Description Usage Arguments Value Details Author(s) Examples
A function for simulating data in which a difference in the means is present only in a subset of dimensions, and equal covariance.
1 2  lol.sims.mean_diff(n, d, rotate = FALSE, priors = NULL, K = 2, md = 1,
subset = c(1), offdiag = 0, s = 1)

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 
priors 
the priors for each class. If 
K 
the number of classes. Defaults to 
md 
the magnitude of the difference in the means in the specified subset of dimensions. Ddefaults to 
subset 
the dimensions to have a difference in the means. Defaults to only the first dimension. 
offdiag 
the offdiagonal elements of the covariance matrix. Should be < 1. 
s 
the scaling parameter of the covariance matrix. S_ij = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to 
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.mean_diff(n=200, d=30) # 200 examples of 30 dimensions
X < data$X; Y < data$Y

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