Data simulation

[ibaldauf]

Dear Phil,

I have a question about your example on github (https://github.com/philchalmers/mirt/blob/master/R/simdata.R).

Why do you use a lognormal distribution with mean 0.2 and standard deviation 0.3 for simulating the a Matrix?

a <- matrix(rlnorm(20,.2,.3))

# for the graded model, ensure that there is enough space between the intercepts,

# otherwise closer categories will not be selected often (minimum distance of 0.3 here)

diffs <- t(apply(matrix(runif(20*4, .3, 1), 20), 1, cumsum))

diffs <- -(diffs - rowMeans(diffs))

d <- diffs + rnorm(20)

dat <- simdata(a, d, 500, itemtype = 'graded')

Thanks for your help

Isabell

[Phil Chalmers]

Mainly just convention. Slopes are often believed to follow log-normal distributions for unidimensional models, which matches the popular log-normal prior in software like BILOG-MG. But of course you can choose any distribution you'd like (in published simulation work people also tend to choose uniform distributions). Really depends on what you are trying to emulate. Cheers. 

Phil

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