 14 6 1.05224751693921 0.333206278005286 ns 6 #> 15 2 -0.507791421657648 0.662061546648583 ns 2 #> 16 3 -0.00966012858117729 0.99289893656246 ns 3 #> d.boot.z.stat d.boot.z.pvalue d.boot.z.significance -#> 1 0.0289584132923613 0.977706077873199 ns -#> 2 2.1163729821146 0.101756937254872 ns -#> 3 1.65338392267009 0.136852265387204 ns -#> 4 2.18188795717211 0.0606836003806744 ns -#> 5 1.04226041244695 0.331943945102461 ns -#> 6 2.93610796496096 0.0148843333523579 * -#> 7 1.85318858658238 0.137480653366135 ns -#> 8 0.573880962872739 0.573563481068977 ns -#> 9 0.408977538685226 0.696753801568227 ns -#> 10 1.64540732742066 0.1385048303125 ns -#> 11 1.55077922892721 0.159552249143758 ns -#> 12 2.03668291207062 0.0760644040514377 ns -#> 13 0.333046049072763 0.761016756537753 ns -#> 14 1.0576600026718 0.330922348593608 ns -#> 15 0.509622606200687 0.660982723244549 ns -#> 16 0.00973633715758349 0.992842918754727 ns +#> 1 0.0276014468259671 0.97875044140037 ns +#> 2 2.11134701701347 0.10233493680879 ns +#> 3 1.67600791343304 0.132263448642767 ns +#> 4 2.12071979935888 0.0667503673247065 ns +#> 5 1.03756609320658 0.333978621629479 ns +#> 6 2.77065034924187 0.0197652293840094 * +#> 7 1.76028327597653 0.153166166195353 ns +#> 8 0.577008613944405 0.571496330602201 ns +#> 9 0.425217529507766 0.685506175360378 ns +#> 10 1.63791870182241 0.140072980275729 ns +#> 11 1.58556233614025 0.151499007860614 ns +#> 12 1.93374355649653 0.0892037747747804 ns +#> 13 0.39244722767569 0.72094875991824 ns +#> 14 1.03972126692463 0.338541348748645 ns +#> 15 0.54146070930643 0.64244084391876 ns +#> 16 0.00959298293692684 0.992948292625 ns #> #> $shannon #> Trait EC_No.Classes CS_No.Classes EC_I EC_I.max @@ -706,79 +706,79 @@

