[R] R question: generating data using MASS

[Michael Parent]

Thanks!

"This problem isn't uniquely defined.  Are you willing to generate more samples than you need and then throw away extreme values?  Or do you want to 'censor' extreme values (i.e. set values <= 1 to 1 and values >=7 to 7)?"

I'd like the retain a normal distribution so I wouldn't want to delete the other values or truncate them. Can I use the cut command on the data that gets generated and retain a normal(ish, at least) distribution?

Oh, thanks for the help on the matrix, that is easier, and also the random missingness, I will try those!

Thanks,
Mike


On Aug 29, 2011, at 2:29 AM, Ben Bolker wrote:

> uf_mike <michael.parent <at> ufl.edu> writes:
> 
>> 
>> Hi, all! I'm new to R but need to use it to solve a little problem I'm having
>> with a paper I'm writing. The question has a few components and I'd
>> appreciate guidance on any of them.
>> 
>> 1. The most essential thing is that I need to generate some multivariate
>> normal data on a restricted integer range (1 to 7). I know I can use MASS
>> mvrnorm command to do this but have a couple questions about that:
>> -I can make the simulated data but I don't know how to issue a command that
>> restricts the generated data to be between a specific range (1 to 7), and
>> integer-only.
> 
>   This problem isn't uniquely defined.  Are you willing to generate
> more samples than you need and then throw away extreme values?  Or do
> you want to 'censor' extreme values (i.e. set values <= 1 to 1 and
> values >=7 to 7)?
> 
>  x <- MASS::mvrnorm(10000,...)
>  x2 <- x[x>=1 & x<=7]
>  x3 <- x2[1:1000]  ## or however many you need
>  x4 <- round(x3)
> 
> 
>> -Is there a way to specify a single desired correlation between all the
>> variables (i.e., I want, say, five variables to all be correlated about .30
>> with each other), rather than input the entire covariance matrix as sigma?
> 
>   What's wrong with
> 
> m <- matrix(0.3,nrow=5,ncol=5)
> diag(m) <- 1
> m <- m*variance
> 
>  ?
>> 
>> 2. I need to introduce missing data (NA) AFTER generating the data set, and
>> I need it to be random and at a specific prevalence (say, 5%). Is there a
>> simple way to take the initial data set and randomly replace 5% of values
>> with NA missing values?
> 
>  x4[sample(seq(x4),size=0.05*length(x4),replace=FALSE)] <- NA
>> 
> 
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