diffnet_singlesplit: Differential Network for user specified data splits

In nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity

Description

Differential Network for user specified data splits

Usage

diffnet_singlesplit(x1, x2, split1, split2,
  screen.meth = "screen_bic.glasso", compute.evals = "est2.my.ev3",
  algorithm.mleggm = "glasso_rho0", include.mean = FALSE,
  method.compquadform = "imhof", acc = 1e-04, epsabs = 1e-10,
  epsrel = 1e-10, show.warn = FALSE, save.mle = FALSE, ...)

Arguments

x1	Data-matrix sample 1. You might need to center and scale your data-matrix.
x2	Data-matrix sample 2. You might need to center and scale your data-matrix.
split1	Samples (condition 1) used in screening step.
split2	Samples (condition 2) used in screening step.
screen.meth	Screening procedure. Options: 'screen_bic.glasso' (default), 'screen_cv.glasso', 'screen_shrink' (not recommended).
compute.evals	Method to estimate the weights in the weighted-sum-of-chi2s distribution. The default and (currently) the only available option is the method 'est2.my.ev3'.
algorithm.mleggm	Algorithm to compute MLE of GGM. The algorithm 'glasso_rho' is the default and (currently) the only available option.
include.mean	Should sample specific means be included in hypothesis? Use include.mean=FALSE (default and recommended) which assumes mu1=mu2=0 and tests the hypothesis H0: Omega_1=Omega_2.
method.compquadform	Method to compute distribution function of weighted-sum-of-chi2s (default='imhof').
acc	See ?davies (default 1e-04).
epsabs	See ?imhof (default 1e-10).
epsrel	See ?imhof (default 1e-10).
show.warn	Should warnings be showed (default=FALSE)?
save.mle	Should MLEs be in the output list (default=FALSE)?
...	Additional arguments for screen.meth.

Details

Remark:

* If include.mean=FALSE, then x1 and x2 have mean zero and DiffNet tests the hypothesis H0: Omega_1=Omega_2. You might need to center x1 and x2. * If include.mean=TRUE, then DiffNet tests the hypothesis H0: mu_1=mu_2 & Omega_1=Omega_2 * However, we recommend to set include.mean=FALSE and to test equality of the means separately. * You might also want to scale x1 and x2, if you are only interested in differences due to (partial) correlations.

Value

list consisting of

pval.onesided	p-value
pval.twosided	ignore this output
teststat	log-likelihood-ratio test statistic
weights.nulldistr	estimated weights
active	active-sets obtained in screening-step
sig	constrained mle (covariance) obtained in cleaning-step
wi	constrained mle (inverse covariance) obtained in cleaning-step
mu	mle (mean) obtained in cleaning-step

Author(s)

n.stadler

Examples

##set seed
set.seed(1)

##sample size and number of nodes
n <- 40
p <- 10

##specifiy sparse inverse covariance matrices
gen.net <- generate_2networks(p,graph='random',n.nz=rep(p,2),
                              n.nz.common=ceiling(p*0.8))
invcov1 <- gen.net[[1]]
invcov2 <- gen.net[[2]]
plot_2networks(invcov1,invcov2,label.pos=0,label.cex=0.7)

##get corresponding correlation matrices
cor1 <- cov2cor(solve(invcov1))
cor2 <- cov2cor(solve(invcov2))

##generate data under alternative hypothesis
library('mvtnorm')
x1 <- rmvnorm(n,mean = rep(0,p), sigma = cor1)
x2 <- rmvnorm(n,mean = rep(0,p), sigma = cor2)

##run diffnet
split1 <- sample(1:n,20)#samples for screening (condition 1)
split2 <- sample(1:n,20)#samples for screening (condition 2)
dn <- diffnet_singlesplit(x1,x2,split1,split2)
dn$pval.onesided#p-value

nethet documentation built on Nov. 8, 2020, 6:54 p.m.