plotly_spEMN01: Plot mixture pdf for the semiparametric mixture model output...

In mixtools: Tools for Analyzing Finite Mixture Models

Plot mixture pdf for the semiparametric mixture model output by spEMsymlocN01 using plotly.

Description

This is an updated version of plotlspEMN01 function by using plotly. For technical details, please refer to plot.spEMN01.

Usage

plotly_spEMN01(x, bw=x$bandwidth, knownpdf=dnorm, add.plot=FALSE,
               width = 3 , col.dens = NULL, col.hist =  '#1f77b4',
               title = NULL , title.size = 15 , 
               title.x = 0.5 , title.y = 0.95,
               xlab = "t" , xlab.size = 15 , xtick.size = 15,
               ylab = "Density" , ylab.size = 15 , ytick.size = 15,
               legend.text = "Densities" , legend.text.size = 15 , 
               legend.size = 15)

Arguments

x	An object of class "spEMN01" as returned by spEMsymlocN01
bw	Bandwidth for weighted kernel density estimation.
knownpdf	The known density of component 1, default to dnorm.
add.plot	Set to TRUE to add to an existing plot.
width	Line width.
col.dens	Color of density lines. Number of colors specified needs to be consistent with number of components.
col.hist	Color of histogram.
title	Text of the main title.
title.size	Size of the main title.
title.x	Horsizontal position of the main title.
title.y	Vertical posotion of the main title.
xlab	Label of X-axis.
xlab.size	Size of the lable of X-axis.
xtick.size	Size of tick lables of X-axis.
ylab	Label of Y-axis.
ylab.size	Size of the lable of Y-axis.
ytick.size	Size of tick lables of Y-axis.
legend.text	Title of legend.
legend.text.size	Size of the legend title.
legend.size	Size of legend.

Value

A plot of the density of the mixture

Author(s)

Didier Chauveau

References

• Chauveau, D., Saby, N., Orton, T. G., Lemercier B., Walter, C. and Arrouys, D. Large-scale simultaneous hypothesis testing in soil monitoring: A semi-parametric mixture approach, preprint (2013).

See Also

spEMsymlocN01, plot.spEMN01

Examples

## Probit transform of p-values
## from a Beta-Uniform mixture model
## comparion of parametric and semiparametric EM fit
## Note: in actual situations n=thousands
set.seed(50)
n=300 # nb of multiple tests
m=2 # 2 mixture components
a=c(1,0.1); b=c(1,1); lambda=c(0.6,0.4) # parameters
z=sample(1:m, n, rep=TRUE, prob = lambda)
p <- rbeta(n, shape1 = a[z], shape2 = b[z]) # p-values
o <- order(p)
cpd <- cbind(z,p)[o,] # sorted complete data, z=1 if H0, 2 if H1
p <- cpd[,2] # sorted p-values
y <- qnorm(p) # probit transform of the pvalues
# gaussian EM fit with component 1 constrained to N(0,1)
s1 <- normalmixEM(y, mu=c(0,-4),
                  mean.constr = c(0,NA), sd.constr = c(1,NA))
s2 <- spEMsymlocN01(y, mu0 = c(0,-3)) # spEM with N(0,1) fit
plotly_spEMN01(s2 , add.plot = FALSE)

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.