lfmm_sampler {lfmm}

LFMM generative data sampler

Description

Simulate data from the latent factor model.

Usage

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

Arguments

n

number of observations.

p

number of response variables.

K

number of latent variables (factors).

outlier.prop

proportion of outlier.

cs

correlation 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.

Details

lfmm_sampler() 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].

Value

A list with simulated data.

Author(s)

kevin caye, olivier francois

Examples

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)