library(knitr) knitr::opts_chunk$set(warning=FALSE,message=FALSE)
PPBstats
is a freely available package based on the R software for Participatory Plant Breeding statisticial analyses.
It performs analyses on the data collected during PPB programs at four levels:
The objectives of PPBstats
are
library(PPBstats)
Descriptive analysis can be done regarding:
Below an example on unipart network for seed lots analysis:
# get data data(data_network_unipart_sl) # format data for the package net_unipart_sl = format_data_PPBstats( type = "data_network", data = data_network_unipart_sl, network_part = "unipart", vertex_type = "seed_lots") # Display outputs plot(net_unipart_sl, plot_type = "network", organize_sl = TRUE)
Other examples can be found in the book.
Statistical analysis can be done
Below an example on GGE model:
# get data data(data_model_GxE) # format data for the package data_model_GxE = format_data_PPBstats(data_model_GxE, type = "data_agro")
Once the data are formated, the model is run
# run the GGE model out_gge = model_GxE(data_model_GxE, variable = "y1", gxe_analysis = "GGE")
Then, check it, for example with residuals
# check if the model went well out_check_gge = check_model(out_gge) p_out_check_gge = plot(out_check_gge) p_out_check_gge$residuals
Then, compare means
# get mean comparisons out_mean_comparisons_gge = mean_comparisons(out_check_gge, p.adj = "bonferroni") p_out_mean_comparisons_gge = plot(out_mean_comparisons_gge) p_out_mean_comparisons_gge$germplasm$`1`
Then, look at biplot
# get biplot out_biplot_gge = biplot_data(out_check_gge) p_out_biplot_gge = plot(out_biplot_gge) p_out_biplot_gge$biplot$which_won_where
Sensory analysis can be done on
Below an example on hedonic analysis
# get data data(data_hedonic) # format data for the package data_hedonic = format_data_PPBstats(data_hedonic, type = "data_organo_hedonic", threshold = 2)
# descriptive analysis p_note = plot(data_hedonic, plot_type = "boxplot", x_axis = "germplasm", in_col = "location", vec_variables = "note" ) p_note$note$`germplasm-1|location-1` descriptors = data_hedonic$descriptors p_des = plot(data_hedonic, plot_type = "radar", in_col = "germplasm", vec_variables = descriptors ) p_des$`all-variables`
A model can also be run.
Under development ...
A full tutorial of PPBstats
is available in the book on the website.
ppbstats@framalistes.org
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