    Examples

    #> 9 ANGB 4 4 1.63181278826841 1.38629436111989 #> 10 CUAL9M 5 5 1.81895733334165 1.6094379124341 #> 11 LVC9M 5 5 1.45781261313429 1.6094379124341 -#> 12 TNPR9M 5 5 2.15936383275214 1.6094379124341 +#> 12 TNPR9M 5 5 2.15936383275215 1.6094379124341 #> 13 PL9M 3 2 1.02393231699601 1.09861228866811 #> 14 STRP 4 4 1.70471635517281 1.38629436111989 #> 15 STRC 2 2 0.985051563686417 0.693147180559945 #> 16 PSTR 3 2 0.932902628052214 1.09861228866811 #> EC_I.rel EC_I.V EC_I.boot.V -#> 1 0.994869227718287 0.000495550546246209 0.000500478256462913 -#> 2 1.22552873323805 0.000266837142793751 0.00027760991843949 -#> 3 0.806231732681647 0.00076741293709652 0.000764495155907523 -#> 4 1.08868934988106 0.000644551217715297 0.000645926018044173 -#> 5 0.820289054412 0.0005480962422548 0.000582100703818462 -#> 6 1.26193231630002 0.000259759685726472 0.000264986010904686 -#> 7 0.955211987086096 0.000312696844542425 0.000327851998328458 -#> 8 1.06855402234261 0.000496178249738945 0.000498321779180061 -#> 9 1.17710410864701 0.000306008701151613 0.000297748881794837 -#> 10 1.13018173567856 0.000397228605602063 0.000382460904189658 -#> 11 0.90578990458197 0.000698631874341118 0.000708290605282677 -#> 12 1.34168818571345 0.000264448274894998 0.000262503018773464 -#> 13 0.932023360340673 0.00010004153594366 0.000106621186189839 -#> 14 1.22969291586506 0.000219120350395739 0.000212521582419488 -#> 15 1.42112900595031 2.53468656861686e-05 2.60518261149287e-05 -#> 16 0.849164566676391 0.000206905486943159 0.000214596362847361 +#> 1 0.994869227718287 0.000495550546246209 0.000483808510194458 +#> 2 1.22552873323805 0.00026683714279372 0.000269948802236365 +#> 3 0.806231732681647 0.00076741293709653 0.000765752404583755 +#> 4 1.08868934988106 0.000644551217715297 0.000699283275380162 +#> 5 0.820289054412 0.0005480962422548 0.000517842713945963 +#> 6 1.26193231630002 0.000259759685726472 0.000271033385039332 +#> 7 0.955211987086096 0.000312696844542415 0.000303798121154971 +#> 8 1.06855402234261 0.000496178249738945 0.000505626109031182 +#> 9 1.17710410864701 0.000306008701151613 0.00030810953251426 +#> 10 1.13018173567856 0.000397228605602063 0.000382025104334203 +#> 11 0.90578990458197 0.000698631874341118 0.000665418478253527 +#> 12 1.34168818571345 0.000264448274894998 0.000279879360986154 +#> 13 0.932023360340673 0.000100041535943681 0.000101884272024935 +#> 14 1.22969291586506 0.00021912035039574 0.000213652548557445 +#> 15 1.42112900595031 2.53468656861686e-05 2.75676958917147e-05 +#> 16 0.849164566676391 0.000206905486943159 0.000215549640442331 #> CS_I CS_I.max CS_I.rel CS_I.V #> 1 1.60715392433211 1.38629436111989 1.15931649828959 0.00466900952090494 #> 2 1.44881563572518 1.09861228866811 1.31876882378736 0.00183098114336171 -#> 3 1.60685189446012 1.6094379124341 0.998393216691366 0.009152696661561 +#> 3 1.60685189446012 1.6094379124341 0.998393216691366 0.00915269666156106 #> 4 1.94652505102181 1.6094379124341 1.20944401519528 0.00501290672204481 #> 5 1.39390699808299 1.38629436111989 1.00549135679738 0.00481441616742965 -#> 6 2.3831722498942 1.79175946922805 1.330073757568 0.00172264312694068 +#> 6 2.3831722498942 1.79175946922805 1.330073757568 0.00172264312694065 #> 7 1.12338192727968 1.09861228866811 1.02254629669362 0.00290287113955504 -#> 8 2.56080478714959 2.19722457733622 1.16547248449867 0.00546163553548546 +#> 8 2.56080478714959 2.19722457733622 1.16547248449867 0.00546163553548553 #> 9 1.69700114892879 1.38629436111989 1.22412757097122 0.00354899161566695 -#> 10 1.92688046691671 1.6094379124341 1.19723814881589 0.00380714696318462 +#> 10 1.92688046691671 1.6094379124341 1.19723814881589 0.00380714696318465 #> 11 1.59702066650884 1.6094379124341 0.992284731315619 0.00676974620846264 #> 12 2.24730159981114 1.6094379124341 1.39632699245436 0.00129552549169971 #> 13 0.999591034189098 0.693147180559945 1.44210502794168 7.02197242512667e-06 -#> 14 1.74920954558179 1.38629436111989 1.26178796844324 0.00208634675528074 +#> 14 1.74920954558179 1.38629436111989 1.26178796844324 0.0020863467552808 #> 15 0.97366806454962 0.693147180559945 1.40470608819769 0.000443961107972778 #> 16 0.91829583405449 0.693147180559945 1.32482084585941 0.00132275132275134 #> CS_I.boot.V I.t.stat I.t.df I.t.pvalue -#> 1 0.00480562376552929 -0.083123660050637 205.323489838581 0.933834195011031 -#> 2 0.00190389717964702 -2.2364706637494 220.068588438014 0.026324103457891 -#> 3 0.00975436742663595 -3.10514822627187 197.214845421711 0.00218182272218793 -#> 4 0.00504134163483005 -2.58385107753543 213.627424864319 0.0104369229742795 -#> 5 0.00486598368938334 -1.00646624014807 208.16009186019 0.315359849464886 -#> 6 0.00189962243957371 -2.74217439229001 221.982338302533 0.00660128212793436 -#> 7 0.00283263942725677 -1.30452382793487 205.904913192671 0.193511253079245 -#> 8 0.00572492849237465 -1.30032759187611 199.746997945695 0.194987041994505 -#> 9 0.00327400506431514 -1.04992396937323 198.073409342984 0.295032389672131 -#> 10 0.00406495217186986 -1.66442333074281 204.6640656715 0.0975580697314574 -#> 11 0.00749230319979225 -1.61083557606938 204.247107694347 0.108759859193218 -#> 12 0.00153272490926273 -2.22646968440542 242.577447747539 0.0269016983557458 -#> 13 5.4428106092502e-05 2.35246259571945 1837.93308927512 0.0187545059227447 -#> 14 0.00213209754585181 -0.92664648595754 204.91630470168 0.35520019436459 -#> 15 0.000518758648853524 0.52546865211132 187.669671190779 0.599877733009115 -#> 16 0.00130914624498079 0.373471875118419 224.120762900574 0.709150296735107 +#> 1 0.0047929123394758 -0.083123660050637 205.323489838581 0.933834195011031 +#> 2 0.00187875640207067 -2.23647066374941 220.068588438007 0.0263241034578903 +#> 3 0.00931911218476163 -3.10514822627186 197.214845421711 0.00218182272218803 +#> 4 0.00517797969455639 -2.58385107753543 213.627424864319 0.0104369229742795 +#> 5 0.00488761731418891 -1.00646624014807 208.16009186019 0.315359849464886 +#> 6 0.00187862560896072 -2.74217439229004 221.982338302534 0.00660128212793374 +#> 7 0.00298070544341396 -1.30452382793487 205.90491319267 0.193511253079244 +#> 8 0.00547694938695042 -1.3003275918761 199.746997945695 0.194987041994507 +#> 9 0.00362924040839815 -1.04992396937323 198.073409342984 0.295032389672131 +#> 10 0.00434669657011922 -1.66442333074281 204.664065671499 0.097558069731458 +#> 11 0.00669021208688097 -1.61083557606938 204.247107694347 0.108759859193219 +#> 12 0.0013166833981556 -2.22646968440541 242.577447747539 0.0269016983557465 +#> 13 4.24533303750304e-05 2.35246259571922 1837.93308927511 0.0187545059227561 +#> 14 0.00212088460832853 -0.926646485957522 204.916304701679 0.3552001943646 +#> 15 0.000438689236849775 0.52546865211132 187.669671190779 0.599877733009115 +#> 16 0.00146139768748281 0.373471875118422 224.120762900574 0.709150296735105 #> I.t.significance I.boot.z.df I.boot.z.stat I.boot.z.pvalue -#> 1 ns 7 0.0814155185672253 0.937390479125064 -#> 2 * 4 2.17012625279399 0.0957965847666416 -#> 3 ** 8 2.99598875336569 0.0171763823718015 -#> 4 * 8 2.54946869261093 0.0342029368526513 -#> 5 ns 7 1.01498007480015 0.343905985513691 -#> 6 ** 10 2.62328519827353 0.0254538046535547 -#> 7 ns 4 1.33161582845932 0.253802403771567 -#> 8 ns 17 1.27580057379954 0.219183344785758 -#> 9 ns 6 1.09349739209037 0.316122036476727 -#> 10 ns 8 1.61679274885384 0.144585066218383 -#> 11 ns 8 1.55127315842358 0.159435230393212 -#> 12 * 8 2.09002282647133 0.0700155886847151 -#> 13 * 3 1.98549502296823 0.141300749145812 -#> 14 ns 6 0.923523546884622 0.391361319789632 -#> 15 ns 2 0.491026342566424 0.672000253243908 -#> 16 ns 3 0.373450651537337 0.733641766498944 +#> 1 ns 7 0.0817336348245371 0.937146464242981 +#> 2 * 4 2.2064267784542 0.0919914613051522 +#> 3 ** 8 3.08665470359158 0.0149652109934245 +#> 4 * 8 2.57117798516249 0.0330669187074517 +#> 5 ns 7 1.01631020857175 0.343315003404297 +#> 6 ** 10 2.66359626201418 0.0237522092011334 +#> 7 ns 4 1.28540612042092 0.268024804385408 +#> 8 ns 17 1.2958056339159 0.212360842372221 +#> 9 ns 6 1.04598381571325 0.335865437056982 +#> 10 ns 8 1.5855588008592 0.151499807281787 +#> 11 ns 8 1.60319190075875 0.147559909191939 +#> 12 * 8 2.20200971458306 0.0588093099330856 +#> 13 * 3 1.98106680901202 0.141910185247116 +#> 14 ns 6 0.935780231278676 0.385503284430038 +#> 15 ns 2 0.532654452382285 0.647528455859605 +#> 16 ns 3 0.355800409404779 0.745538558892587 #> I.boot.z.significance #> 1 ns #> 2 ns @@ -816,22 +816,22 @@

