In antononcube/QRMon-R: Quantile Regression Monad

Distribution data

The following data is generated to have heteroscedasticity.

xs <- seq(-3, 3, 0.01)
dfDistributionData <- purrr::map_df( xs, function(x) { data.frame( X = x, Y = exp(-x^2) + rnorm( n = 1, mean = 0, sd =  0.15 * sqrt( abs( 1.5 - x) / 1.5 ) ) ) })
dim(dfDistributionData)

ggplot(dfDistributionData) + geom_point(aes(x = X, y = Y ))

usethis::use_data( dfDistributionData )

Weather data

```{Mathematica, eval=F} tsData = WeatherData[{"Orlando", "USA"}, "Temperature", {{2015, 1, 1}, {2019, 1, 1}, "Day"}] tsData2 = QRMonUnit[tsData] ⟹ QRMonTakeData[] Export["~/QRMon-R/data/tsData.csv", Prepend[tsData2, {"Time", "Temperature"}]]

```r
dfTemperatureData <- read.csv( "~/QRMon-R/data/tsData.csv")
head(dfTemperatureData)

usethis::use_data( dfTemperatureData )

Financial data

library(quantmod)
finData <- getSymbols("GE", from = "2014-01-01", to = "2019-01-01", auto.assign = FALSE)

dfFinancialData <- as.data.frame(finData)
dfFinancialData <- data.frame( Time = as.Date(rownames(dfFinancialData)), Value = dfFinancialData$GE.Adjusted )

ggplot(dfFinancialData) + geom_line( aes( x = Time, y = Value ))

usethis::use_data( dfFinancialData )

antononcube/QRMon-R documentation built on July 26, 2021, 1:07 p.m.