ant figure out how to fix this. I am not an experienced programmer.
Any help or tips would be greatly appreciated.
Thank you,
Boel
--~*~**~***~*~***~**~*~--
Boel Brynedal, MSc, PhD student
Karolinska Institutet
Department of Clinical neuroscience
__
Hi All,
I'm working on analyzing a large data set, lets asume that
dim(Data)=c(1000,8700). I want to calculate the canberra distance
between the columns of this matrix, and using a toy example ('test' is
a matrix filled with random numbers 0-1):
> system.time(d<-as.matrix(dist(t(test), method = "
Stefan, that looks wonderful! I am most certainly going to try to
download and use your 'wordspace', even though I am unsure on how to
even download it at this point. Many thanks! But yes, I need the
Canberra distance unfortunately. But decreasing the time by more than
60% will save me days! If I c
Hi,
I want to simulate a data set with similar covariance structure as my
observed data, and have calculated a covariance matrix (dimensions
8368*8368). So far I've tried two approaches to simulating data:
rmvnorm from the mvtnorm package, and by using the Cholesky
decomposition
(http://www.cereb
o
> better to post on a stats site like stats.stackexchange.com rather
> than here.
>
> -- Bert
>
> On Sat, Aug 11, 2012 at 7:17 AM, Boel Brynedal wrote:
>> Hi,
>>
>> I want to simulate a data set with similar covariance structure as my
>> observed data, an
).
@Michael - I am simulating a sample size of 20351* 8368 so I do not
think that the sample size is the issue here.
2012/8/12 peter dalgaard :
>
> On Aug 11, 2012, at 16:17 , Boel Brynedal wrote:
>
>> cov8=cov(sample8,method='spearman')
>
> There's your problem
A clarification - yes, calculating the pearson covariance does give
the expected results. I dont fully understand why yet, but many thanks
for this help!
2012/8/12 Boel Brynedal :
> Thanks for these replies.
> @Peter - are these methods only suitable for pearson covariances? That
>
7 matches
Mail list logo
