diffregr_singlesplit {nethet} | R Documentation |
Differential Regression (single-split version).
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, ...)
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
##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