Differential Regression (single-split version).

Description

Differential Regression (single-split version).

Usage

1
2
3
4
5
diffregr_singlesplit(y1, y2, x1, x2, split1, split2,
  screen.meth = "screen_cvtrunc.lasso",
  compute.evals = "est2.my.ev3.diffregr", method.compquadform = "imhof",
  acc = 1e-04, epsabs = 1e-10, epsrel = 1e-10, show.warn = FALSE,
  n.perm = NULL, ...)

Arguments

y1

Response vector condition 1.

y2

Response vector condition 2.

x1

Predictor matrix condition 1.

x2

Predictor matrix condition 2.

split1

Samples condition 1 used in screening-step.

split2

Samples condition 2 used in screening-step.

screen.meth

Screening method (default='screen_cvtrunc.lasso').

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.diffregr'.

method.compquadform

Algorithm for computing distribution function of weighted-sum-of-chi2 (default='imhof').

acc

See ?davies (default=1e-4).

epsabs

See ?imhof (default=1e-10).

epsrel

See ?imhof (default=1e-10).

show.warn

Show warnings (default=FALSE)?

n.perm

Number of permutation for "split-perm" p-value (default=NULL).

...

Other arguments specific to screen.meth.

Details

Intercepts in regression models are assumed to be zero (mu1=mu2=0). You might need to center the input data prior to running Differential Regression.

Value

List consisting of

pval.onesided

"One-sided" p-value.

pval.twosided

"Two-sided" p-value. Ignore all "*.twosided results.

teststat

2 times Log-likelihood-ratio statistics

weights.nulldistr

Estimated weights of weighted-sum-of-chi2s.

active

List of active-sets obtained in screening step.

beta

Regression coefficients (MLE) obtaind in cleaning-step.

Author(s)

n.stadler

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
##set seed
set.seed(1)

##number of predictors / sample size
p <- 100
n <- 80

##predictor matrices
x1 <- matrix(rnorm(n*p),n,p)
x2 <- matrix(rnorm(n*p),n,p)

##active-sets and regression coefficients
act1 <- sample(1:p,5)
act2 <- c(act1[1:3],sample(setdiff(1:p,act1),2))
beta1 <- beta2 <- rep(0,p)
beta1[act1] <- 0.5
beta2[act2] <- 0.5

##response vectors 
y1 <- x1%*%as.matrix(beta1)+rnorm(n,sd=1)
y2 <- x2%*%as.matrix(beta2)+rnorm(n,sd=1)

##run diffregr
split1 <- sample(1:n,50)#samples for screening (condition 1)
split2 <- sample(1:n,50)#samples for screening (condition 2)
fit <- diffregr_singlesplit(y1,y2,x1,x2,split1,split2)
fit$pval.onesided#p-value

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.