Jan 25

ClimMob-analysis v3.0 (2024-01-25)

Improvements

• Adds analysis of variance for variety performance
• Adds pseudo ranking when network is poorly connected

Bug fixes

• Fixes changes in reference for the log-worth plot

God jul

Improvements

• Enables participant individual reports
• Fix small issues in the text in main report
• Implement modules of data handling and analysis to allow the generation of different reports
• Add a simple forward selection to choose the best covariates for the Plackett-Luce trees
• Optimize the main script to avoid mistakes in placing tables and indices
• Update the participant reports, now the report in rendered as one single file (two per participant) and uses new display items i) a vertical bar plot to illustrate a podium ii) a horizontal bar plot with the probabilities of winning for the varieties in the baseline trait

Winds of spring

ClimMob-analysis v1.2 (2021-03-17)

Improvements

• New criteria in filtering the trait entries (participants' responses): (1) having at least 5 valid entries (no NA's, both positive and negative answered), and (2) that all the technologies are tested at least twice per given trait.
• If no trait passes these criteria, then a report with the message is generated.
• The main script is revised in its structure and documentation to increase readability and improve bug fixes. The new structure to organise the sections follows, first an overview of the ClimMob project. Then the analysis of the main trait, followed by the other traits independently. Sections are now organised by trait and not but the type of analysis.
• The main trait (reference trait) is selected based on the availability of the most common reference traits, first the algorithm looks for the overall performance (or overall preference), then yield, then taste. If none of these traits are found, the algorithm selects the last trait assessed in the project, as provided by the arguments in the file data.json.
• The algorithm deals trait/covariates assessed more than once if in different data collection moments. To avoid issues in factor levels, we add an additional string with sequential numbers per each duplicated trait/covariate. Check function rename_duplicates() here.
• We added a new chart showing the number of valid answers received for each trait in the data collection moments. Even those removed for the analysis due to few data available.
• Table with number of technologies assessed by gender now is produced based on the aliases of the variable "REG_gender" which makes possible the inclusion of new aliases.
• Information on the data collection moment is provided and linked to their respective traits and covariates. This will help the reader to find out which trait/covariate belongs to the data collection moment, mostly when the trait/covariate is collected in more than one data collection moment.
• An alpha of 0.5 is used in the Plackett-Luce tree for the main trait. This is to enable the creation of trees even with a small sample size. The algorithm still prints the message if the tree has significant groups with an alpha of 0.1 (default for the analysis).
• Minor improvements in sorting the traits for the participants' reports and how traits and question asked are displayed.
• Analysis will handle ties (if any)

BUG FIXES

• Indices and p.values were mistakenly printed in the text describing the table of "Summary of differences found in varieties by trait" saying, for example, that one trait had significant difference when it did not. The indices in the data.frame are taken correctly now. This error did not influences the readability of previous versions of the mentioned table, only the text that is printed before it.

Jule

ClimMob-analysis v1.1-2 (2020-12-13)

Improvements

• Enable the production of reports when registration and data collection are performed at the same time (e.g. market tasting)
• Variables from multichoice questions in ODK are now decoded and displayed as factors.

Changes in behaviour

• The native characteristic 'overall characteristic' from the ClimMob default library is not required. If not provided the algorithm will use the last characteristic submitted in the file with the parameters for analysis (info.json). The reported is adapted to accept this new behaviour.
• In the case of error in matching the strings of covariates from the parameters (data.json) and data (info.json) the algorithm will not break but rather impute the "intercept" covariate.
• The file "climmob.R" executes all the code required to produce the reports. It calls for the other files in "R/" when needed.
• The reference to the Tricot user guide is added to the report.

Bug fixes

• Fixes an issue in matching the names of explanatory variables by using the string $ to indicate the end of the pattern.

I am ready

ClimMob-analysis v1.1-1 (2020-08-13)

Improvements

• More informative error messages
• Enable the production of participants individual reports
• Many bug fixes

ClimMob v1.1

ClimMob-analysis v1.1 (2020-08-08)

Improvements

• Analitical process was moved to a single script "R/analysis_climmob.R"
• Dynamic statements for the report based on the in-putted data and results from analysis. This will also make less complicated to translate the report to other languages
• Code is wrapped in a tryCatch() to prevent crashes when a non-expected behaviour in the data occurs. When any error occurs during the analysis the script will return a "failed report" with a message asking the user to contact ClimMob supporting service.
• The process now produce the reports for projects with only one characteristic, the 'overall_performance', which is mandatory. If overall_performance is missing the script will return the "failed report".
• Rankings can be build with more than 3 items per participant using do.call(), which will build the rankings with rank_tricot() when 3 items or with rank_numeric() when 4+ items. Projects with 4 or more items (to be tested by each participant) can now be enabled in ClimMob.net

Changes in behaviour

• New argument (arg[10]) is added for the reference item (from a discussion with IITA and Cornell). The argument can be NULL or NA (which will take the first item as reference) or a character string selected by the user which should match with one of the items tested in the project.
• Map is build with package leaflet
• New packages added leaflet and multcompView
• Coordinates in the report map (if any) are clustered in groups with a resolution of 0.05, to comply with the participant's privacy.