Re: [R] generating lognormal variables with given correlation
This reference may be relevant for you: Connover, W.J., Iman, R.L. A
distribution-free approach to inducing rank correlation among input
variables. Technometric, 3, 311-334, 1982.
Also, you may want to look at a more modern approach implemented in the
copula package:
install.packages("copula")
library(help="copula")
I hope this helps,
Francisco,
Karl Ove Hufthammer wrote:
> Mollet, Fabian:
>
>> I would like these (lognormal distributed) parameters to be correlated,
>> that is, I would like to have pairwise samples of 2 parameters with a
>> given correlation coefficient.
>>
>> I have seen that a covariance matrix can be fixed when generating random
>> variables from a multivariate normal distribution e.g. by the function
>> mvrnorm.
>>
>> Is there a function to do the same for a multivariate lognormal
>> distribution?
>
> I don't know about any, but you should be aware that not all values of the
> correlation is possible with lognormal distributions. For example, if both
> variables have a standard lognormal distribution, they can't have correlation
> less than 1/e = -0.37. As the variance of the two distributions increase, the
> absolute value of the maximum and minimum correlation possible decrease (to
> zero).
>
> Using the normal product-moment correlation as a measure of dependence rarely
> makes much sense unless the association between the variables is linear.
>
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Re: [R] generating lognormal variables with given correlation
Mollet, Fabian: > I would like these (lognormal distributed) parameters to be correlated, > that is, I would like to have pairwise samples of 2 parameters with a > given correlation coefficient. > > I have seen that a covariance matrix can be fixed when generating random > variables from a multivariate normal distribution e.g. by the function > mvrnorm. > > Is there a function to do the same for a multivariate lognormal > distribution? I don't know about any, but you should be aware that not all values of the correlation is possible with lognormal distributions. For example, if both variables have a standard lognormal distribution, they can't have correlation less than 1/e = -0.37. As the variance of the two distributions increase, the absolute value of the maximum and minimum correlation possible decrease (to zero). Using the normal product-moment correlation as a measure of dependence rarely makes much sense unless the association between the variables is linear. -- Karl Ove Hufthammer __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] generating lognormal variables with given correlation
Dear R users I use simulated data to evaluate a model by sampling the parameters in my model from lognormal distributions. I would like these (lognormal distributed) parameters to be correlated, that is, I would like to have pairwise samples of 2 parameters with a given correlation coefficient. I have seen that a covariance matrix can be fixed when generating random variables from a multivariate normal distribution e.g. by the function mvrnorm. Is there a function to do the same for a multivariate lognormal distribution? Thank you! Fabian [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
