Implement seeds for algorithms using caret

[dchiu911]

library(caret)
#> Warning: package 'caret' was built under R version 3.5.2
#> Loading required package: lattice
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 3.5.2
library(randomForest)
#> randomForest 4.6-14
#> Type rfNews() to see new features/changes/bug fixes.
#> 
#> Attaching package: 'randomForest'
#> The following object is masked from 'package:ggplot2':
#> 
#>     margin

ntree <- 33

set.seed(321)
trainData <- twoClassSim(5000, linearVars = 3, noiseVars = 9)
testData  <- twoClassSim(5000, linearVars = 3, noiseVars = 9)

set.seed(432)
mySeeds <- c(sapply(simplify = FALSE, 1:25, function(u) sample(10 ^ 4, 3)),
             2019)
cvCtrl <- trainControl(method = "repeatedcv", number = 5, repeats = 5, 
                       classProbs = TRUE, summaryFunction = twoClassSummary, 
                       seeds = mySeeds)

fitRFcaret <- train(Class ~ ., data = trainData, trControl = cvCtrl,
                    ntree = ntree, method = "rf", metric = "ROC")

set.seed(tail(mySeeds, 1)[[1]])
fitRFmanual <- randomForest(Class ~ ., data = trainData,
                            mtry = fitRFcaret$bestTune$mtry, ntree = ntree) 

all.equal(as.vector(predict(fitRFcaret, testData)),
          as.vector(predict(fitRFmanual, testData)))
#> [1] TRUE

^{Created on 2019-05-29 by the reprex package (v0.3.0)}