options(knitr.table.format = "html") options(max.print=100, scipen=999, width = 800) knitr::opts_chunk$set(echo=FALSE, cache=FALSE, prompt=FALSE, eval = TRUE, tidy=TRUE, root.dir = "..", fig.height = 8, fig.width = 20, comment=NA, message=FALSE, warning=FALSE) knitr::opts_knit$set(width=100, figr.prefix = T, figr.link = T) knitr::knit_hooks$set(inline = function(x) { prettyNum(x, big.mark=",") })
library(BAS) library(broom) library(caret) library(corrplot) library(data.table) library(dplyr) library(extrafont) library(ggplot2) library(graphics) library(gridExtra) library(reshape2) library(statsr) library(vcd)
source("../R/bestPredictions.R") source("../R/bma.R") source("../R/bmaAnalysis.R") source("../R/bmaComplexity.R") source("../R/bmaEvaluation.R") source("../R/bmaPerformance.R") source("../R/bmaPerformanceReport.R") source("../R/bmaImage.R") source("../R/bmaModel1.R") source("../R/bmaModel1Plots.R") source("../R/bmaPredict.R") source("../R/bmaPredictModels.R") source("../R/bmaPDC.R") source("../R/bmaPIP.R") source("../R/bmaPIPPlots.R") source("../R/corrAssociation.R") source("../R/preprocess.R") source("../R/univariate.R") source("../R/univariateQual.R") source("../R/univariateQuant.R") source("../R/bivariate.R") source("../R/bivariateQual.R") source("../R/bivariateQuant.R") source("../R/summaryStats.R") source("../R/visualization.R")
run <- FALSE if (run == FALSE) { load("./analysis/yX.Rdata") load("./analysis/preprocessed.Rdata") load("./analysis/models.Rdata") load("./analysis/analysis.Rdata") load("./analysis/report.Rdata") load("./analysis/eval.Rdata") load("./analysis/performance.Rdata") } dir.create("./analysis", showWarnings = FALSE)
load("../inst/extdata/movies.RData")
preprocessed <- preprocess(movies) save(preprocessed, file = "./analysis/preprocessed.Rdata")
edaUni <- univariate(data = preprocessed)
edaBi <- bivariate(data = preprocessed)
if (run == TRUE) { yX <- preprocessed %>% select(-title) save(yX, file = "./analysis/yX.Rdata") }
if (isTRUE(run)) { models <- bma(yX = yX) save(models, file = "./analysis/models.Rdata") }
analysis <- bmaAnalysis(models = models) save(analysis, file = "./analysis/analysis.Rdata")
trials <- 400 if (run == TRUE) { performance <- bmaPerformance(yX, trials = trials) save(performance, file = "./analysis/performance.Rdata") }
if (isTRUE(run)) { report <- bmaPerformanceReport(performance = performance) save(report, file = "./analysis/report.Rdata") }
eval <- bmaEvaluation(mList = models, candidates = report$best, top = 10) save(eval, file = "./analysis/eval.Rdata")
cases <- read.csv(file = "../inst/extdata/movies2predict.csv", stringsAsFactors = FALSE) predictions <- bestPredictions(best = report$best[c(1, 7, 8, 9),], models = models, newdata = cases) save(predictions, file = "./analysis/predictions.Rdata")
run <- FALSE
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