# overplot This function overlays the scatterplot matrices of two multivariate datsets. Moreover, it shows the dependencies among all the pairs for both datsets. ```r overplot(data1, data2, col1 = "black", col2 = "grey", xlim = NA, ylim = NA, labels = NA, method = "pearson", cex.cor = 1, cex.labels = 1, cor.signif = 2, cex.axis = 1, pch1 = 1, pch2 = 1) ``` ## Arguments - `data1, data2`: Two `N x d` matrices of data to be plotted. - `col1, col2`: Colors used for `data1` and `data2` during the plot. Default is `col1="black"` and `col2="grey"`. - `xlim, ylim`: Two bidimensional vectors indicating the limits of x and y axes for all the scatterplots. If not given, they are authomatically computed for each of the scatterplots. - `labels`: A character vector with the variable names to be printed (if not given, the names of `data1` variables are printed). - `method`: Character indicating the dependence types to be computed between the pairs. Possibilites: "kendall", "spearman" and "pearson" (default) - `cex.cor`: Number: character dimension of the printed dependencies. Default `cex.cor=1`. - `cex.labels`: Number: character dimension of the printed variable names. Default `cex.labels=1`. - `cor.signif`: Number: number of significant numbers of the printed dependencies. Default `cor.signif=2`. - `cex.axis`: Number: dimension of the axis numeric values. Default cex.axis=1. - `pch1, pch2`: Paramter to specify the symbols to use when plotting points of `data1` and `data2`.Default is `pch1=1` and `pch2=1`. ## Returns A matrix of overlaying scatterplots of the multivariate datsets `data1` and `data2`, with the dependencies of the pairs. ## Examples ```r # Example 1 # Read and prepare the data for the plot data(dataset) data1 <- dataset$data[1:300,] data2 <- dataset$data[301:600,] overplot(data1,data2,xlim=c(0,1),ylim=c(0,1),method="kendall") ## Not run: # Example 2 # Read and prepare the data for the plot data(dataset) data <- dataset$data[1:200,1:5] colnames(data) <- c("Y1","Y2","X3","X4","X5") # Fit copula families for the defined vine: ListVines <- CDVineCondListMatrices(data,Nx=3) Matrix=ListVines$CVine[[1]] RVM <- CDVineCondFit(data,Nx=3,Matrix=Matrix) # Simulate data: d=dim(RVM$Matrix)[1] cond1 <- data[,RVM$Matrix[(d+1)-1,(d+1)-1]] cond2 <- data[,RVM$Matrix[(d+1)-2,(d+1)-2]] cond3 <- data[,RVM$Matrix[(d+1)-3,(d+1)-3]] condition <- cbind(cond1,cond2,cond3) Sim <- CDVineCondSim(RVM,condition) # Plot the simulated variables Sim over the observed Sim <- data.frame(Sim) overplot(data[,1:2],Sim[,1:2],xlim=c(0,1),ylim=c(0,1),method="spearman") overplot(data,Sim,xlim=c(0,1),ylim=c(0,1),method="spearman") ## End(Not run) ``` ## Author(s) Emanuele Bevacqua

overplot function