I assume that you want to do the fitdistr on one of the columns of the
dataframe that you have read in. What does 'str(ONES3)' show? If the
data is in the first column, try:
fitdistr(ONES3[[1]],chi-squared)
On 9/9/07, Terence Broderick [EMAIL PROTECTED] wrote:
I am trying to fit the
On Sat, 23 Sep 2006, Luca Telloli wrote:
Hello R-Users,
I'm new to R so I apologize in advance for any big mistake I might
be doing. I'm trying to fit a set of samples with some probabilistic
curve, and I have an important question to ask; in particular I have
some data, from which I
At 09:35 AM 2/10/2006, Gregor Gorjanc wrote:
Hello!
I would like to get MLE for parameter lambda of Poisson distribution. I
can use fitdistr() for this. After looking a bit into the code of this
function I can see that value for lambda and its standard error is
estimated via
estimate - mean(x)
Bernardo Rangel tura wrote:
At 09:35 AM 2/10/2006, Gregor Gorjanc wrote:
Hello!
I would like to get MLE for parameter lambda of Poisson distribution. I
can use fitdistr() for this. After looking a bit into the code of this
function I can see that value for lambda and its standard error is
Gregor Gorjanc [EMAIL PROTECTED] writes:
Hello!
I would like to get MLE for parameter lambda of Poisson distribution. I
can use fitdistr() for this. After looking a bit into the code of this
function I can see that value for lambda and its standard error is
estimated via
estimate -
Peter Dalgaard wrote:
Gregor Gorjanc [EMAIL PROTECTED] writes:
Hello!
I would like to get MLE for parameter lambda of Poisson distribution. I
can use fitdistr() for this. After looking a bit into the code of this
function I can see that value for lambda and its standard error is
estimated
Hi,
values in parentesis below the estimate of a parameter
is the standard deviation of parameter, that's a
measure of variability.
Regards.
Vito
set.seed(123)
x - rgamma(100, shape = 5, rate = 0.1)
fitdistr(x, gamma)
shape rate
6.45947303 0.13593172
{BCC'ed to VR's maintainer}
Carsten == Carsten Steinhoff [EMAIL PROTECTED]
on Tue, 5 Apr 2005 17:31:04 +0200 writes:
Carsten Hi all, I'm using the function fitdistr (library
Carsten MASS) to fit a distribution to given data. What I
Carsten have to do further, is getting
Thanks, your advice worked. I don't have much experience with maths, and
therefore tried to stay away from dealing with optimization, but going
down to this level opens a lot of possibilities. For the record, the
code I used, as you suggested:
###
shape - mean(data)^2/var(data)
scale
I'm sorry, but I don't have time to read all your code. However,
I saw that you tested for x alpha in your Pareto distribution
example. Have you considered reparameterizing to estimate log.del =
log(alpha-min(x))? Pass log.del as part of the vector of parameters to
estimate, then
Thanks for the help, the wrapper function was very useful. I managed to
solve the problem using Spencer Graves' suggestion. I am analyzing the
interarrival times between HTTP packets on a campus network. The dataset
actually has more than 14 Million entries! It represents the traffic
generated by
Are you interested in turning that into a monitor, processing each
day's data sequentially or even each entry as it arrived? If yes, you
may wish to evaluate the Foundations of Monitoring documents
downloadable from www.prodsyse.com. If you have any questions about
that, I might be able
In my experience, the most likely cause of this problem is that
optim may try to test nonpositive values for shape or scale. I avoid
this situation by programming the log(likelihood) in terms of log(shape)
and log(scale) as follows:
gammaLoglik -
+ function(x, logShape, logScale,
Spencer Graves's suggestion of using shape and scale parameters on a log
scale is a good one.
To do specifically what you want (check values for which the objective
function is called and see what happens) you can do the following
(untested!), which makes a local copy of dgamma that you
PS. 11 MILLION entries??
On Tue, 30 Sep 2003, Ben Bolker wrote:
Spencer Graves's suggestion of using shape and scale parameters on a log
scale is a good one.
To do specifically what you want (check values for which the objective
function is called and see what happens) you can
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