[R] plot positive predictive values

[ONKELINX, Thierry]

You could use a glm with the binomial family to model that.

A solution with ggplot2

library(ggplot2)
ggplot(dataset, aes(x = x, y = y, weights = n)) +
	geom_smooth(method = "glm", family = binomial)
	geom_point()


------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens r2L
Verzonden: vrijdag 4 september 2009 13:04
Aan: r-help at r-project.org
Onderwerp: [R] plot positive predictive values

Hi,

I'm trying to fit a smooth line in a plot(y ~ x) graph.

x is continuous variable
y is a proportion of success in sub-samples, 0 <= y <= 1, from a Monte
Carlo simulation.

For each x there may be several y-values from different runs. Each run
produces several sub-samples, where "0" mean no success in any sub-
sample, "0.5" means success in half of the sub-samples, and "1" means
success in all sub-samples, and so on.

As x is increased, the y-value approaches 1, and may reach it; it can,
of course never bypass it.

>From my understanding of the data at hand, each point along the x-axis
has its own beta-distribution of the y-values, then as 0 <= y <= 1,
which shift gradually through distributions similar to

curve(dbeta(x,2, 2), add=F, col="red", xlim=c(0,1), ylim=c(0,4))
curve(dbeta(x,4, 2), add=T, col="red", xlim=c(0,1), ylim=c(0,4))
curve(dbeta(x,4, 1), add=T, col="red", xlim=c(0,1), ylim=c(0,4))
curve(dbeta(x,4, .1), add=T, col="red", xlim=c(0,1), ylim=c(0,4))

as x increases.

If I plot my data using boxplot it shows also very nicely how the the
data approaches 1 and variation decreases. However, my x-axis data are
continuous.

Is there a way to produce a regression line which would smoothly follow
that trend?

It may well be easier than I believe, but my head is at a full-stop...

Thanks for any help!

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