s_response: Generate True Response vector for Linear Simulation

In sahirbhatnagar/eclust: Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data

Description

Given the true beta vector, covariates and environment variable this function generates the linear response with specified signal to noise ratio.

Usage

s_response(n, n0, p, genes, binary_outcome = FALSE, E,
  signal_to_noise_ratio = 1, include_interaction = FALSE, beta = NULL)

Arguments

n	Total number of subjects
n0	Total number of unexposed subjects
p	Total number of genes (or covariates)
genes	nxp matrix of the genes or covariates
binary_outcome	Logical. Should a binary outcome be generated. Default is FALSE. See details on how a binary outcome is generated
E	binary 0,1, vector of the exposure/environment variable
signal_to_noise_ratio	a numeric variable for the signal to noise ratio
include_interaction	Logical. Should the response include the interaction between E and the genes (for the non-zero beta coefficient vector)
beta	true beta coefficient vector. Assumes this vector is in the same order as the genes.

Value

a data.frame/data.table containing the response and the design matrix. Also an object of class expression

Examples

library(magrittr)

# simulation parameters
rho = 0.90; p = 500 ;SNR = 1 ; n = 200; n0 = n1 = 100 ; nActive = p*0.10 ; cluster_distance = "tom";
Ecluster_distance = "difftom"; rhoOther = 0.6; betaMean = 2;
alphaMean = 1; betaE = 3; distanceMethod = "euclidean"; clustMethod = "hclust";
cutMethod = "dynamic"; agglomerationMethod = "average"

#in this simulation its blocks 3 and 4 that are important
#leaveOut:  optional specification of modules that should be left out
#of the simulation, that is their genes will be simulated as unrelated
#("grey"). This can be useful when simulating several sets, in some which a module
#is present while in others it is absent.
d0 <- s_modules(n = n0, p = p, rho = 0, exposed = FALSE,
                modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
                minCor = 0.01,
                maxCor = 1,
                corPower = 1,
                propNegativeCor = 0.3,
                backgroundNoise = 0.5,
                signed = FALSE,
                leaveOut = 1:4)

d1 <- s_modules(n = n1, p = p, rho = rho, exposed = TRUE,
                modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
                minCor = 0.4,
                maxCor = 1,
                corPower = 0.3,
                propNegativeCor = 0.3,
                backgroundNoise = 0.5,
                signed = FALSE)

truemodule1 <- d1$setLabels

X <- rbind(d0$datExpr, d1$datExpr) %>%
  magrittr::set_colnames(paste0("Gene", 1:p)) %>%
  magrittr::set_rownames(paste0("Subject",1:n))

betaMainEffect <- vector("double", length = p)

# the first nActive/2 in the 3rd block are active
betaMainEffect[which(truemodule1 %in% 3)[1:(nActive/2)]] <- runif(
  nActive/2, betaMean - 0.1, betaMean + 0.1)

# the first nActive/2 in the 4th block are active
betaMainEffect[which(truemodule1 %in% 4)[1:(nActive/2)]] <- runif(
  nActive/2, betaMean+2 - 0.1, betaMean+2 + 0.1)
beta <- c(betaMainEffect, betaE)

result <- s_response(n = n, n0 = n0,
                     p = p, genes = X, binary_outcome = FALSE,
                     E = c(rep(0,n0), rep(1, n1)), signal_to_noise_ratio = 1,
                     include_interaction = FALSE,
                     beta = beta)
result[1:5,1:5]

sahirbhatnagar/eclust documentation built on May 29, 2019, 12:58 p.m.