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

plotly_spEMN01 {mixtools}

R Documentation

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).

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)

[Package mixtools version 2.0.0 Index]

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