s_response_mars: Generate True Response vector for Non-Linear Simulation

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

Description

Given the covariates and environment variable this function generates the nonlinear response with specified signal to noise ratio.

Usage

s_response_mars(n, n0, p, genes, beta, binary_outcome = FALSE, E,
  signal_to_noise_ratio = 1, truemodule, nActive)

Arguments

n	total number of subjects
n0	total number of subjects with E=0
p	number of genes in design matrix
genes	nxp matrix of the genes or covariates
beta	true beta coefficient vector
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	signal to noise ratio, default is 1
truemodule	numeric vector of the true module membership used in the s_response_mars function. Modules 3 and 4 are active in the response. See s_response_mars function for details.
nActive	number of active genes in the response used in the s_response_mars

Value

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

Note

See Bhatnagar et al (2017+) for details on how the response is simulated.

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_mars(n = n, n0 = n0,
                          p = p, genes = X, binary_outcome = TRUE,
                          E = c(rep(0,n0), rep(1, n1)), signal_to_noise_ratio = 1,
                          truemodule = truemodule1, nActive = nActive,
                          beta = beta)
result[1:5,1:5]

eclust documentation built on May 1, 2019, 8:46 p.m.