search for: _sum_

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2006 Nov 15
1
OPTIM--non finite finite different [13]
Dear All: I used optim() to minimise the loglikelihood function for fitting data to negative binomial distribution. But there initial value of log-likelihood and iteration 10 value are reasonable. for example: initial value 1451657.994524 iter 10 value 47297.534905 iter 20 value -623478636.8236478 Then the iter 20 vlaue suddelnly changes to a negative value and in the end the error mesage is
2008 Dec 26
1
histogramm$density
hello, i am using the hist function with classified values. The class breaks are >1, so histogram$density is != 1. How to plot the histogram with freq=FALSE and the real class density values. I used: > h2 = hist(value, breaks = breaks_vector) > h2$density = round(h2$counts/sum(h2$counts), 2) > h2$intensities = h2$density > plot(h2, freq=F) but this isn't the best way, i
2009 Oct 28
1
cross-over designs
Hi, I have a dataset from a client where the data is from a cross-over design. Basically, each subject in a survey was asked to rate two products, A and B. The subject sampled A first and then after an appropriate wash-out period he/she sampled B. The next subject did the same, but in a different order. How can I do an ANOVA analysis on a cross-over design with only two treatments. This
2011 Jul 15
2
scaling advice
Hi, I have a consultants nightmare -- I was given a project that another consultant did and I was told to do the same calculations, but there's no documentation on what he did. Basically, I have yes/no answers to survey questions about the effectiveness of product attributes by brands. There are 44 attributes and 13 brands. The other guy scaled the proportion of respondents who said
2004 Nov 13
3
density estimation: compute sum(value * probability) for given distribution
Dear R users, This is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F)