When there is evidence that an experimental factor impacts on response, it is usefull to perform post hoc mean comparison tests to find the condition(s) that have an effect on readout.
This page uses the ggstatsplot to create graphics with details from statistical tests. Note that currently, the 'Pairwise Comparison' is EXTREMELY slow when a lot of comparisons are made. So, be patient, or filter your dataset before hand to compute only relevant comparisons. The 'Test Type' button specifiy the type of method used for mean comparisons. Refer to the package documentation for details.
Tukey HSD tests results: post hoc comparisons on each combination of factor levels in the model.
Convert your symptom intensity data into a qualitative index for plant pathogen race analysis: Pathogen races (also referred to as physiological races or pathotypes) are defined by their profile of pathogenicity on a defined set of differential host cultivars (i.e. a set of host genotypes that each carry a distinct profile of resistance genes). Oftentimes, pathogenicity is defined as an ordered categorical variable (ordinal) with levels depicting disease outcome (e.g. Resistant < Moderately Susceptible < Susceptible)
This tool takes mean symptom measures and convert them into categories defined by the user. This categorical data is then plotted as a Heatmap where it is straightforward to observe the clustering of virulence profiles into races. Furthermore, unique pathogenicity profiles (i.e. races) in the data set are computed and assigned to each strain in the table summarizing the output data. Finally, the categories distribution for each individual (levels of the variable displayed in row) is displayed in the stacked barplot.