    Examples

    #> 15 STRC 2 2 0.292763414762376 0.303603702882552 #> 16 PSTR 3 2 0.261175379595452 0.275932675604997 #> M.boot.z.stat M.boot.z.df M.boot.z.pvalue M.boot.z.significance -#> 1 1.02756937825155 7 0.338344343021771 ns -#> 2 3.79496673324823 4 0.0191866065531044 * -#> 3 2.26112093496701 8 0.0536284663965344 ns -#> 4 3.2531239293837 8 0.0116464944450465 * -#> 5 2.11612518546809 7 0.0721330472553692 ns -#> 6 5.95100597878787 10 0.000141051247321563 ** -#> 7 3.02275972535326 4 0.0390571880158876 * -#> 8 2.48225397264992 17 0.0237957435130873 * -#> 9 1.74938059198107 6 0.130802588581816 ns -#> 10 3.84248119824936 8 0.00492980715298131 ** -#> 11 2.54466644076434 8 0.034459548440612 * -#> 12 4.14376457763053 8 0.00323658746729461 ** -#> 13 4.23727436917642 3 0.0240618809490489 * -#> 14 3.94919457545945 6 0.00754460565013074 ** -#> 15 0.97452643366258 2 0.43258009161777 ns -#> 16 0.854260984884811 3 0.45574973148037 ns +#> 1 1.00391168633292 7 0.348854575801086 ns +#> 2 3.39520672163674 4 0.0273980949692247 * +#> 3 2.28323260882762 8 0.051809250272037 ns +#> 4 3.3110316235721 8 0.0106817822611812 * +#> 5 2.15395666678097 7 0.0682139277106039 ns +#> 6 5.96821850593738 10 0.000137837562706689 ** +#> 7 2.99754729409007 4 0.0400387096312927 * +#> 8 2.42994156651831 17 0.0264706618143546 * +#> 9 1.80568141934553 6 0.12099659678039 ns +#> 10 3.8867793107122 8 0.00463023359089327 ** +#> 11 2.53855378664644 8 0.0347890152183959 * +#> 12 4.24645671583821 8 0.00281266593084537 ** +#> 13 4.42355476586344 3 0.0214543888693413 * +#> 14 3.81879071089912 6 0.00877307594548063 ** +#> 15 0.913484492624288 2 0.457416629856141 ns +#> 16 0.8043452846734 3 0.480033356696624 ns #> # } @@ -849,15 +849,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

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    - +

    Frequency Distribution Histogram

    - Source: R/freqdist.evaluate.core.R + Source: R/freqdist.evaluate.core.R
    @@ -112,47 +112,49 @@

    Frequency Distribution Histogram

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    -
    highlight
    +
    highlight

    Individual names to be highlighted as a character vector.

    -
    include.highlight
    +
    include.highlight

    If TRUE, the highlighted individuals are included in the frequency distribution histogram. Default is TRUE.

    -
    highlight.se
    +
    highlight.se

    Optional data frame of standard errors for the individuals specified in highlight. It should have the same column names as in data.

    -
    highlight.col
    +
    highlight.col

    The colour(s) to be used to highlighting individuals in the plot as a character vector of the same length as highlight. Must be valid colour values in R (named colours, hexadecimal representation, @@ -161,12 +163,9 @@

    Arguments

    Value

    - - -

    A list with the ggplot objects of stacked frequency - distribution histograms plots for each trait specified as

    -

    -

    quantitative and qualitative.

    +

    A list with the ggplot objects of stacked frequency + distribution histograms plots for each trait specified as + quantitative and qualitative.

    See also

    @@ -569,15 +568,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/index.html b/reference/index.html index f0f2c4b..19372ac 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,5 +1,5 @@ -Function reference • EvaluateCorePackage index • EvaluateCore - +
    - +
    @@ -235,15 +235,15 @@

    Deprecated
    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/iqr.evaluate.core.html b/reference/iqr.evaluate.core.html index 4086f34..3bfd053 100644 --- a/reference/iqr.evaluate.core.html +++ b/reference/iqr.evaluate.core.html @@ -1,5 +1,5 @@ -Interquartile Range — iqr.evaluate.core • EvaluateCoreInterquartile Range — iqr.evaluate.core • EvaluateCore
    - +

    Interquartile Range

    - Source: R/iqr.evaluate.core.R + Source: R/iqr.evaluate.core.R
    @@ -104,31 +104,31 @@

    Interquartile Range

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A data frame with the IQR values of the EC and CS for the traits +

    A data frame with the IQR values of the EC and CS for the traits specified as quantitative.

