Hi Lavan,
a continuous density is not restricted to be within [0, 1]. Its only
bound to have an integral of 1.
For example
dnorm(0,sd=.1)
is a very common density and gives 3.989423. A density function is not a
probability function!
If you think your data x is discrete than you can assign the correct
probability mass for each data point by
prop.table(table(x))
hth.
Lavan schrieb:
Hi,
I have a vector or random variables and I'm estimating the density using
"bkde" function in the KernSmooth package. The out put contains two vectors
(x and y), and the R documentation calls y as the density estimates, but my
y-values are not exact density etstimates (since these are numbers larger
than 1)! what is y here? Is it possible to get the true estimated density at
each value of x?
Thanks
--
Eik Vettorazzi
Institut für Medizinische Biometrie und Epidemiologie
Universitätsklinikum Hamburg-Eppendorf
Martinistr. 52
20246 Hamburg
T ++49/40/42803-8243
F ++49/40/42803-7790
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