Displaying 4 results from an estimated 4 matches for "newdist".
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2009 Feb 08
5
glmmBUGS: logistic regression on proportional data
...mer to this package, and
wondered if anyone could help me specify the model correctly.
I am trying to specify the response variable, /yseed/, as # of successes
out of total observations... but I suspect that given the error below,
that is not correct. Also, Newsect should be a factor, whereas Newdist
is continuous.
Thanks,
John
Newdat<-data.frame(Newtree=rep(1:3, each=20), Newsect=rep(c("a","b"),
each=10), Newdist=rep(1:5, 2),
y=rpois(60,2), tot=rep(c(14,12,10,8,6), 12))
yseed<-cbind(Newdat$y, Newdat$tot)
mod<-glmmBUGS(yseed~Newsect + Newdi...
2006 Mar 21
2
Sorting by computed temporary field
...think of:
- Make a new find_and_sort method that adds a new @distance member
element and sorts the new collection. Would something like this work?
def find_and_sort(event_id)
@rides = Ride.find(:all, :conditions => {"event_id = ?", event_id})"
for ride in @rides
ride.newdistance = distance_between_zips(session[:user].zip_code,
Event.find(event_id).zip_code);
end
uhm.... sort @rides by newdistance
end
What if I want to also remove items from @rides before they are
displayed if that newdistance is too large or some other condition?
uhm... I''m stu...
2005 May 18
1
'fitdistr' and two views of the same data?
...blogs/mjadud/archives/2005/05/a_question_abou.html
The short version of the question is this:
When I ask 'fitdistr' to try and fit my distribution as a "weibull"
distribution, it comes up with some rather wacky parameters.
If I take the same distribution, and do something like
newdist <- mapply(function(x) ((x %/% 20) + 1), origdist)
which effectively forces the data into a histogram, 'fitdist' on
'newdist' gives me an entirely different set of parameters.
Distressingly, the parameters it gives me are, upon inspection, good;
that is, the parameters reported f...
2012 Nov 25
5
bbmle "Warning: optimization did not converge"
I am using the Ben bolker's R package "bbmle" to estimate the parameters of a
binomial mixture distribution via Maximum Likelihood Method. For some data
sets, I got the following warning messages:
*Warning: optimization did not converge (code 1: )
There were 50 or more warnings (use warnings() to see the first 50)*
Also, warnings() results the following:
*In 0:(n - x) : numerical