nethet (version 1.4.2)

diffnet_singlesplit: Differential Network for user specified data splits

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), 'screen_mb'.

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.

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

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.

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