Generate a plot showing the quality of the fit of the Poisson lognormal to the data.
vector of observed counts
number of points on the graph to highlight
The function fits the Poisson lognormal to the raw read counts
n and uses that
to generate theoretical percents for a PP (“percent-percent” or “probability-probability”)
plot. A perfect fit falls on the diagonal (marked with a dotted line).
n.points plots some extra points on the graph. The first point (the lower-left-most)
represents the fraction of all OTUs that have 1 count in the empirical (x-axis) and theoretical
(y-axis) distributions. The second point represents OTUs with 1 or 2 counts. The third point
represents OTUs with up to 3 counts, and so on.
ggplot object whose default
data object has columns
Scott Olesen email@example.com
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# make up some data n <- rpoilog(1000, 1.0, 1.0) # plot it p <- ppplot(n) p # compare to the lognormal's fit # first, make the empirical cumulative distribution function from the data x <- tabulate(n + 1) empirical <- cumsum(x / sum(x)) # then, get the theoretical percents theoretical <- plnorm(0:max(n), meanlog=mean(log(n)), sdlog=sd(log(n))) lognormal.fit <- data.frame(empirical=empirical, theoretical=theoretical) # add that data in a new layer p + geom_line(data=lognormal.fit, color='red')
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