qad-package {qad} | R Documentation |
A copula-based measure for quantifying asymmetry in dependence and associations.
Package: | qad |
Type: | Package |
Version: | 0.1.1 |
Date: | 2018-12-18 |
Florian Griessenberger: florian.griessenberger@sbg.ac.at,
Robert R. Junker: Robert.Junker@sbg.ac.at,
Wolfgang Trutschnig: Wolfgang.Trutschnig@sbg.ac.at
##Create data set # n <- 1000 # x <- rnorm(n,0,2) # y <- x^2 + rnorm(n) # sample <- data.frame(x,y) # plot(sample) ##Function: empirical copula # eval <- data.frame(x=c(0,0.1,0.2,0.45,1), y=c(0,0.1,0.5,0.23,1)) # emp_c_copula_eval(sample, eval, resolution = 10) # mass <- emp_c_copula(sample, resolution=10) ##Function: qad() # qad(sample, resolution = NULL, permutation = FALSE, nperm = 100, DoParallel = TRUE, ncores = NULL) # help(qad) # mod <- qad(sample) # mod <- qad(sample, resolution = NULL, permutation = TRUE, nperm = 10, DoParallel = TRUE) ##Functions: summary(), coef() # help(summary.qad) # mod <- qad(sample) # summary(mod) # coef(mod) # coef(mod, select = c('q(x2,x1)','mean.dependence')) ##Function: plot() # help(plot.qad) # plot(mod) # plot(mod, addSample = TRUE, copula = FALSE, margins = TRUE, point.size = 0.7, panel.grid = FALSE) ##Function: cci() # help(cci) # n <- 1000 # cci(n, alternative = "one.sided") # cci(n, alternative = "two.sided") ##Function: predict() # help(predict.qad) # values <- c(-2.4,1,0,2.6) # predict.qad(mod, values = values, conditioned = 'x1') # predict(mod, values, conditioned = "x1", nr_intervals = 10, pred_plot = TRUE, panel.grid = FALSE) # values <- c(0.1,0.5) # predict(mod, values, conditioned = "x2", nr_intervals = 5, copula = TRUE, pred_plot = TRUE) ##Function: pairwise.qad and heatmap.qad # df <- iris[1:4] # mod <- pairwise.qad(df) # heatmap.qad(mod, select = 'dependence')