Default kernel density estimation is poorly suited for this sort of
situation.
A better alternative is logspline -- see the eponymous package -- you
can
specify lower limits for the distribution as an option.
url: www.econ.uiuc.edu/~roger Roger Koenker
email [EMAIL PROTECTED] Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Nov 25, 2008, at 10:43 AM, Jeroen Ooms wrote:
I am using density() to plot a density curves. However, one of my
variables
is truncated at zero, but has most of its density around zero. I
would like
to know how to plot this with the density function.
The problem is that if I do this the regular way density(), values
near zero
automatically get a very low value because there are no observed
values
below zero. Furthermore there is some density below zero, although
there are
no observed values below zero.
This illustrated the problem:
mydata <- rnorm(100000);
mydata <- mydata[mydata>0];
plot(density(mydata));
the 'real' density is exactly the right half of a normal
distribution, so
truncated at zero. However using the default options, the line seems
to
decrease with a nice curve at the left, with some density below
zero. This
is pretty confusing for the reader. I have tried to decrease the bw,
masks
(but does not fix) some of the problem, but than also the rest of
the curve
loses smoothness. I would like to make a plot of this data that
looks like
the right half of a normal distribution, while keeping the curve
relatively
smooth.
Is there any way to specify this truncation in the density function,
so that
it will only use the positive domain to calculate density?
--
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http://www.nabble.com/plotting-density-for-truncated-distribution-tp20684995p20684995.html
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