    @@ -166,17 +166,17 @@

    Examples

    iqr.evaluate.core(data = ec, names = "genotypes", quantitative = quant, selected = core) -#> Trait EC_IQR CS_IQR -#> NMSR NMSR 10.000000 9.250000 -#> TTRN TTRN 2.500000 2.666667 -#> TFWSR TFWSR 4.800000 5.550000 -#> TTRW TTRW 1.500000 2.266667 -#> TFWSS TFWSS 7.400000 8.300000 -#> TTSW TTSW 2.200000 2.837500 -#> TTPW TTPW 11.250000 13.650000 -#> AVPW AVPW 3.470833 5.110000 -#> ARSR ARSR 3.000000 3.000000 -#> SRDM SRDM 6.000000 4.625000 +#> Trait EC_IQR CS_IQR +#> 1 NMSR 10.000000 9.250000 +#> 2 TTRN 2.500000 2.666667 +#> 3 TFWSR 4.800000 5.550000 +#> 4 TTRW 1.500000 2.266667 +#> 5 TFWSS 7.400000 8.300000 +#> 6 TTSW 2.200000 2.837500 +#> 7 TTPW 11.250000 13.650000 +#> 8 AVPW 3.470833 5.110000 +#> 9 ARSR 3.000000 3.000000 +#> 10 SRDM 6.000000 4.625000
    @@ -192,15 +192,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/levene.evaluate.core.html b/reference/levene.evaluate.core.html index 24c4a10..8a206de 100644 --- a/reference/levene.evaluate.core.html +++ b/reference/levene.evaluate.core.html @@ -1,5 +1,5 @@ -Levene's Test — levene.evaluate.core • EvaluateCoreLevene's Test — levene.evaluate.core • EvaluateCore
    - +

    Levene's Test

    - Source: R/levene.evaluate.core.R + Source: R/levene.evaluate.core.R
    @@ -104,31 +104,31 @@

    Levene's Test

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A data frame with the following columns

    +

    A data frame with the following columns

    Trait

    The quantitative trait.

    @@ -158,7 +158,7 @@

    Value

    References

    Levene H (1960). “Robust tests for equality of variances.” -In Olkin I, Ghurye SG, Hoeffding W, Madow WG, Mann HB (eds.), Contribution to Probability and Statistics: Essays in Honor of Harold Hotelling, 278--292. +In Olkin I, Ghurye SG, Hoeffding W, Madow WG, Mann HB (eds.), Contribution to Probability and Statistics: Essays in Honor of Harold Hotelling, 278–292. Stanford University Press, Palo Alto, CA.

    @@ -226,15 +226,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/pca.evaluate.core-1.png b/reference/pca.evaluate.core-1.png index 68b5a0a..4dc8e40 100644 Binary files a/reference/pca.evaluate.core-1.png and b/reference/pca.evaluate.core-1.png differ diff --git a/reference/pca.evaluate.core.html b/reference/pca.evaluate.core.html index 800fe66..073bfe1 100644 --- a/reference/pca.evaluate.core.html +++ b/reference/pca.evaluate.core.html @@ -1,5 +1,5 @@ -Principal Component Analysis — pca.evaluate.core • EvaluateCorePrincipal Component Analysis — pca.evaluate.core • EvaluateCore - +
    - +

    Principal Component Analysis

    - Source: R/pca.evaluate.core.R + Source: R/pca.evaluate.core.R
    @@ -114,48 +114,48 @@

    Principal Component Analysis

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    -
    center
    +
    center

    either a logical value or numeric-alike vector of length equal to the number of columns of x, where ‘numeric-alike’ means that as.numeric(.) will be applied successfully if is.numeric(.) is not true.

    -
    scale
    +
    scale

    either a logical value or a numeric-alike vector of length equal to the number of columns of x.

    -
    npc.plot
    +
    npc.plot

    The number of principal components for which eigen values are to be plotted. The default value is 6.

    Value

    - - -

    A list with the following components.

    +

    A list with the following components.

    EC PC Importance

    A data frame of importance of principal components for EC

    @@ -219,10 +219,10 @@

    Examples

    #> Standard deviation 2.303455 1.237223 0.996322 0.9455592 0.8817511 0.6251589 #> Proportion of Variance 0.530590 0.153070 0.099270 0.0894100 0.0777500 0.0390800 #> Cumulative Proportion 0.530590 0.683660 0.782930 0.8723400 0.9500900 0.9891700 -#> PC7 PC8 PC9 PC10 -#> Standard deviation 0.271212 0.1864651 1.549134e-15 1.20846e-15 -#> Proportion of Variance 0.007360 0.0034800 0.000000e+00 0.00000e+00 -#> Cumulative Proportion 0.996520 1.0000000 1.000000e+00 1.00000e+00 +#> PC7 PC8 PC9 PC10 +#> Standard deviation 0.271212 0.1864651 4.033635e-15 1.338255e-15 +#> Proportion of Variance 0.007360 0.0034800 0.000000e+00 0.000000e+00 +#> Cumulative Proportion 0.996520 1.0000000 1.000000e+00 1.000000e+00 #> #> $`EC PC Loadings` #> PC1 PC2 PC3 PC4 PC5 PC6 @@ -237,16 +237,16 @@

