plotly_FDR: Plot False Discovery Rate (FDR) estimates from output by EM-like strategies using `plotly`

Description

This is an updated version of plotFDR. For more technical details, please refer to plotFDR.

Usage

plotly_FDR(post1, post2=NULL, lg1="FDR 1", lg2=NULL, 
          compH0=1, alpha=0.1, complete.data =NULL, pctfdr=0.3,
          col = NULL, width = 3 ,
          title = NULL , title.size = 15 , title.x = 0.5 , title.y = 0.95,
          xlab = "Index" , xlab.size = 15 , xtick.size = 15,
          ylab = "Probability" , ylab.size = 15 , ytick.size = 15,
          legend.text = "" , legend.text.size = 15 , legend.size = 15)

Value

A plot of one or two FDR estimates, with the true FDR if available

Arguments

post1: The matrix of posterior probabilities from objects such as the output from spEMsymlocN01. The rows need to be sorted by increasing pvalues.
post2: A second object like post1 if comparison is desired, also sorted by increasing pvalues.
lg1: Text describing the FDR estimate in post1.
lg2: Text describing the FDR estimate in post2 if provided.
compH0: The component indicator associated to the null hypothesis H0, normally 1 since it is defined in this way in spEMsymlocN01, but in case of label switching in other algorithms it can be set to 2.
alpha: The target FDR level; the index at which the FDR estimate crosses the horizontal line for level alpha gives the maximum number of cases to reject.
complete.data: An array with $n$ lines and 2 columns, with the component indicator in column 1 and the p-values in column 2, sorted by p-values.
pctfdr: The level up to which the FDR is plotted, i.e. the scale of the vertical axis.
col: Color of traces.
width: Width of traces.
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.

Author

Didier Chauveau

References

Chauveau, D., Saby, N., Orton, T. G., Lemercier B., Walter, C. and Arrouys, D. Large-scale simultaneous hypothesis testing in monitoring carbon content from French soil database -- A semi-parametric mixture approach, Geoderma 219-220 (2014), 117-124.

Examples

Run this code

## 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_FDR(s1$post, s2$post, lg1 = "normalmixEM", lg2 = "spEMsymlocN01",
           complete.data = cpd) # with true FDR computed from z

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