rf: Random forest analysis

In pdi: Phenotypic Index Measures for Oak Decline Severity

Description

Perform random forest repetitions.

Usage

rf(analysisTable, cls, params = list(), nreps = 100, seed = 1234)

Arguments

analysisTable	tibble of phenotype data suitable for random forest analysis as returned by preparePhenotypeData
cls	analysisTable column to use as response vector. NULL for unsupervised analyses.
params	additional arguments to pass to randomForest::randomForest
nreps	number of repetitions
seed	random number seed

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)

pdi documentation built on Feb. 10, 2021, 1:08 a.m.