observe({
output$correlPlot <- renderPlot({
validate(
need(
!is.null(input$dataFile),
'Please choose a datafile !'
),
need(
K() > 1,
'Requires more than one error set !'
)
)
data = input$corSample
order = 'original'
if(input$dendro)
order = 'hclust'
method = input$corMethod
# margin = input$corMargin
if (data == "data") {
if(!is.null(outSel()))
Data = Data[ !outSel(), ]
cmat = cor(Data, method = method)
} else if (data == "errors") {
if(!is.null(outSel()))
Errors = Errors[ !outSel(), ]
cmat = cor(Errors, method = method)
} else if (data == "mue") {
if(is.null(bsList()) |
is.null(bsList()$mue)) {
S = estBS1 (Errors,
props = "mue",
do.sip = FALSE,
silent = TRUE)
} else {
S = bsList()
}
cmat = S$mue$corr
} else {
if(is.null(bsList()) |
is.null(bsList()$q95hd)) {
S = estBS1 (Errors,
props = "q95hd",
do.sip = FALSE,
silent = TRUE)
} else {
S = bsList()
}
cmat = S$q95hd$corr
}
plotCorMat(
X = cmat,
order = order,
cex.lab = input$corCexLab,
gPars = gPars
)
},
# Best with square plot, except for small matrices
# width = max(300,round(plotHeight * K()^0.5 / nomSize)),
# height = max(200,round(plotHeight * K()^0.5 / nomSize))
width = 1.25*plotWidth, height = 1.25*plotHeight
)
})
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