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Last data update: 2014.03.03

R: Differential Network for user specified data splits

diffnet_singlesplit

R Documentation

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.

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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(nethet)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/nethet/diffnet_singlesplit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: diffnet_singlesplit
> ### Title: Differential Network for user specified data splits
> ### Aliases: diffnet_singlesplit
> 
> ### ** 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
[1] 0.0004411369
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>