perform multidimensional scaling of random forest proximities

mds(rfModels, dimensions = 2)

Arguments

rfModels

list containing random forest models as returned by rf()

dimensions

number of dimensions to scale to

Examples

library(dplyr) ## Retrieve file paths for example data files <- list.files(system.file('phenotypeDataCollectionSheets', package = 'pdi'),full.names = TRUE) ## Prepare data d <- map(files,readPhenotypeSheet) %>% map(preparePhenotypeData) %>% bind_rows() %>% siteAdjustment() %>% mutate(`Live crown ratio (%)` = liveCrownRatio(`Total height (m)`, `Lower crown height (m)`), `Crown condition (%)` = crownCondition(`Missing crown (%)`, `Crown transparency (%)`), `Crown volume (m^3)` = crownVolume(`Crown radius (m)`, `Total height (m)`, `Lower crown height (m)`, `Crown condition (%)`), `Bleed prevalence (%)` = bleedPrevalence(`Active bleed length (mm)`, `Active bleeds`, `Black staining length (mm)`, `Black staining`, `Diameter at breast height (m)`), `Agrilus exit hole density (m^-2)` = agrilusExitHoleDensity(`Agrilus exit holes`, `Diameter at breast height (m)`) ) t <- makeAnalysisTable(d) ## Generate random forest models m <- rf(t,cls = NULL,nreps = 10) mds_data <- mds(m,2)