    Examples

    #> ARSR 0.011265368 0.52673026 0.37858111 0.5081543 -0.444763915 0.34913640 #> SRDM -0.004586239 0.21186429 -0.89638345 0.3837654 -0.027148446 0.05797814 #> PC7 PC8 PC9 PC10 -#> NMSR -0.66616815 0.181386616 2.716790e-16 2.045788e-15 -#> TTRN 0.49417918 -0.095026370 -1.567310e-15 -1.169297e-15 -#> TFWSR 0.32960316 0.499641762 -2.002707e-01 3.096651e-01 -#> TTRW -0.31608453 -0.416500300 3.273437e-01 2.117040e-01 -#> TFWSS 0.10285083 -0.558642359 -2.603566e-01 4.025719e-01 -#> TTSW -0.07640644 0.454657280 3.955273e-01 2.558006e-01 -#> TTPW 0.21455970 -0.104941479 4.324565e-01 -6.686784e-01 -#> AVPW -0.20120313 0.065453705 -6.644485e-01 -4.297208e-01 -#> ARSR 0.01979215 -0.027945977 -5.836779e-18 7.484100e-17 -#> SRDM 0.01152032 -0.009060183 -7.744396e-17 -8.136954e-17 +#> NMSR 0.66616815 0.181386616 -1.645650e-15 7.445343e-16 +#> TTRN -0.49417918 -0.095026370 3.536674e-16 -1.428385e-15 +#> TFWSR -0.32960316 0.499641762 -3.419350e-01 -1.381350e-01 +#> TTRW 0.31608453 -0.416500300 -1.460210e-01 3.614559e-01 +#> TFWSS -0.10285083 -0.558642359 -4.445235e-01 -1.795787e-01 +#> TTSW 0.07640644 0.454657280 -1.764363e-01 4.367448e-01 +#> TTPW -0.21455970 -0.104941479 7.383608e-01 2.982832e-01 +#> AVPW 0.20120313 0.065453705 2.963963e-01 -7.336900e-01 +#> ARSR -0.01979215 -0.027945977 -1.084265e-16 5.583470e-18 +#> SRDM -0.01152032 -0.009060183 -1.189926e-16 -1.143250e-16 #> #> $`CS PC Importance` #> PC1 PC2 PC3 PC4 PC5 PC6 @@ -254,7 +254,7 @@

    Examples

    #> Proportion of Variance 0.520270 0.172200 0.100090 0.0802700 0.0714300 0.0427500 #> Cumulative Proportion 0.520270 0.692480 0.792570 0.8728400 0.9442600 0.9870100 #> PC7 PC8 PC9 PC10 -#> Standard deviation 0.3207286 0.1643265 8.218513e-16 3.899579e-16 +#> Standard deviation 0.3207286 0.1643265 1.032366e-15 3.669511e-16 #> Proportion of Variance 0.0102900 0.0027000 0.000000e+00 0.000000e+00 #> Cumulative Proportion 0.9973000 1.0000000 1.000000e+00 1.000000e+00 #> @@ -271,16 +271,16 @@

    Examples

    #> ARSR 0.02642625 0.53641424 0.049592980 -0.54225171 -0.54787309 0.334180984 #> SRDM -0.06233279 0.14455341 0.931570561 -0.04658879 0.31551819 0.073690249 #> PC7 PC8 PC9 PC10 -#> NMSR 0.69455927 -0.089542525 -5.321232e-16 1.466265e-16 -#> TTRN -0.49373454 0.026538022 5.590506e-16 -3.306053e-16 -#> TFWSR -0.24125034 -0.553541017 8.413770e-03 3.862264e-01 -#> TTRW 0.26321979 0.434887362 -4.150225e-01 9.041080e-03 -#> TFWSS -0.17717813 0.557775840 1.001223e-02 4.596024e-01 -#> TTSW 0.13648832 -0.424831173 -4.477452e-01 9.753929e-03 -#> TTPW -0.21841109 0.053240284 -1.741566e-02 -7.994501e-01 -#> AVPW 0.21517212 -0.012287406 7.917109e-01 -1.724707e-02 -#> ARSR -0.04644065 0.032656495 -9.717546e-17 -1.548356e-16 -#> SRDM -0.01212024 0.009789162 5.912017e-17 1.088709e-16 +#> NMSR 0.69455927 0.089542525 -5.893990e-16 2.361951e-17 +#> TTRN -0.49373454 -0.026538022 7.853091e-16 -3.234060e-16 +#> TFWSR -0.24125034 0.553541017 3.409506e-02 3.848106e-01 +#> TTRW 0.26321979 -0.434887362 -4.135011e-01 3.663710e-02 +#> TFWSS -0.17717813 -0.557775840 4.057250e-02 4.579176e-01 +#> TTSW 0.13648832 0.424831173 -4.461038e-01 3.952578e-02 +#> TTPW -0.21841109 -0.053240284 -7.057336e-02 -7.965194e-01 +#> AVPW 0.21517212 0.012287406 7.888086e-01 -6.989017e-02 +#> ARSR -0.04644065 -0.032656495 2.874973e-17 -6.847934e-17 +#> SRDM -0.01212024 -0.009789162 5.367396e-17 1.281538e-18 #> #> $`Scree Plot` @@ -303,15 +303,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/pdfdist.evaluate.core.html b/reference/pdfdist.evaluate.core.html index e813903..d20134b 100644 --- a/reference/pdfdist.evaluate.core.html +++ b/reference/pdfdist.evaluate.core.html @@ -1,5 +1,5 @@ -Distance Between Probability Distributions — pdfdist.evaluate.core • EvaluateCoreDistance Between Probability Distributions — pdfdist.evaluate.core • EvaluateCore - +
    - +

    Distance Between Probability Distributions

    - Source: R/pdfdist.evaluate.core.R + Source: R/pdfdist.evaluate.core.R
    @@ -116,31 +116,31 @@

    Distance Between Probability Distributions

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A data frame with the following columns.

    +

    A data frame with the following columns.

    Trait

    The quantitative trait.

    @@ -251,15 +251,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/percentdiff.evaluate.core.html b/reference/percentdiff.evaluate.core.html index fb2f024..861cdfb 100644 --- a/reference/percentdiff.evaluate.core.html +++ b/reference/percentdiff.evaluate.core.html @@ -1,5 +1,5 @@ -Percentage Difference of Means and Variances — percentdiff.evaluate.core • EvaluateCorePercentage Difference of Means and Variances — percentdiff.evaluate.core • EvaluateCore - +
    - +

    Percentage Difference of Means and Variances

    - Source: R/percentdiff.evaluate.core.R + Source: R/percentdiff.evaluate.core.R
    @@ -150,50 +150,45 @@

    Percentage Difference of Means and Variances

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    -
    alpha
    +
    alpha

    Type I error probability (Significance level) of difference.

