Description Usage Arguments Value Details Author(s) Examples
A simulation for the multiclass hump experiment, in which each class has a unique hump which distinguishes its mean.
1 | lol.sims.kident(n, d, rotate = FALSE, priors = NULL, b = 4, K = 4, maxvar = 25)
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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 |
b |
scalar for mu scaling. Default to |
K |
the number of classes. Should be an even number. Defaults to |
maxvar |
the maximum covariance between the two classes. Defaults to |
A list of class simulation
with the following:
X |
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Y |
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mus |
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Sigmas |
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priors |
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simtype |
The name of the simulation. |
params |
Any extraneous parameters the simulation was created with. |
robust |
If robust is not false, a list containing |
For more details see the help vignette:
vignette("sims", package = "lolR")
Eric Bridgeford
1 2 3 | library(lolR)
data <- lol.sims.rtrunk(n=200, d=30) # 200 examples of 30 dimensions
X <- data$X; Y <- data$Y
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