#' Plot the decontaminated cumulative distribution function (CDF) of the unknown component for an estimated admixture model #' #' Plot the decontaminated CDF of the unknown component of the admixture model under study, after inversion of the admixture #' CDF. Recall that an admixture model follows the cumulative distribution function (CDF) L, where #' L = p*F + (1-p)*G, with g a known CDF and p and f unknown quantities. #' #' @param x_val A vector of x-axis values at which to plot the decontaminated cumulative distribution function F. #' @param decontamin_cdf An object from function 'decontamin_cdf_unknownComp', containing the unknown decontaminated #' distribution function, as well as the support of the distribution (either discrete or continuous). #' @param add_plot A boolean (TRUE by default) specifying if one plots the decontaminated density over an existing plot. Used for visual #' comparison purpose. #' #' @details The decontaminated CDF is obtained by inverting the admixture CDF, given by L = p*F + (1-p)*G, to isolate the #' unknown component F after having estimated p and L. #' #' @return The plot of the decontaminated cumulative distribution function. #' #' @examples #' ####### Continuous support: #' ## Simulate data: #' list.comp <- list(f1 = 'norm', g1 = 'norm', #' f2 = 'norm', g2 = 'norm') #' list.param <- list(f1 = list(mean = 3, sd = 0.5), g1 = list(mean = 0, sd = 1), #' f2 = list(mean = 3, sd = 0.5), g2 = list(mean = 5, sd = 2)) #' sample1 <- rsimmix(n=3000, unknownComp_weight=0.7, comp.dist = list(list.comp$f1,list.comp$g1), #' comp.param=list(list.param$f1,list.param$g1)) #' sample2 <- rsimmix(n=2500, unknownComp_weight=0.8, comp.dist = list(list.comp$f2,list.comp$g2), #' comp.param=list(list.param$f2,list.param$g2)) #' ## Estimate the mixture weight in each of the sample in real-life setting: #' list.comp <- list(f1 = NULL, g1 = 'norm', #' f2 = NULL, g2 = 'norm') #' list.param <- list(f1 = NULL, g1 = list(mean = 0, sd = 1), #' f2 = NULL, g2 = list(mean = 5, sd = 2)) #' estimate <- IBM_estimProp(sample1[['mixt.data']], sample2[['mixt.data']], comp.dist = list.comp, #' comp.param = list.param, with.correction = FALSE, n.integ = 1000) #' ## Determine the decontaminated version of the unknown CDF by inversion: #' res1 <- decontamin_cdf_unknownComp(sample1 = sample1[['mixt.data']], #' comp.dist = list.comp[1:2], comp.param = list.param[1:2], #' estim.p = estimate$prop.estim[1]) #' res2 <- decontamin_cdf_unknownComp(sample1 = sample2[['mixt.data']], #' comp.dist = list.comp[3:4], comp.param = list.param[3:4], #' estim.p = estimate$prop.estim[2]) #' plot_decontamin_cdf(x_val = seq(from=-1, to=5, length.out=30), decontamin_cdf = res1, #' add_plot = FALSE) #' plot_decontamin_cdf(x_val = seq(from=-1, to=5, length.out=30), decontamin_cdf = res2, #' add_plot = TRUE) #' ####### Countable discrete support: #' list.comp <- list(f1 = 'pois', g1 = 'pois', #' f2 = 'pois', g2 = 'pois') #' list.param <- list(f1 = list(lambda = 3), g1 = list(lambda = 2), #' f2 = list(lambda = 3), g2 = list(lambda = 4)) #' sample1 <- rsimmix(n=4000, unknownComp_weight=0.7, comp.dist = list(list.comp$f1,list.comp$g1), #' comp.param=list(list.param$f1,list.param$g1)) #' sample2 <- rsimmix(n=3000, unknownComp_weight=0.9, comp.dist = list(list.comp$f2,list.comp$g2), #' comp.param=list(list.param$f2,list.param$g2)) #' ## Estimate the mixture weight in each of the sample in real-life setting: #' list.comp <- list(f1 = NULL, g1 = 'pois', #' f2 = NULL, g2 = 'pois') #' list.param <- list(f1 = NULL, g1 = list(lambda = 2), #' f2 = NULL, g2 = list(lambda = 4)) #' estimate <- IBM_estimProp(sample1[['mixt.data']], sample2[['mixt.data']], comp.dist = list.comp, #' comp.param = list.param, with.correction = FALSE, n.integ = 1000) #' res1 <- decontamin_cdf_unknownComp(sample1 = sample1[['mixt.data']], #' comp.dist = list.comp[1:2], comp.param = list.param[1:2], #' estim.p = estimate$prop.estim[1]) #' res2 <- decontamin_cdf_unknownComp(sample1 = sample2[['mixt.data']], #' comp.dist = list.comp[3:4], comp.param = list.param[3:4], #' estim.p = estimate$prop.estim[2]) #' plot_decontamin_cdf(x_val = seq(from=0, to=15, by=1), decontamin_cdf = res1, #' add_plot = FALSE) #' plot_decontamin_cdf(x_val = seq(from=0, to=15, by=1), decontamin_cdf = res2, #' add_plot = TRUE) #' ####### Finite discrete support: #' list.comp <- list(f1 = 'multinom', g1 = 'multinom', #' f2 = 'multinom', g2 = 'multinom') #' list.param <- list(f1 = list(size=1, prob=c(0.3,0.4,0.3)), g1 = list(size=1, prob=c(0.6,0.3,0.1)), #' f2 = list(size=1, prob=c(0.3,0.4,0.3)), g2 = list(size=1, prob=c(0.2,0.6,0.2))) #' sample1 <- rsimmix(n=5000, unknownComp_weight=0.7, comp.dist = list(list.comp$f1,list.comp$g1), #' comp.param=list(list.param$f1,list.param$g1)) #' sample2 <- rsimmix(n=4000, unknownComp_weight=0.9, comp.dist = list(list.comp$f2,list.comp$g2), #' comp.param=list(list.param$f2,list.param$g2)) #' list.comp <- list(f1 = NULL, g1 = 'multinom', #' f2 = NULL, g2 = 'multinom') #' list.param <- list(f1 = NULL, g1 = list(size=1, prob=c(0.6,0.3,0.1)), #' f2 = NULL, g2 = list(size=1, prob=c(0.2,0.6,0.2))) #' estimate <- IBM_estimProp(sample1[['mixt.data']], sample2[['mixt.data']], comp.dist = list.comp, #' comp.param = list.param, with.correction = FALSE, n.integ = 1000) #' res1 <- decontamin_cdf_unknownComp(sample1 = sample1[['mixt.data']], #' comp.dist = list.comp[1:2], comp.param = list.param[1:2], #' estim.p = estimate$prop.estim[1]) #' res2 <- decontamin_cdf_unknownComp(sample1 = sample2[['mixt.data']], #' comp.dist = list.comp[3:4], comp.param = list.param[3:4], #' estim.p = estimate$prop.estim[2]) #' plot_decontamin_cdf(x_val = seq(from=0, to=5, by=1), decontamin_cdf = res1, #' add_plot = FALSE) #' plot_decontamin_cdf(x_val = seq(from=0, to=5, by=1), decontamin_cdf = res2, #' add_plot = TRUE) #' #' @author Xavier Milhaud #' @export plot_decontamin_cdf <- function(x_val, decontamin_cdf, add_plot = FALSE) { if (!add_plot) { stats::plot.stepfun(x = decontamin_cdf, xval = x_val) } else { old_par <- graphics::par()$new graphics::par(new = TRUE) stats::plot.stepfun(x = decontamin_cdf, xval = x_val, add = TRUE, col = "blue") on.exit(graphics::par(old_par)) } }