    -
    rr.crit
    +
    rr.crit

    The critical value of range ratio considered to be acceptable for a representative CS. The default value is 0.7.

    Value

    - - -

    A data frame with the values of

    -

    -

    \(MD\%_{Hu}\),

    -

    -

    \(VD\%_{Hu}\),

    -

    -

    \(MD\%_{Kim}\),

    -

    -

    \(VD\%_{Kim}\) and

    -

    -

    \(\overline{d}D\%\).

    +

    A data frame with the values of + \(MD\%_{Hu}\), + \(VD\%_{Hu}\), + \(MD\%_{Kim}\), + \(VD\%_{Kim}\) and + \(\overline{d}D\%\).

    Details

    @@ -232,8 +227,7 @@

    Details

    \[RR\%_{0.7} = \left ( \frac{S_{RR_{0.7}}}{n} \right ) \times 100\]

    Where, \(S_{RR_{0.7}}\) is the number of traits with a range ratio -smaller than 0.7 (\(\frac{R_{CS_{i}}}{R_{EC_{i}}} < 0.7\)) -\(R_{CS_{i}}\) is the range of the \(i\)th trait in the CS, +smaller than 0.7 (\(\frac{R_{CS_{i}}}{R_{EC_{i}}} \(R_{CS_{i}}\) is the range of the \(i\)th trait in the CS, \(R_{EC_{i}}\) is the range of the \(i\)th trait in the EC and \(n\) is the total number of traits.

    @@ -241,13 +235,13 @@

    Details

    References

    Diwan N, McIntosh MS, Bauchan GR (1995). “Methods of developing a core collection of annual Medicago species.” -Theoretical and Applied Genetics, 90(6), 755--761.

    Hu J, Zhu J, Xu HM (2000). +Theoretical and Applied Genetics, 90(6), 755–761.

    Hu J, Zhu J, Xu HM (2000). “Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.” -Theoretical and Applied Genetics, 101(1), 264--268.

    Kim K, Chung H, Cho G, Ma K, Chandrabalan D, Gwag J, Kim T, Cho E, Park Y (2007). +Theoretical and Applied Genetics, 101(1), 264–268.

    Kim K, Chung H, Cho G, Ma K, Chandrabalan D, Gwag J, Kim T, Cho E, Park Y (2007). “PowerCore: A program applying the advanced M strategy with a heuristic search for establishing core sets.” -Bioinformatics, 23(16), 2155--2162.

    Studnicki M, Madry W, Schmidt J (2013). +Bioinformatics, 23(16), 2155–2162.

    Studnicki M, Madry W, Schmidt J (2013). “Comparing the efficiency of sampling strategies to establish a representative in the phenotypic-based genetic diversity core collection of orchardgrass (Dactylis glomerata L.).” -Czech Journal of Genetics and Plant Breeding, 49(1), 36--47.

    +Czech Journal of Genetics and Plant Breeding, 49(1), 36–47.

    See also

    @@ -295,15 +289,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

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diff --git a/reference/qq.evaluate.core-4.png b/reference/qq.evaluate.core-4.png index b762a9c..e5a4672 100644 Binary files a/reference/qq.evaluate.core-4.png and b/reference/qq.evaluate.core-4.png differ diff --git a/reference/qq.evaluate.core-40.png b/reference/qq.evaluate.core-40.png index 91f33ca..db943a1 100644 Binary files a/reference/qq.evaluate.core-40.png and b/reference/qq.evaluate.core-40.png differ diff --git a/reference/qq.evaluate.core-5.png b/reference/qq.evaluate.core-5.png index a73f044..88b5981 100644 Binary files a/reference/qq.evaluate.core-5.png and b/reference/qq.evaluate.core-5.png differ diff --git a/reference/qq.evaluate.core-6.png b/reference/qq.evaluate.core-6.png index 98f29ec..dc7a221 100644 Binary files a/reference/qq.evaluate.core-6.png and b/reference/qq.evaluate.core-6.png differ diff --git a/reference/qq.evaluate.core-7.png b/reference/qq.evaluate.core-7.png index c746c14..c391625 100644 Binary files a/reference/qq.evaluate.core-7.png and b/reference/qq.evaluate.core-7.png differ diff --git a/reference/qq.evaluate.core-8.png b/reference/qq.evaluate.core-8.png index d305304..fe66b34 100644 Binary files a/reference/qq.evaluate.core-8.png and b/reference/qq.evaluate.core-8.png differ diff --git a/reference/qq.evaluate.core-9.png b/reference/qq.evaluate.core-9.png index b86c92c..c1810f7 100644 Binary files a/reference/qq.evaluate.core-9.png and b/reference/qq.evaluate.core-9.png differ diff --git a/reference/qq.evaluate.core.html b/reference/qq.evaluate.core.html index 460da88..37488a5 100644 --- a/reference/qq.evaluate.core.html +++ b/reference/qq.evaluate.core.html @@ -1,5 +1,5 @@ -Quantile-Quantile Plots — qq.evaluate.core • EvaluateCoreQuantile-Quantile Plots — qq.evaluate.core • EvaluateCore - +
    - +

    Quantile-Quantile Plots

    - Source: R/qq.evaluate.core.R + Source: R/qq.evaluate.core.R
    @@ -112,27 +112,29 @@

    Quantile-Quantile Plots

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    -
    annotate
    +
    annotate

    Adds the divergence/distance value between probability distributions of CS and EC as an annotation to the QQ plot. Either "none" (no annotation (Default)) or "kl" (Kullback-Leibler @@ -142,16 +144,14 @@

    Arguments

    Value

    - - -

    A list with the ggplot objects of QQ plots of CS vs EC for +

    A list with the ggplot objects of QQ plots of CS vs EC for each trait specified as quantitative.

