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plotly_compCDF <- function(data,
weights,
x=seq(min(data, na.rm=TRUE), max(data, na.rm=TRUE), len=250),
comp=1:NCOL(weights),
makeplot=TRUE,
cex = 3,
width = 3,
legend.text = "Composition",
legend.text.size = 15,
legend.size = 15,
title = "Empirical CDF",
title.x = 0.5,
title.y = 0.95,
title.size = 15,
xlab = "Data",
xlab.size = 15,
xtick.size = 15,
ylab = "Probability",
ylab.size = 15,
ytick.size = 15,
col.comp = NULL){
if (NROW(weights) != NROW(data)) {
stop("data and weights arguments must have same number of rows")
}
if (is.null(col.comp)){
col.comp <- hue_pal()(length(comp))
}
if (length(col.comp) != length(comp)){
print(paste("Please specify",length(comp),"colors in 'col.comp'."))
}
# First, normalize the weights so the sum of each column is 1/NCOL(data)
weights <- t(t(weights) / (NCOL(data) * colSums(weights)))
# Next, give a binomial count for each row of the data and for each x
f <- function(row, cutpt) colSums(outer(row, cutpt, "<="), na.rm = TRUE)
bc <- apply(data, 1, f, x)
# bc is a length(x) by n matrix; each column should be multiplied by
# the appropriate weight(s) and then the rows summed to give the
# unnormalized cdf estimates. This is just a matrix product.
cdfs <- bc %*% weights[,comp,drop=FALSE]
if(makeplot) {
plot <- plot_ly()
for (i in 1:length(comp)) {
plot <- add_trace(plot,
plot,
x=x , y=cdfs[,comp[i]] , type = 'scatter' , mode = 'lines+markers',
marker = list(size = cex , color = col.comp[i]),
line = list(width = width , color = col.comp[i]),
name = comp[i] , showlegend = TRUE) %>%
plotly::layout(
legend = list(title=list(text=legend.text,
font=list(size=legend.text.size)),
font = list(size=legend.size)),
title = list(text = title,
x = title.x,
y = title.y,
font = list(size=title.size)),
xaxis = list(title = list(text = xlab,
font = list(size = xlab.size)),
tickfont = list(size = xtick.size)
),
yaxis = list(title = list(text = ylab,
font = list(size = ylab.size)),
tickfont = list(size = ytick.size),
range = c(0 , 1)
)
)
}
print(plot)
}
invisible(t(cdfs))
}
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