R: Plot False Discovery Rate (FDR) estimates from output by...

plotly_FDR {mixtools}

R Documentation

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)

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.

Value

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

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 monitoring carbon content from French soil database – A semi-parametric mixture approach, Geoderma 219-220 (2014), 117-124.

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

[Package mixtools version 2.0.0 Index]

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