plot.decontaminated_density: Plot method for object of class 'decontaminated_density'

In admix: Package Admix for Admixture (aka Contamination) Models

Plot method for object of class 'decontaminated_density'

Description

Plot the decontaminated density of the unknown component from some admixture model, after inversion of the admixture cumulative distribution functions.

Usage

## S3 method for class 'decontaminated_density'
plot(x, x_val, add_plot = FALSE, ...)

Arguments

x	An object of class 'decontaminated_density' (see ?decontaminated_density).
x_val	(numeric) A vector of points at which to evaluate the probability mass/density function.
add_plot	(default to FALSE) A boolean specifying if one plots the decontaminated density over an existing plot. Used for visual comparison purpose.
...	Arguments to be passed to generic method 'plot', such as graphical parameters (see ?par).

Details

The decontaminated density is obtained by inverting the admixture density, given by l = p*f + (1-p)*g, to isolate the unknown component f after having estimated p and l.

Value

The plot of the decontaminated density.

Author(s)

Xavier Milhaud [e-mail]

Examples

####### Continuous support:
## Simulate mixture data:
mixt1 <- twoComp_mixt(n = 400, weight = 0.4,
                      comp.dist = list("norm", "norm"),
                      comp.param = list(list("mean" = 3, "sd" = 0.5),
                                        list("mean" = 0, "sd" = 1)))
mixt2 <- twoComp_mixt(n = 350, weight = 0.6,
                      comp.dist = list("norm", "norm"),
                      comp.param = list(list("mean" = 3, "sd" = 0.5),
                                        list("mean" = 5, "sd" = 2)))
data1 <- getmixtData(mixt1)
data2 <- getmixtData(mixt2)
## Define the admixture models:
admixMod1 <- admix_model(knownComp_dist = mixt1$comp.dist[[2]],
                         knownComp_param = mixt1$comp.param[[2]])
admixMod2 <- admix_model(knownComp_dist = mixt2$comp.dist[[2]],
                         knownComp_param = mixt2$comp.param[[2]])
## Estimation:
est <- admix_estim(samples = list(data1,data2),
                   admixMod = list(admixMod1,admixMod2), est_method = 'PS')
prop <- getmixingWeight(est)
## Determine the decontaminated version of the unknown density by inversion:
res1 <- decontaminated_density(sample1 = data1, estim.p = prop[1],
                               admixMod = admixMod1)
res2 <- decontaminated_density(sample1 = data2, estim.p = prop[2],
                               admixMod = admixMod2)
## Use appropriate sequence of x values:
plot(x = res1, x_val = seq(from = 0, to = 6, length.out = 100), add_plot = FALSE)
plot(x = res2, col = "red", x_val = seq(from = 0, to = 6, length.out = 100), add_plot = TRUE)

####### Countable discrete support:
mixt1 <- twoComp_mixt(n = 4000, weight = 0.7,
                      comp.dist = list("pois", "pois"),
                      comp.param = list(list("lambda" = 3),
                                        list("lambda" = 2)))
mixt2 <- twoComp_mixt(n = 3500, weight = 0.85,
                      comp.dist = list("pois", "pois"),
                      comp.param = list(list("lambda" = 3),
                                        list("lambda" = 4)))
data1 <- getmixtData(mixt1)
data2 <- getmixtData(mixt2)
## Define the admixture models:
admixMod1 <- admix_model(knownComp_dist = mixt1$comp.dist[[2]],
                         knownComp_param = mixt1$comp.param[[2]])
admixMod2 <- admix_model(knownComp_dist = mixt2$comp.dist[[2]],
                         knownComp_param = mixt2$comp.param[[2]])
## Estimation:
est <- admix_estim(samples = list(data1,data2),
                   admixMod = list(admixMod1,admixMod2), est_method = "IBM")
prop <- getmixingWeight(est)
## Determine the decontaminated version of the unknown density by inversion:
res1 <- decontaminated_density(sample1 = data1, estim.p = prop[1],
                               admixMod = admixMod1)
res2 <- decontaminated_density(sample1 = data2, estim.p = prop[2],
                               admixMod = admixMod2)
## Use appropriate sequence of x values:
plot(x = res1, x_val = seq(from = 0, to = 15, by = 1), add_plot = FALSE)
plot(x = res2, x_val = seq(from = 0, to = 15, by = 1), add_plot = TRUE, col = "red")

####### Finite discrete support:
mixt1 <- twoComp_mixt(n = 4000, weight = 0.7,
                      comp.dist = list("multinom", "multinom"),
                      comp.param = list(list("size" = 1, "prob" = c(0.3,0.4,0.3)),
                                        list("size" = 1, "prob" = c(0.6,0.3,0.1))))
mixt2 <- twoComp_mixt(n = 3500, weight = 0.85,
                      comp.dist = list("multinom", "multinom"),
                      comp.param = list(list("size" = 1, "prob" = c(0.3,0.4,0.3)),
                                        list("size" = 1, "prob" = c(0.2,0.6,0.2))))
data1 <- getmixtData(mixt1)
data2 <- getmixtData(mixt2)
## Define the admixture models:
admixMod1 <- admix_model(knownComp_dist = mixt1$comp.dist[[2]],
                         knownComp_param = mixt1$comp.param[[2]])
admixMod2 <- admix_model(knownComp_dist = mixt2$comp.dist[[2]],
                         knownComp_param = mixt2$comp.param[[2]])
## Estimation:
est <- admix_estim(samples = list(data1,data2),
                   admixMod = list(admixMod1,admixMod2), est_method = "IBM")
prop <- getmixingWeight(est)
## Determine the decontaminated version of the unknown density by inversion:
res1 <- decontaminated_density(sample1 = data1, estim.p = prop[1],
                               admixMod = admixMod1)
res2 <- decontaminated_density(sample1 = data2, estim.p = prop[2],
                               admixMod = admixMod2)
## Use appropriate sequence of x values:
plot(x = res1, x_val = seq(from=1, to=3, by=1), add_plot = FALSE)
plot(x = res2, x_val = seq(from=1, to=3, by=1), add_plot = TRUE, col = "red")

admix documentation built on June 8, 2025, 9:32 p.m.