    References

    Wilk MB, Gnanadesikan R (1968). “Probability plotting methods for the analysis for the analysis of data.” -Biometrika, 55(1), 1--17.

    +Biometrika, 55(1), 1–17.

    See also

    @@ -337,15 +337,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/rpr.evaluate.core.html b/reference/rpr.evaluate.core.html index c57805d..ee6f830 100644 --- a/reference/rpr.evaluate.core.html +++ b/reference/rpr.evaluate.core.html @@ -1,5 +1,5 @@ -Ratio of Phenotype Retained — rpr.evaluate.core • EvaluateCoreRatio of Phenotype Retained — rpr.evaluate.core • EvaluateCore
    - +

    Ratio of Phenotype Retained

    - Source: R/rpr.evaluate.core.R + Source: R/rpr.evaluate.core.R
    @@ -104,31 +104,31 @@

    Ratio of Phenotype Retained

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    The Ratio of Phenotype Retained value.

    +

    The Ratio of Phenotype Retained value.

    Details

    @@ -145,9 +145,9 @@

    Details

    References

    Kim K, Chung H, Cho G, Ma K, Chandrabalan D, Gwag J, Kim T, Cho E, Park Y (2007). “PowerCore: A program applying the advanced M strategy with a heuristic search for establishing core sets.” -Bioinformatics, 23(16), 2155--2162.

    Li Z, Zhang H, Zeng Y, Yang Z, Shen S, Sun C, Wang X (2002). +Bioinformatics, 23(16), 2155–2162.

    Li Z, Zhang H, Zeng Y, Yang Z, Shen S, Sun C, Wang X (2002). “Studies on sampling schemes for the establishment of corecollection of rice landraces in Yunnan, China.” -Genetic Resources and Crop Evolution, 49(1), 67--74.

    +Genetic Resources and Crop Evolution, 49(1), 67–74.

    @@ -189,15 +189,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/scv.evaluate.core.html b/reference/scv.evaluate.core.html index 226e578..9730f14 100644 --- a/reference/scv.evaluate.core.html +++ b/reference/scv.evaluate.core.html @@ -1,5 +1,5 @@ -Synthetic Variation Coefficient — scv.evaluate.core • EvaluateCoreSynthetic Variation Coefficient — scv.evaluate.core • EvaluateCore - +
    - +

    Synthetic Variation Coefficient

    - Source: R/scv.evaluate.core.R + Source: R/scv.evaluate.core.R
    @@ -108,31 +108,31 @@

    Synthetic Variation Coefficient

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    The Synthetic Variation Coefficient values for EC and CS

    +

    The Synthetic Variation Coefficient values for EC and CS

    Details

    @@ -150,9 +150,9 @@

    Details

    References

    Dong YS (1998). “Exploration on genetic diversity center for cultivated soybean in China.” -Chinese Crops Journal, 1, 18--19.

    Dong YS, Zhuang BC, Zhao LM, Sun H, He MY (2001). +Chinese Crops Journal, 1, 18–19.

    Dong YS, Zhuang BC, Zhao LM, Sun H, He MY (2001). “The genetic diversity of annual wild soybeans grown in China.” -Theoretical and Applied Genetics, 103(1), 98--103.

    +Theoretical and Applied Genetics, 103(1), 98–103.

    @@ -195,15 +195,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/shannon.evaluate.core-deprecated.html b/reference/shannon.evaluate.core-deprecated.html index 6dcfb9d..d4961d0 100644 --- a/reference/shannon.evaluate.core-deprecated.html +++ b/reference/shannon.evaluate.core-deprecated.html @@ -1,5 +1,5 @@ -Shannon-Weaver Diversity Index — shannon.evaluate.core-deprecated • EvaluateCoreShannon-Weaver Diversity Index — shannon.evaluate.core-deprecated • EvaluateCore - +
    - +

    Shannon-Weaver Diversity Index

    - Source: R/shannon.evaluate.core.R + Source: R/shannon.evaluate.core.R
    @@ -108,9 +108,7 @@

    Shannon-Weaver Diversity Index

    Value

    - - -

    A data frame with the following columns.

    +

    A data frame with the following columns.

    Trait

    The qualitative trait.

    @@ -213,15 +211,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/shannon.evaluate.core.html b/reference/shannon.evaluate.core.html new file mode 100644 index 0000000..144c9d6 --- /dev/null +++ b/reference/shannon.evaluate.core.html @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/reference/signtest.evaluate.core.html b/reference/signtest.evaluate.core.html index e872753..4d15086 100644 --- a/reference/signtest.evaluate.core.html +++ b/reference/signtest.evaluate.core.html @@ -1,5 +1,5 @@ -Sign Test — signtest.evaluate.core • EvaluateCoreSign Test — signtest.evaluate.core • EvaluateCore - +
    - +

    Sign Test

    - Source: R/signtest.evaluate.core.R + Source: R/signtest.evaluate.core.R
    @@ -108,31 +108,31 @@

    Sign Test

    Arguments

    -
    data
    + + +
    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
    +
    names

    Name of column with the individual names as a character string

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A data frame with the following components.

    +

    A data frame with the following components.

    Comparison

    The comparison measure.

    @@ -162,9 +162,9 @@

    Details

    References

    Basigalup DH, Barnes DK, Stucker RE (1995). “Development of a core collection for perennial Medicago plant introductions.” -Crop Science, 35(4), 1163--1168.

    Tai PYP, Miller JD (2001). +Crop Science, 35(4), 1163–1168.

    Tai PYP, Miller JD (2001). “A Core Collection for Saccharum spontaneum L. from the World Collection of Sugarcane.” -Crop Science, 41(3), 879--885.

    +Crop Science, 41(3), 879–885.

