Description Usage Arguments Details Value Author(s) References Examples
This function computes regularized least squares estimates for latent factor mixed models using a lasso penalty.
1 2 3 4 5 6 7 8 9 10 11 | lfmm_lasso(
Y,
X,
K,
nozero.prop = 0.01,
mu.num = 100,
mu.min.ratio = 0.01,
mu = NULL,
it.max = 100,
relative.err.epsilon = 1e-06
)
|
Y |
a response variable matrix with n rows and p columns. Each column is a response variable (e.g., SNP genotype, gene expression level, beta-normalized methylation profile, etc). Response variables must be encoded as numeric. |
X |
an explanatory variable matrix with n rows and d columns. Each column corresponds to a distinct explanatory variable (eg. phenotype, exposure, outcome). Explanatory variables must be encoded as numeric. |
K |
an integer for the number of latent factors in the regression model. |
nozero.prop |
a numeric value for the expected proportion of non-zero effect sizes. |
mu.num |
a numeric value for the number of 'mu' values (advance parameter). |
mu.min.ratio |
(advance parameter) A fraction of |
mu |
(advance parameter) Smallest value of |
it.max |
an integer value for the number of iterations of the algorithm. |
relative.err.epsilon |
a numeric value for a relative convergence error. Determine whether the algorithm converges or not. |
The algorithm minimizes the following penalized least-squares criterion
The response variable matrix Y and the explanatory variable are centered.
an object of class lfmm
with the following attributes:
U the latent variable score matrix with dimensions n x K,
V the latent variable axes matrix with dimensions p x K,
B the effect size matrix with dimensions p x d.
Kevin Caye, Basile Jumentier, Olivier Francois
B. Jumentier, Caye, K., J. Lepeule, and O. François, 2019 Sparse latent factor regression models for genome-wide and epigenome-wide association studies (in prep)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | library(lfmm)
## An EWAS example with Y = methylation data
## and X = exposure
## Simulate the data
dat <- lfmm_sampler(n = 100,
p = 1000,
K = 3,
outlier.prop = 0.02,
cs = 0.1,
sigma = 0.2,
B.sd = 5,
B.mean = 0,
U.sd = 1.0,
V.sd = 1.0)
Y <- scale(dat$Y)
X <- scale(dat$X)
## Fit an LFMM with 2 latent factors
mod.lfmm <- lfmm_lasso(Y = Y,
X = X,
K = 3,
nozero.prop = 0.02)
## Manhattan plot of sparse effect sizes
effect <- mod.lfmm$B
causal <- dat$outlier
plot(effect,
pch = 19,
cex = .3,
xlab = "Probe",
col = "grey")
points(causal,
effect[causal],
col = "blue")
|
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