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[EMAIL PROTECTED] wrote:
| I am trying to fit a set of data to a Weibull distribution. Because
the implementation requires that I put the data in the range of 0 x
1 I have a normailze function:
|
| normalize - function(x) {
| y - (x-min(x)) /
I am trying to fit a set of data to a Weibull distribution. Because the
implementation requires that I put the data in the range of 0 x 1 I have a
normailze function:
normalize - function(x) {
y - (x-min(x)) / (max(x) - min(x))
y = y + 0.5 * (y == 0) -0.5 * (y == 1)
return (y)
This may be a begining question. If so, please bear with me.
If I have some data that based on the historgram and other plots it looks
like a beta distribution. Is there a function or functions within R to help me
determine the model parameters for such a distirbution? Similarily for other
rkevinburton at charter.net writes:
If I have some data that based on the historgram and other plots it looks
like a beta distribution. Is there
a function or functions within R to help me determine the model parameters for
such a distirbution?
library(MASS)
?fitdistr
~ The beta distribution only applies to data that are bounded
between 0 and 1 (in some cases strictly bounded, i.e. 0x1 not
just 0=x=1).
~ As a start, try something like
d2 = (Diff-min(Diff)+0.001)/(max(Diff)-min(Diff)+0.002)
fitdistr(d2,densfun=beta,start=list(shape1=3,shape2=2))
~
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