texmexseq (version 0.3)

ppplot: PP plot for Poisson lognormal distribution

Description

Generate a plot showing the quality of the fit of the Poisson lognormal to the data.

Usage

ppplot(n, n.points=10)

Arguments

n

vector of observed counts

n.points

number of points on the graph to highlight

Value

a ggplot object whose default data object has columns empirical and theoretical

Details

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).

The optional 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.

See Also

texmex.fit

Examples

# 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')