Bivariate Verteilungen: 3D-Plot und Contour-Plot скачать в хорошем качестве

Bivariate Verteilungen: 3D-Plot und Contour-Plot

Bivariate Density Plot (normal*normal) ## library(plot3D) library(copula) gs = 200 # grid size x = seq(-4, 4, length.out=gs) # range x-values y = seq(-4, 4, length.out=gs) # range y-values grid = as.matrix(expand.grid(x,y)) mv = mvdc(normalCopula(.6), c("norm", "norm"), list(list(0, 1), list(0, 1))) # Normal/Gauß-Copula mv_density = dMvdc(grid, mv) z = matrix(mv_density, nrow=gs, ncol=gs, byrow=FALSE) panelfirst = function(pmat){ scale = max(z)/dnorm(0) lines3D(x=x, y=rep(max(y), gs), z=dnorm(x)*scale, colkey=FALSE, lwd=3, add=TRUE) ## marginal density of x lines3D(x=rep(min(x), gs), y=y, z=dnorm(x)*scale, colkey=FALSE, lwd=3, add=TRUE) ## marginal density of y } persp3D(x=x, y=y, z=z, colkey=FALSE, panel.first=panelfirst, contour=list(side="zmax"), xlab="X", ylab="Y", zlab="joint density", phi=50, theta=30) contour2D(z=z, x=x, y=y, colkey=FALSE, xlab="X", ylab="Y", frame.plot=FALSE) END ######################### Bivariate Density Plot (binom, binom) ## library(plot3Drgl) library(copula) ACHTUNG: MODELL nur für große n und kleine p geeignet! ## n = 8 # number of trials p = 0.5 # success probability for each trial rho = 0.0 # correlation coefficient gs = n+1 # grid size x = seq(0, n, length.out=gs) y = seq(0, n, length.out=gs) grid = as.matrix(expand.grid(x,y)) mv = mvdc(normalCopula(rho), margins=c("binom", "binom"), paramMargins=list(list(n, p), list(n, p))) mv_density = dMvdc(grid, mv) z = matrix(mv_density, nrow=gs, ncol=gs, byrow=FALSE) # plot hist3Drgl(x=x, y=y, z=z, colkey=FALSE, xlab="X", ylab="Y", zlim=c(0, max(z)), space=.3, zlab="joint probability", phi=50, theta=30, shade=.4, ltheta=90) contour2D(z=z, x=x, y=y, colkey=FALSE, xlab="X", ylab="Y", frame.plot=FALSE) add marginal distributions hist_x = dbinom(x, size=n, prob=p)^2 # univariate histogram show2d({ par(mar=c(0,0,0,0)) barplot(hist_x, yaxs="i", ylim=c(0,max(z)), axes=FALSE, col="darkgrey")}, expand=1, face="y+", texmipmap=FALSE) # add at max(y) show2d({ par(mar=c(0,0,0,0)) barplot(hist_x, yaxs="i", ylim=c(0,max(z)), axes=FALSE, col="darkgrey")}, expand=1, face="x-", texmipmap=FALSE) # add at min(x) END ######################### Für Fragen und konstruktive Verbesserungsvorschläge nutzt bitte die Kommentare. Kanalinfo:    / @statistikverstehen