R/plot_roc.R

In sparsenetgls: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression

#'The plot_roc() function
#'@description The plot_roc function is designed to produce the Receiver
#'Operative Characteristics (ROC) Curve for visualizing the prediction 
#'accuracy of a Gaussian Graphical model (GGM) to the true graph structure. 
#'The GGM must use a l-p norm regularizations (p=1,2) with the series of 
#'solutions conditional on the regularization parameter.
#'
#'@import  Matrix MASS
#'@param result_assessment It is the list result from function
#'path_result_for_roc() which has five-dimensions recording
#'the path number (i.e. the order of \eqn{\lambda} ), the sensitivity,
#'the specificity, the Negative predicted value (NPV) and
#'the Positive predicted value (PPV) respectively.
#'
#'@param group It is a logical parameter indicating if the result_assessment
#'is for several GGM models. When it is TRUE,
#'it produceS the ROC from several GGM models. when it is FALSE, it only
#'produces a ROC for one model.
#'
#'@param ngroup It is an  integer recording the number of models when group
#'is TRUE.
#'
#'@param est_names it is used for labeling the GGM model in legend of ROC
#'curve.
#'
#'@return  Return the plot of Receiver Operational Curve
#'
#'@examples
#'prec1 <- matrix(c(0,2,3,1,0,0.5,0,0,0.4),nrow=3,ncol=3)
#'Omega_est <- array(dim=c(3,3,3))
#'Omega_est[,,1] <- matrix(c(1,1,1,0.2,0.5,0.2,2,0.2,0.3),nrow=3,ncol=3)
#'Omega_est[,,2] <- matrix(c(0,1,1,1,0,0,0,0,1),nrow=3,ncol=3)
#'Omega_est[,,3] <- matrix(c(0,0,0,0,0,0,0,0,0),nrow=3,ncol=3)
#'roc_path_result <- path_result_for_roc(PREC_for_graph=prec1,
#'OMEGA_path=Omega_est,pathnumber=3)
#'plot_roc(result_assessment=roc_path_result,group=FALSE,ngroup=0,
#'est_names='test example')
#'
#'
#'@export

plot_roc <- function(result_assessment, group = TRUE, 
    ngroup = 0, est_names) {
    
    if (!group) {
        Sensitivity <- result_assessment[, 1]
        FPR <- 1 - result_assessment[, 2]
        
        plot(FPR, Sensitivity, xlab = "1-specificity", 
            ylab = "sensitivity", ylim = c(0, 1), xlim = c(0, 1), 
            pch = 19, cex = 1.5, 
            main = "ROC of network structure estimates")
        lines(FPR, Sensitivity)
        
    } else {
        
        k = 1
        sentivity = FPR = vector("numeric", length = ngroup)
        
        plot((1 - result_assessment[, 2, 1]), result_assessment[, 
            1, 1], xlab = "1-specificity", ylab = "sensitivity", 
            pch = 19, cex = 1.5, ylim = c(0, 1), xlim = c(0, 
                1), main = "ROC of network structure estimates")
        
        while (k <= ngroup) {
            sensitivity = result_assessment[, 1, k]
            FPR = 1 - result_assessment[, 2, k]
            points(FPR, sensitivity, xlab = "1-specificity", 
                ylab = "sensitivity", pch = 19, cex = 1, 
                col = k)
            lines(FPR, sensitivity, col = k)
            k = k + 1
        }
    }
    segments(0, 0, 1, 1)
    legend(0.1, 0.9, est_names, lty = seq_len(ngroup), 
        col = seq_len(ngroup), bty = "n")
}