    @@ -208,15 +208,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/snk.evaluate.core.html b/reference/snk.evaluate.core.html index bf0cba2..eab113b 100644 --- a/reference/snk.evaluate.core.html +++ b/reference/snk.evaluate.core.html @@ -1,5 +1,5 @@ -Student-Newman-Keuls Test — snk.evaluate.core • EvaluateCoreStudent-Newman-Keuls Test — snk.evaluate.core • EvaluateCore
    - +

    Student-Newman-Keuls Test

    - Source: R/snk.evaluate.core.R + Source: R/snk.evaluate.core.R
    @@ -104,31 +104,31 @@

    Student-Newman-Keuls Test

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    A data frame with the following components.

    +

    A data frame with the following components.

    Trait

    The quantitative trait.

    @@ -167,9 +167,9 @@

    Value

    References

    Keuls M (1952). “The use of the ,,studentized range" in connection with an analysis of variance.” -Euphytica, 1(2), 112--122.

    Newman D (1939). +Euphytica, 1(2), 112–122.

    Newman D (1939). “The distribution of range in samples from a normal population, expressed in terms of an independent estimate of standard deviation.” -Biometrika, 31(1-2), 20--30.

    +Biometrika, 31(1-2), 20–30.

    See also

    @@ -236,15 +236,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/ttest.evaluate.core.html b/reference/ttest.evaluate.core.html index 4664996..153149e 100644 --- a/reference/ttest.evaluate.core.html +++ b/reference/ttest.evaluate.core.html @@ -1,5 +1,5 @@ -Student's t Test — ttest.evaluate.core • EvaluateCoreStudent's t Test — ttest.evaluate.core • EvaluateCore
    - +

    Student's t Test

    - Source: R/ttest.evaluate.core.R + Source: R/ttest.evaluate.core.R
    @@ -104,29 +104,33 @@

    Student's t Test

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    -
    Trait
    + + +
    Trait

    The quantitative trait.

    EC_Min
    @@ -164,7 +168,7 @@

    Value

    References

    Student (1908). “The probable error of a mean.” -Biometrika, 6(1), 1--25.

    +Biometrika, 6(1), 1–25.

    See also

    @@ -231,15 +235,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/vpf.evaluate.core.html b/reference/vpf.evaluate.core.html index 29f0a41..6ab9fc9 100644 --- a/reference/vpf.evaluate.core.html +++ b/reference/vpf.evaluate.core.html @@ -1,5 +1,5 @@ -Variance of Phenotypic Frequency — vpf.evaluate.core • EvaluateCoreVariance of Phenotypic Frequency — vpf.evaluate.core • EvaluateCore
    - +

    Variance of Phenotypic Frequency

    - Source: R/vpf.evaluate.core.R + Source: R/vpf.evaluate.core.R
    @@ -104,31 +104,31 @@

    Variance of Phenotypic Frequency

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    qualitative
    +
    qualitative

    Name of columns with the qualitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    The Variance of Phenotypic Frequency values for EC and CS.

    +

    The Variance of Phenotypic Frequency values for EC and CS.

    Details

    @@ -147,7 +147,7 @@

    Details

    References

    Li Z, Zhang H, Zeng Y, Yang Z, Shen S, Sun C, Wang X (2002). “Studies on sampling schemes for the establishment of corecollection of rice landraces in Yunnan, China.” -Genetic Resources and Crop Evolution, 49(1), 67--74.

    +Genetic Resources and Crop Evolution, 49(1), 67–74.

    @@ -190,15 +190,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/vr.evaluate.core.html b/reference/vr.evaluate.core.html index 40c15f9..1572ea6 100644 --- a/reference/vr.evaluate.core.html +++ b/reference/vr.evaluate.core.html @@ -1,5 +1,5 @@ -Variable Rate of Coefficient of Variation — vr.evaluate.core • EvaluateCoreVariable Rate of Coefficient of Variation — vr.evaluate.core • EvaluateCore
    - +

    Variable Rate of Coefficient of Variation

    - Source: R/vr.evaluate.core.R + Source: R/vr.evaluate.core.R
    @@ -104,31 +104,31 @@

    Variable Rate of Coefficient of Variation

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    - - -

    The \(VR\) value.

    +

    The \(VR\) value.

    Details

    @@ -145,7 +145,7 @@

    Details

    References

    Hu J, Zhu J, Xu HM (2000). “Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.” -Theoretical and Applied Genetics, 101(1), 264--268.

    +Theoretical and Applied Genetics, 101(1), 264–268.

    @@ -187,15 +187,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/reference/wilcox.evaluate.core.html b/reference/wilcox.evaluate.core.html index 989513b..451fa09 100644 --- a/reference/wilcox.evaluate.core.html +++ b/reference/wilcox.evaluate.core.html @@ -1,5 +1,5 @@ -Wilcoxon Rank Sum Test — wilcox.evaluate.core • EvaluateCoreWilcoxon Rank Sum Test — wilcox.evaluate.core • EvaluateCore - +
    - +

    Wilcoxon Rank Sum Test

    - Source: R/wilcox.evaluate.core.R + Source: R/wilcox.evaluate.core.R
    @@ -106,29 +106,33 @@

    Wilcoxon Rank Sum Test

    Arguments

    -
    data
    + + +
    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 individual names as a character string

    +
    names
    +

    Name of column with the individual names as a character string.

    -
    quantitative
    +
    quantitative

    Name of columns with the quantitative traits as a character vector.

    -
    selected
    +
    selected

    Character vector with the names of individuals selected in core collection and present in the names column.

    Value

    -
    Trait
    + + +
    Trait

    The quantitative trait.

    EC_Med

    The median value @@ -148,7 +152,7 @@

    Value

    References

    Mann HB, Whitney DR (1947). “On a test of whether one of two random variables is stochastically larger than the other.” -The Annals of Mathematical Statistics, 18(1), 50--60.

    Wilcoxon F (1945). +The Annals of Mathematical Statistics, 18(1), 50–60.

    Wilcoxon F (1945). “Individual comparisons by ranking methods.” Biometrics Bulletin, 1(6), 80.

    @@ -206,15 +210,15 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.1.0.

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