Description Usage Arguments Details Value Author(s) Examples
Differential Regression (single-split version).
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, ...)
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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. |
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.
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. |
n.stadler
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
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