Description Usage Arguments Value Details Author(s) Examples
A function for simulating from 2 classes with differing means each with 2 subclusters, where one subcluster has a narrow tail and the other subcluster has a fat tail.
1 2  lol.sims.fat_tails(n, d, rotate = FALSE, f = 15, s0 = 10, rho = 0.2,
t = 0.8, priors = NULL)

n 
the number of samples of the simulated data. 
d 
the dimensionality of the simulated data. 
rotate 
whether to apply a random rotation to the mean and covariance. With random rotataion matrix 
f 
the fatness scaling of the tail. S2 = f*S1, where S1_ij = rho if i != j, and 1 if i == j. Defaults to 
s0 
the number of dimensions with a difference in the means. s0 should be < d. Defaults to 
rho 
the scaling of the offdiagonal covariance terms, should be < 1. Defaults to 
t 
the fraction of each class from the narrowertailed distribution. Defaults to 
priors 
the priors for each class. If 
A list of class simulation
with the following:
X 

Y 

mus 

Sigmas 

priors 

simtype 
The name of the simulation. 
params 
Any extraneous parameters the simulation was created with. 
For more details see the help vignette:
vignette("sims", package = "lolR")
Eric Bridgeford
1 2 3  library(lolR)
data < lol.sims.fat_tails(n=200, d=30) # 200 examples of 30 dimensions
X < data$X; Y < data$Y

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