set_blocks: Gather settings for parallel inference on partitioned...

In hruffieux/locus: Large-Scale Variational Inference for Combined Selection of Covariate and Response Variables in Regression Models

Gather settings for parallel inference on partitioned predictor space.

Description

Parallel applications of the method on blocks of candidate predictors for large datasets allows faster and less RAM-greedy executions.

Usage

set_blocks(p, pos_bl, n_cpus, verbose = TRUE)

Arguments

p	Number of candidate predictors.
pos_bl	Vector gathering the predictor block positions (first index of each block).
n_cpus	Number of CPUs to be used. If large, one should ensure that enough RAM will be available for parallel execution. Set to 1 for serial execution.
verbose	If TRUE, messages are displayed when calling set_blocks.

Value

An object of class "blocks" preparing the settings for parallel inference in a form that can be passed to the locus function.

See Also

locus

Examples

seed <- 123; set.seed(seed)

###################
## Simulate data ##
###################

## Example using small problem sizes:
##
n <- 200; p <- 1200; p0 <- 200; d <- 50; d0 <- 40

## Candidate predictors (subject to selection)
##
# Here we simulate common genetic variants (but any type of candidate
# predictors can be supplied).
# 0 = homozygous, major allele, 1 = heterozygous, 2 = homozygous, minor allele
#
X_act <- matrix(rbinom(n * p0, size = 2, p = 0.25), nrow = n)
X_inact <- matrix(rbinom(n * (p - p0), size = 2, p = 0.25), nrow = n)

shuff_x_ind <- sample(p)
X <- cbind(X_act, X_inact)[, shuff_x_ind]

bool_x_act <- shuff_x_ind <= p0

pat_act <- beta <- matrix(0, nrow = p0, ncol = d0)
pat_act[sample(p0*d0, floor(p0*d0/5))] <- 1
beta[as.logical(pat_act)] <-  rnorm(sum(pat_act))

## Gaussian responses
##
Y_act <- matrix(rnorm(n * d0, mean = X_act %*% beta, sd = 0.5), nrow = n)
Y_inact <- matrix(rnorm(n * (d - d0), sd = 0.5), nrow = n)
shuff_y_ind <- sample(d)
Y <- cbind(Y_act, Y_inact)[, shuff_y_ind]

########################
## Infer associations ##
########################

n_bl <- 6
pos_bl <- seq(1, p, by = ceiling(p/n_bl))
list_blocks <- set_blocks(p, pos_bl, n_cpus = 1)

vb <- locus(Y = Y, X = X, p0_av = p0, link = "identity",
            list_blocks = list_blocks, user_seed = seed)

hruffieux/locus documentation built on Jan. 10, 2024, 10:07 p.m.