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

plotly_spEMN01.Rd

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

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).

Author

Didier Chauveau

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))
#> number of iterations= 49 
s2 <- spEMsymlocN01(y, mu0 = c(0,-3)) # spEM with N(0,1) fit
plotly_spEMN01(s2 , add.plot = FALSE)