x sum to
some multiple of the number of categories), or (c) is one free to use
arbitrarily-scaled penalty matrices?
I apologise if I am wasting your by making an obvious mistake. I am a
clinician, not a statistician. So, I do not understand the m
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and
or (c) is one free to use
arbitrarily-scaled penalty matrices for estimates of the accuracy of an ordinal
gold standard?
Thanks, in advance, for your help,
Jonathan Williams
turn(y)} ).
Thanks, in advance, for your help,
Jonathan Williams
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turn(y)} ).
Thanks, in advance, for your help,
Jonathan Williams
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and provide commented, minimal
Dear Helpers,
I was trying to find a repository of earlier Windows versions of R packages.
However, while I can find the Archives for Linux versions (in the "Old Sources"
section of each package's Downloads) , I cannot find one for Windows versions.
Does such a repository exist? If so, where ca
mparing m1 with
m2 or when comparing m3 with m4.
With many thanks, in anticipation of your help in explaining this,
Jonathan Williams
Here is a simple sample code to generate the discrepancies:-
set.seed(1)
A=factor(rep(c(1:5),600))
B=factor(rep(c(0:2),each=1000))
b=as.numeric(as.character(B)
Thanks Don MacQueen for this reply to my initial query - please SEE MY REPLIES
TO THESE IDEAS AND FURTHER INFORMATION BELOW
>From: Don MacQueen [m...@llnl.gov]
>Sent: 23 February 2010 21:25
>To: Jonathan Williams; r-help@r-project.org
>
>Subject: Re: [R] Problem with strptime gen
4-05" "1927-04-05" "1927-04-05" "1927-04-05"
"1939-04-08" "1939-04-08" "1939-04-08" "1939-04-08" "1938-10-24"
"1938-10-24" "1938-10-24"
[35] "1938-10-24
2])), but this prints "A
(beta[1-42])".
Anything else that I try returns an error (e.g. xlab=expression(A
beta[1]42]) returns 'Error: unexpected symbol').
So, I would be very grateful if someone can tell me how to write my label.
Thanks,
Jonathan Williams
Dear R Helpers,
Is there a way to generate multivariate correlated binomial data in R, similar
to how the rmvbin procedure in package bindata can generate multivariate
correlated binary data?
Thanks for your help,
Jonathan Williams
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R-help@r
s 73.965. There are similar changes for the values of BIC and logLik.
Am I doing something wrong, here? If not, which are the real AIC and logLik
values for the different models?
Thanks for your help,
Jonathan Williams
Output:-
> fit0=lmer(y~x1+x2+(1|id), data=datx); print(summary(fit0),co
family=quasi... But, I
may be wrong. If you get more information about this from the great man, then
would you please let me know?
Thanks,
Jonathan Williams
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ch.length")
#[1] 1
regexpr('.',f) #find the occurrences '.' in f - should be one at location 4
# but regexpr gives 1 at location 1
#[1] 1
#attr(,"match.length")
#[1] 1
Sorry if I am missing something obvious. I'd be very grateful i
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