We can use a simulated data set and run through the PReMiuM workflow to get an idea of how it works and an example of the results.

Workflow

Download the rds file here and read it into R using df <- read_rds("path/hyb_simulated.rds")[[1]] where path is the path to the downloaded file. The rds file contains a list of 3 simulated data sets so we select the first data set.

Check for constant (no variance) variables and for this example select the minimum variables.

variance.var <- names(which(map_dbl(df[,6:93], var, na.rm = TRUE) != 0))
min.vars <- str_subset(variance.var, "Min")

Run the profile regression and post-processing.

runInfoObj <- profRegr(covNames, outcome = 'Yield', yModel = 'Normal', xModel = "Mixed", discreteCovs = "Pedi", continuousCovs = min.vars, data = df, nSweeps = 3000, nBurn = 1000, nProgress = 100)
calcDists <- calcDissimilarityMatrix(runInfoObj)
clusObj <- calcOptimalClustering(calcDists)
riskProfObj <- calcAvgRiskAndProfile(clusObj)

Save the output as rds files to easily read in for post-hoc analysis.

write_rds(riskProfObj, "../riskProfObj.rds", compress = "xz")
write_rds(clusObj, "../clusObj.rds", compress = "xz")