rm(list = ls())
library(tidyverse)
library(banter)
library(rfPermute)
## test package example data
data(train.data)
ex.mdl <- initBanterModel(train.data$events) %>%
addBanterDetector(train.data$detectors, ntree = 100, sampsize = 1) %>%
runBanterModel(ntree = 100, sampsize = 1)
summary(ex.mdl)
data(test.data)
predict(ex.mdl, test.data)
## test CalCurCEAS data
load("data/calcurceas data.rdata")
ntree <- 1000
sampsize <- 5
# Initialize model
mdl <- initBanterModel(survey.train$events)
# Add detectors using list
mdl <- addBanterDetector(
mdl,
survey.train$detectors[c("bp", "dw")],
ntree = ntree,
sampsize = sampsize,
num.cores = 1
)
# Add detectors using data.frame
mdl <- addBanterDetector(
mdl,
data = survey.train$detectors$ec,
name = "ec",
ntree = ntree,
sampsize = sampsize
)
# Run model
mdl <- runBanterModel(mdl, 5000, 2)
# Model Summaries
summary(mdl, bins = 20)
rf <- getBanterModel(mdl)
proximityPlot(rf)
plotVotes(rf)
impHeatmap(rf, 20)
plotImpVarDist(rf, getBanterModelData(mdl), "species")
plotPredictedProbs(rf)
# Prediction
test.pred <- predict(mdl, survey.test)
for(x in test.pred) print(head(x))
#save.image("data/test ws.rdata")
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