lfmm_sampler: LFMM generative data sampler
In cayek/MatrixFactorizationR: Latent Factor Mixed Models

Description Usage Arguments Details Value Author(s) Examples

Simulate data from the latent factor model.

1 2	lfmm_sampler(n, p, K, outlier.prop, cs, sigma = 0.2, B.sd = 1, B.mean = 0, U.sd = 1, V.sd = 1)

`n`	number of observations.
`p`	number of response variables.
`K`	number of latent variables (factors).
`outlier.prop`	proportion of outlier.
`cs`	correlation with between X and U.
`sigma`	standard deviation of residual errors.
`B.sd`	standard deviation for the effect size (B).
`B.mean`	mean of B.
`U.sd`	standard deviations for K factors.
`V.sd`	standard deviations for loadings.

lfmm_sample() sample a response matrix Y and a primary variable X such that

Y = U t(V) + X t(B) + Epsilon.

U,V, B and Epsilon are simulated according to normal multivariate distributions. Moreover U and X are such that cor(U[,i], X) = cs[i].

A list with simulated data.

kevin caye, olivier francois

dat <- lfmm_sampler(n = 100, 
                    p = 1000, 
                    K = 3,
                    outlier.prop = 0.1,
                    cs = c(0.8),
                    sigma = 0.2,
                    B.sd = 1.0, 
                    B.mean = 0.0,
                    U.sd = 1.0, 
                    V.sd = 1.0)